Author: Bhavan Kumar Natarajan

Securing AI Agent Communication: Decentralized Identity & Protocol

by Raghu Bala

1. What are the biggest challenges in enabling AI agents to communicate securely across different enterprises? 

There are two important aspects to this communication:

·         Identity
·         Protocol

Let us do a deeper dive on each of these aspects.

IDENTITY

Today AI Agents are being built for use within enterprises and being built in such a manner that they are simply extensions of robotic process automation scripts.  This is a major flaw.  AI Agents have to have Permanent IDs because without identity there is no traceability or accountability as to who or what performed a particular task.  This accountability and traceability is there with Human operators because everyone has an Employee ID within an organization.  AI Agents have to accounted for from a security standpoint at the same level as humans and not as RPA scripts.  

The identity of an AI Agent within an organization can be tied to the Identity and Access Management system (IAM) of that enterprise which may Okta or Microsoft Active Directory etc.  In the real world this is tantamount to a Driver’s License for movement throughout America even for Domestic Air travel.

Now, if we extend the AI Agent’s reach outside of an Enterprise and need it to communicate with other AI Agents outside of the Enterprise, this crosses the Trust Boundaries governed by the IAM.  So, how can trust be established between two AI Agents across Enterprise or trust boundaries?  

A complex and unscalable approach would be the federation of IAMs between any two peering enterprises.  This is cumbersome and not scalable because it becomes a N(N-1)/2 problem.

Now, if we use a Decentralized Identity Access Management system (Registry) and a Decentralized ID then any Agent can discover, and authenticate any other Agent.  This is a scalable and inexpensive solution to a complex problem.  In the real world, this is tantamount to having to carry a Passport for International air travel.  This approach can also be used within an organization if an enterprise chooses to do so.

Another important aspect is how this Identity held is held by an AI Agent?

Each AI Agent whether operating internally within a trust boundary or between trust boundaries needs a receptacle to carry its identity.  In the real world, this is similar to how human’s carry a wallet with their Driver’s License, cash, credit cards, medical cards and more.  So a Wallet is needed to hold the identity of an Agent.

PROTOCOL

Once an AI Agent is equipped with a Decentralized ID, Wallet and is registered in a Registry, it is ready to communicate with other AI Agents.  But in order to do that, one needs a protocol – i.e. a way of communicating.  

This protocol needs two aspects – 

·         To authenticate the other agent(s) 
·         A vocabulary for communicating.

The authentication is common to any interaction as this is not context specific.

The communication vocabulary is however context specific.  

For instance, 

·         if two agents are trading with one another in the stock exchange, they are communicating about buying and selling equities at a given price.  

Whereas, 

·         if two agents are communicating on the topic of health insurance, they may be discussing ICD-10 and CPT codes appropriate for Medical billing.   

2. How can AI agent authentication and identity management prevent security risks?

Identity Management and Authentication are key building blocks in establishing trust between AI Agents.  As described earlier, one needs to have a decentralized ID, a Registry and a Protocol for communication to occur between any two AI Agents.

Now, the first half of that communication is to authenticate the other agent.  Say Agent A wishes to authenticate Agent B.  A number of trust factors would have to be established when each of these agents are initially registered on the Registry.

a. Provenance: 

Which entity created this agent ?  Are they legitimate?   An example of this is during App registration on the Apple App store, where Apple administers a rigorous background check on the entities attempting to submit a mobile application for listing.  Similar checks need to be done as part of the submission to the Registry. 

b. KYA:

To prove the legitimacy of an Agent, there needs to be a Know-Your-Agent (KYA) process established.  There will be background checks (police, Interpol, FBI and several other checks) similar to KYC/AML.

c. Secure Execution Environment: 

To avoid a legitimate agent being infected by malicious code that makes it behave in an improper manner, it is paramount that agents operate within a secure execution environment.

3. What industries are most likely to benefit first from widespread AI agent adoption?

There are many use cases for Agent to Agent communication that would improve efficiency and cost.  Let us describe a common one in Healthcare.

Healthcare

In a typical scenario when a patient arrives at a clinic for a health checkup, the patient presents their Health Insurance ID to the admin person.  The admin person then calls the Health Insurance company to verify the legitimacy of the Health Insurance ID.  This process is still done manually in most cases.  Upon completion of this check, the patient is admitted for consultation.  Upon completion, the notes are summarized, the Medical billing codes are then negotiated with the Health Insurance company.

If we decompose this example into a workflow, we can identity very easily the steps that can be solved by agents.

  • Insurance ID Verification – Verification Agentic (2 Party)
  • Consultation – Human
  • Transcription – Transcription Agent
  • Summarization  – Summarization Agent
  • Medical Billing – Billing Agent (2 Party)

4. How does AI agent interoperability impact regulatory compliance in industries like finance and healthcare?

In Healthcare and Finance there are compliance measures such as HIPAA and SOC2.   AI Agent communications are in fact safer than Human in the loop in many cases because AI Agents do not do the following:

  • Leave a paper trail e.g. writing critical info on Post-It Notes or notepads that Humans always do.
  • Talk loudly or spell out key information without realizing it could be recorded 
  • No audit trails for every interaction

Further measures include:

  • Protocols in Agent to Agent communication can be encrypted 
  • Storing information in repositories in a HIPAA or SOC2 compliant format
  • Masking Personally Identifying Information (PII) whenever needed 
  • Providing audit trails for every action and interaction with other agents or Humans

5. What ethical considerations come with AI agents handling autonomous transactions?

Ethical considerations are an important consideration when agents are used in workflows.  The state of the art AI Agents are still not at the maturity level industry wide to make ethical or moral decisions in our opinion.

To resolve this, when there are moral and ethical dilemmas, it is best to include Humans in the Loop as part of the decision making process.  If there are decisions that can be automated without such considerations, is when Agents can autonomously make decisions.  

In Autonomous agents, examples of such junction points where are ethical considerations can happen:

  • Healthcare – if a patient is issued an insurance denial by an Insurance bot , there need to be provisions for a Human in the Loop to review the case and make a decision as there may be life threatening issues.
  • Finance – a loan denial may involve a customer going through hardship.  Quite often hardships can be resolved with a payment plan and restructuring of finances.  Again, a Human in the Loop to show empathy  may be needed in a situation such as this.

6. How can businesses ensure AI agents remain aligned with human decision-making rather than operating independently?

Businesses can ensure AI Agents and Humans align on decision making by designing workflows with Human in the Loop.  This will ensure that there is oversight, traceability, accountability, observability and governance in all workflows.  

7. What role do decentralized architectures play in AI agent security and reliability?

As mentioned on the section on Identity and Access Management, Decentralized Architectures are key for establishing communication between Agents. 

Over time, we foresee all humans having their own Digital Twins.  These Digital Twins will operate on behalf of humans and carry out tasks such as shopping, searching, booking reservations, and more.

For this reason, unlike all other AI Agents, AI Agents made by Synergetics are NFTs from the ground up with Wallets and Identity-  ready to navigate the vast resources of the world wide web.

8. How will AI agents evolve from assisting human workflows to managing end-to-end processes autonomously?

In many enterprises, knowledge on work processes is buried with the staff working at these organizations.  We call this “Tribal Knowledge”.  

In order for enterprises to transition from AI Agent assisted human workflows to AI Agents operating workflows autonomously, it is necessary for enterprises to bring this tribal knowledge to the surface.

Once these workflows are are clearly understood, one can identify workflows that can be automated and run autonomously by AI Agents and those requiring human intervention.  

9. What lessons can enterprises learn from early adopters of AI-driven automation?

In this early stage, we are seeing a lot of companies claiming to have AI Agents but most are simply thin veneers on top of an LLM.

To have true AI Agents, one needs to consider:

  • Identity
  • Discoverability
  • Traceability, Observability, Accountability
  • Transaction Management, and more

These early AI Agents are simple Prototypes with very little thought given to long term considerations.  Hence, enterprises can learn from these experiences and evolve to more industrial-strength AI Agents which are more capable with sound engineering principles behind them.

10. What are the most common misconceptions about AI agents and their real-world applications?

Several common misconceptions are:

  1. Human job loss:  While there are concerns about some repetitive type work that can be easily automated, humans have always upskilled to better, higher value added work through multiple Industrial Revolutions of the past.  This time will be no different. In most complex workflows, there will be the need for Humans to be in the loop and so job loss fears are overblown.  New vocations will come about e.g. Prompt Engineer, and some older vocations would evolve e.g. Paralegal.
  2. Artificial General Intelligence:  In AI there are seven levels on evolution, and one of them is AGI.  Talk of AGI is again overblown because decision making in many cases is not simply the application of  logic to a problem.  It goes well beyond that.

    Other factors include:
  • Sentiment 
    • e.g. many a time humans are not logical but biological and decide based on the wisdom of the crowds
  • Emotions 
    • e.g. machines are not capable of emotions
  • Ethical considerations 
    • e.g. needs human in the loop
  • Moral considerations 
    • e.g. needs human in the loop
  • Sensory perception 
    • eg. automated car decides to take a turn based on the distance and speed of oncoming traffic


Raghu Bala is Founder of Synergetics.ai , an AI startup, based in Orange County, California.  He is an experienced technology entrepreneur and is an alumnus of Yahoo, Infospace, Automotive.com, PwC, and has had 4 successful startup exits.

Mr. Bala possesses an MBA in Finance from the Wharton School (University of Pennsylvania), an MS in Computer Science from Rensselaer Polytechnic Institute and a BA/BS in Math and Computer Science from the State University of New York at Buffalo.  He is the Head Managing Instructor at 2U and facilitates participants through MIT Sloan courses in Artificial Intelligence, Decentralized Finance and Blockchain.  He is also an Adjunct Professor at VIT (India), and an ex-Adjunct Lecturer at Columbia University, and a Deeptech Mentor at IIT Madras(India).

 He is a published author of books on technical topics and is a frequent contributor online for the last two decades.  His latest books include – co-author of “Handbook on Blockchain” for Springer-Verlag publications, and a Contributing Editor of “Step into the Metaverse” from John Wiley Press, and various technical articles on Medium.com.    

Mr Bala has spoken at several major conferences worldwide including IEEE Smartcomp – Blockchain Panel (Helsinki),  Asian Financial Forum in Hong Kong, Global Foreign Direct Investment Conference in Sydney (Australia) and Huzhou (China), Blockchain Malaysia, IoT India Congress, Google IO, and several more.  He is also served as a Board member of AIM – The global industry association that connects, standardizes and advances automatic identification technologies.

His current areas of focus include Product Development, Engineering and Strategy in the startups related to Agentic AI, Autonomous Agents, Generative AI, IoT, Artificial Intelligence, and the Metaverse.  His industrial domain knowledge spans Automotive, Retail, Supply Chain & Logistics, Healthcare, Insurance, Mobile & Wireless, and more.

The Subject That No One Is Talking About in Agentic AI Today: Identity

by Frank Betz

The Missing Piece in Agentic AI

Everyone is talking about agentic AI systems — how they will revolutionize business, streamline automation, and enhance human-machine collaboration.

But almost no one is talking about the foundational challenge that will determine whether these systems succeed or fail: identity.

Right now, the AI agents being built by tech giants and startups alike are nameless, faceless, and transient. They exist for a moment—running a task, executing a script — before vanishing into the digital ether. This lack of identity means there is:

🚫 No traceability – No way to verify which AI agent performed an action.
🚫 No accountability – No mechanism to hold AI systems responsible for their decisions.
🚫 No trust – No persistent identity for agents to securely interact with humans or other AI systems.

And yet, trust is the bedrock of every system humans rely on — whether in financial transactions, business negotiations, or even basic communications. Without persistent, verifiable identity, AI systems will remain untrusted and unscalable.

We’ve tackled this problem head-on by creating AI agents that can be permanently identified, tokenized, and securely stored in a digital wallet.

Let’s dive into why this is the missing key in agentic AI — and why telcos, enterprises, and policymakers need to pay attention.

The Human Parallel: How Identity Works in the Real World

A human identity follows a clear, traceable lifecycle:

1️⃣ Birth – You are assigned a birth certificate that permanently registers your identity.

2️⃣ Life – You carry IDs (such as a driver’s license, passport, employee badge) to prove who you are in different contexts.

3️⃣ Transactions – You sign contracts, pay bills, and interact with others using your verified identity.

4️⃣ Death – A death certificate marks the end of your legal presence.

Now, compare this to today’s AI agents:

❌ No birth record – An AI agent is spun up at will, with no permanent ID.

❌ No verifiable transactions – There’s no universal way to prove which agent did what.

❌ No traceability – If an AI-generated deepfake spreads disinformation, there’s no way to track it back to its source.

This lack of continuity is the Achilles’ heel of AI systems. The solution? Tokenized, persistent identity.

How Tokenized Identity Solves the Trust Problem

In computer science, a daemon is a background process that runs continuously, often providing essential system functions without direct user interaction. Humans, in many ways, resemble long-running daemons — once born, we persist continuously until death, with an uninterrupted existence and a traceable identity from birth to death.  Our identity is recorded, updated, and verified across systems, ensuring we are accountable for our actions throughout our lifetimes.  However, AI agents do not function this way.  Unlike humans, AI agents are not persistent by default — they can be spun up, perform a task, and shut down in seconds, leaving no inherent trace of their existence.  A single AI agent might execute a financial transaction, generate a piece of content, or initiate a system action before disappearing, with no way to verify who — or what — was responsible for that action. Without permanent identity and traceability, AI agents exist as ephemeral, unaccountable entities, making them vulnerable to misuse, fraud, and manipulation.

This is precisely why tokenized AI identity is critical. If an AI agent executes a harmful action — whether due to a coding flaw, a bad actor’s manipulation, or unintended consequences — how do we track the responsible party? Without a persistent identifier, it becomes impossible to assign accountability, regulate AI behaviors, or create reliable auditing mechanisms.  If a bot spreads misinformation, completes a fraudulent transaction, or executes an unauthorized system change, and then disappears upon shutdown, there is no trail leading back to its source.  Tokenization solves this by ensuring that AI agents have a permanent, immutable identity — one that persists whether the agent is running or not.  With tokenized AI, every action is traceable, every agent is accountable, and organizations can ensure responsible AI deployment. The Synergetics AgentWorks platform has implemented this at scale, ensuring that each AI agent, once created, has a lifelong, verifiable identity—a necessary step in making agentic AI systems secure, transparent, and fit for enterprise and global adoption.

At Synergetics.ai, we’ve developed a tokenization framework that permanently assigns a verifiable, blockchain-backed identity to every AI agent. We did the research, built what is at the moment the only one of its kind, and wouldn’t be as adamant about championing this product-centric approach if we didn’t see the tremendous societal value in:

📌 Tokenized Agents: Each AI agent is issued a unique, permanent ID upon creation.

📌 Blockchain Verification: The ID is stored on a secure ledger for full traceability.

📌 Zero-Knowledge Proofs (ZKP): Identity can be verified without exposing sensitive data—powered by Privado.ai’s ID framework.

📌 Wallet Storage: AI agents carry their identity in a digital wallet, just like humans carry passports and driver’s licenses.

This approach enables three critical functions for agentic AI:

✅ Trust & Accountability – Enterprises can verify which AI agent made a decision or completed a transaction.

✅ Cross-Enterprise Communication – Agents can authenticate themselves when working across organizations.

✅ Security & Compliance – AI systems can meet regulatory and ethical requirements in enterprise and government applications.

The Role of AI Wallets: Storing and Managing Identity

If AI agents are to operate autonomously, they need more than just an identity — they need a secure way to store and use it.

This is where Agent Wallets come in.

🛠 AgentWallet is a secure digital storage for AI agent identity, assets, and credentials. Just as a human carries IDs and credit cards in a physical wallet, an AI agent must have a trusted place to store its identity and interact with the digital world.

🔹 Key Features of an AI Wallet:

• Stores permanent agent identity
• Holds digital assets, cryptographic signatures, and credentials
• Allows for seamless authentication across enterprises
• Enables secure transactions between AI agents

Enterprise vs. Public Identity: A Two-Tiered System

Just as humans carry different forms of ID, AI agents will require two distinct identity types:  an enterprise ID and a public ID.

In the same way that a person receives a state-issued ID — such as a driver’s license — to verify their identity within their home state or country, an AI agent operating within an enterprise must also have a verifiable enterprise ID to authenticate itself in internal systems. This enterprise ID ensures that the AI agent is recognized, trusted, and authorized to perform specific functions within the organization’s secure, private network.  However, when a human crosses international borders, their state-issued ID is no longer sufficient — they need a passport to validate their identity across countries. Similarly, when an AI agent needs to operate outside its enterprise, interacting with external AI agents, digital services, or other organizations, it requires a public ID.

This public, blockchain-backed identity serves as a decentralized verification mechanism, ensuring that the agent is authenticated and trusted beyond its original enterprise environment. Just as a passport provides proof of identity, nationality, and authorization for international travel, an AI agent’s public ID enables it to securely interact with external systems, negotiate transactions, and build verifiable trust in agent-to-agent communications.

1️⃣ Enterprise ID (Private Blockchain)

🔹 Issued within a company for internal AI agents
🔹 Ensures secure transactions & compliance
🔹 Operates on Hyperledger Fabric or similar private blockchains

2️⃣ Public ID (Decentralized Ledger)

🔹 Allows AI agents to interact outside the enterprise
🔹 Used for cross-company AI negotiations, digital commerce
🔹 Runs on a public blockchain for transparency & verification

Without this dual-identity model, AI agents will be restricted in scope — unable to operate securely outside their original environment.

Why Telcos & Enterprises Must Act Now

The identity problem in AI isn’t a theoretical issue — it’s already playing out in real-world security concerns:

🚨 AI Deepfakes – Bots impersonate real people, spreading misinformation.
🚨 Automated Fraud – AI agents execute unauthorized financial transactions.
🚨 Data Leaks & Privacy Risks – Anonymous AI agents collect and misuse user data.

By adopting tokenized identity and AI wallets, enterprises and telcos can:

✔ Ensure traceability in AI-driven decisions
✔ Secure agent-to-agent communications
✔ Meet evolving AI governance & compliance standards

Final Thought: AI Identity is a Make-or-Break Issue

AI systems are evolving fast, but trust will determine their adoption. The next step? Embedding identity into the DNA of agentic AI. This will provide individual and enterprise users with:

✅ Permanent, blockchain-backed identity
✅ Secure, verifiable agent transactions
✅ Wallets for AI to store credentials & assets


Brian Charles, PhD, is VP of Applied AI Research at Synergetics.ai (www.synergetics.ai).  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.

(Part 2) AI Workloads Are Surging in the Enterprise. Can Telecom Players Support Their Needs?

by Frank Betz

Note: This is the second of a two-part series exploring the rise of autonomous businesses driven by agentic AI systems. In Part 1, I focused on how enterprises are adopting these systems to revolutionize operations and decision-making. Part 2 delves into how telcos and telecom-adjacent companies must evolve to support this transformation, building the infrastructure for agent-to-agent communication.


Part 2: Telcos Must Build the Infrastructure to Support Agentic AI, But They Don’t Know How to Do It.

The Evolution of Telecom: Supporting Enterprise Innovation

In Part 1, we explored how enterprises are rapidly adopting agentic AI systems to move toward autonomous business models.

This shift broadly parallels the historical evolution of telecom:

• Telcos first connected individual people and then people within enterprises (e.g., PBX systems).

• They then expanded to enable global communication between enterprises.

• Now, telcos must evolve again to support agent-to-agent communication in the age of AI.

Here’s the challenge: communication outside the enterprise is much more complex.  When AI enters the picture and the data workloads increase, it becomes an obstacle for organizations that are anything less than agentic in nature to function.  Such an agentic AI future for enterprises requires identity, trust, authentication, and authorization to operate at scale and autonomously—capabilities that telcos are uniquely positioned to deliver by virtue of their heritage as regulated entities and continual investment in developing nascent technologies.  At the same time, the world of decentralized, autonomous services such as those that support agentic AI systems historically is not a known operating environment for them.

The OSI Model and the Future of Telco Networks

Just as the OSI model created a framework for traditional telecommunications networking, it can guide telcos in building the next-gen infrastructure for agentic AI:

The OSI model is a seven-layer conceptual model for framing how various disparate hardware and software systems that comprise a telecom network must work together to send data over a network, owing to various technical, geographical and political boundaries.

• Layers 1, 2 and 3 of the OSI model address physical, data link and network layers respectively.

• Layer 4 (Transport): Here, telcos must ensure low-latency, high-bandwidth connectivity across BLE, WiFi, and cellular networks.

• Layer 5 (Session): Persistent, secure agent sessions must be supported to enable cross-enterprise collaboration.

• Layer 6 (Presentation): Protocols are needed to ensure seamless communication between diverse AI systems.

• Layer 7 (Application): App-level solutions are required in order to allow agents to discover, connect, and collaborate.

The Role of Telcos in Agent-to-Agent Communication

To enable secure, reliable, and scalable agent-to-agent communication, telcos must address several key challenges:

1. Transporting All of That Data:

Telcos need to enable enterprise-level support for petabytes of data flowing into and out of corporations every moment of every day.  To accomplish this, telecoms must provide a secure execution environment for AI agents in the transport of their date.  The AgentVM by Synergetics (Layer 4) enables data to traverse networks securely and efficiently by supporting AI-native cloud and edge processing across telco infrastructures.

2. Authentication and Authorization:

Telcos must provide infrastructure that enables agents to authenticate each other and exchange data securely. This aligns with the Session (Layer 5) and Presentation (Layer 6) functions of the OSI model.

3. Enabling Seamless Communication:

For agents that traverse networks, Telcos can leverage AgentFlow (Layer 5 and Layer 6) — a patented protocol for inter-agent communication. It ensures real-time, asynchronous interactions across enterprise boundaries.

4. Establishing Identity and Trust:

AI agents operating across enterprises need verified identities to ensure secure interactions. This is where tools like AgentRegistry from Synergetics comes in (Layer 7), enabling zero-knowledge proof identity verification and Know Your Agent (KYA) compliance.

5. Powering Transactions and Digital Commerce:

Telcos must support agent-driven transactions with solutions like AgentWallet (Layer 7), which handles digital assets, identity, and currency for autonomous agents.

Telcos at a Crossroads

The future of telecom isn’t just about connecting people—it’s about enabling autonomous AI ecosystems that will drive success for their enterprise customers. Telcos must:

·      Invest in AI-native infrastructure to meet the needs of enterprise AI.

·      Adopt decentralized, autonomous tools to integrate AI-driven identity, trust, and communication.

·      Build the next-gen OSI stack that supports agentic AI at scale.

The next wave of telecom innovation isn’t just AI-powered.  It’s AI-native. The question is: Are telcos ready to lead?


Brian Charles, PhD, is VP of Applied AI Research at Synergetics.ai (www.synergetics.ai).  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.

AI Workloads Are Surging in the Enterprise. Can Telecom Players Support Their Needs?

by Frank Betz

Note: This is the first of a two-part series exploring the rise of autonomous businesses driven by agentic AI systems. In Part 1, I focus on how enterprises are adopting these systems to revolutionize operations and decision-making. Part 2 will delve into how telcos and telecom-adjacent companies must evolve to support this transformation, building the infrastructure for agent-to-agent communication. Stay tuned!


Part 1: Enterprises Are Embracing Agentic AI; Is Yours, and is your telecom provider ready?

The Rise of the Autonomous Business

As businesses push toward automation and efficiency, we are witnessing the emergence of the autonomous enterprise. These organizations rely on agentic AI systems—independent, intelligent agents—to optimize decision-making, drive innovation, and handle real-time operations.

Having spent 20+ years serving telecom and enterprise companies around the globe, I’ve realized that the meteoric presence of highly interconnected, real-time

AI apps and systems like ChatGPT, Gemini and other enterprise systems communicating

with each other and ingesting large datasets may be the biggest boon ever know to enterprises –

and telecom companies’ biggest existential threat.  This evolution of managed

AI to agentic AI is the next frontier for any organization that consumes data or transports it.

David Arnoux’s model of “The 5 Levels of the Autonomous Business” perfectly captures this evolution for the enterprise company:

Let’s break this down…

• Level 1 (Manual): Humans control all tasks. Tech is limited to record-keeping.

• Level 2 (Assisted): Automation supports repetitive tasks, while humans make major decisions.

• Level 3 (Semi-Autonomous): Systems take over day-to-day tasks; humans step in for complex decisions.

• Level 4 (Fully Autonomous): Most operations and decisions are automated. Teams oversee performance and handle edge cases.

• Level 5 (Self-Evolving): Processes refine themselves via machine learning—for example, optimizing supply chains or marketing campaigns automatically.

We are rapidly moving into Level 4 and beyond, where businesses will increasingly depend on autonomous AI agents to handle everything from logistics to customer service to cybersecurity

The Enterprise Connection: Agentic AI in Action

To understand how agentic AI systems function and communicate within an enterprise, consider the role of Private Branch Exchange (PBX) systems from the telecom world. Special note: telcos should pay attention here because what I’m about to explain is going to be vital for your future survival.  Here’s the quick walkthrough:

In the early days of telephony, enterprises used PBXs to connect employees within their organization, enabling seamless internal communication while relying on telcos to connect them to the outside world.

Similarly, modern enterprises will use agentic AI systems to automate and optimize internal processes, with AI agents acting as decision-makers and communicators within the organization.

Imagine a logistics company using AI agents to dynamically reroute shipments in response to weather disruptions. These agents must communicate internally to adjust delivery schedules, optimize routes, and inform stakeholders.

However, this is just half the picture. To fully realize the potential of autonomous businesses, these AI agents must also connect and collaborate with agents outside the organization. In the legacy telecom world of the PBX, this is where the communication ends.  Voice calls stayed inside the enterprise; communicating externally required a different set of telecom technologies.  This brings us to the challenges of identity, trust, and communication infrastructure—a topic we’ll explore in Part 2.

What’s Next?

To meet the demands of autonomous enterprises, telecom companies will need to build the next generation of communication infrastructure that supports agent-to-agent connectivity. Much like the OSI model revolutionized traditional telecommunications, it can serve as a blueprint for integrating agentic AI systems into the fabric of modern networks.

Stay tuned for Part 2, where we’ll explore how telcos and telecom-adjacent players must adapt to this new reality.

Brian Charles, PhD, is VP of Applied AI Research at Synergetics.ai (www.synergetics.ai).  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.

ChatGPT Goes to Washington: OpenAI’s Big Play for Government AI

by Frank Betz

The AI revolution just got a policy upgrade. OpenAI has unveiled ChatGPT-Gov, a new, U.S. government-exclusive version of its AI assistant, designed to support federal, state, and local agencies in tackling complex challenges.

Why This Matters

Governments have long struggled to balance innovation with security, privacy, and responsible AI deployment.  With ChatGPT-Gov, OpenAI is signaling that AI isn’t just for boardrooms and startups.  It’s a tool that can empower policy analysts, public servants, and decision-makers to operate more efficiently.

Built on the robust GPT-4-turbo model, this platform provides:

  • ✅ A Secure, U.S.-Only Environment – Data isn’t shared with OpenAI’s broader research efforts.
  • ✅ Customizable AI Solutions – Tailored to the unique needs of agencies.
  • ✅ Strategic AI Deployment – Supporting research, communications, and decision-making at scale.

The Bigger Picture: AI & Public Trust

Bringing AI into the public sector isn’t just about efficiency.  It’s about trust.  While corporations race to integrate AI for competitive advantage, governments must ensure transparency, accountability, and ethical AI use.  OpenAI’s government-first approach could set a precedent for how AI operates in regulated environments.  It could also be a response to the release of DeepSeek’s inexpensive R1 model.

What’s Next?

As AI adoption accelerates in government, key questions emerge:

🔹 How will agencies measure AI effectiveness in policymaking?

🔹 What frameworks will ensure human oversight remains central?

🔹 Will this move push other AI leaders to develop public-sector-focused solutions?

One thing is clear: AI is no longer just disrupting business.  It’s reshaping governance. ChatGPT-Gov certainly looks like OpenAI’s bid to make AI a trusted ally in public service. 🚀


Brian Charles, PhD, is VP of Applied AI Research at Synergetics.ai (www.synergetics.ai).  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.

Geopolitics and Strategy in the AI Arena: The Impending Battle Between OpenAI-o1 and DeepSeek-R1

by Frank Betz

Large language models (LLMs) are driving significant technological progress in the rapidly evolving field of artificial intelligence. Leading the charge is OpenAI, whose state-of-the-art transformer technology excels in handling complex tasks across various domains. OpenAI’s journey began with pioneering research in AI fields like reinforcement learning and robotics, solidifying its reputation as a visionary in the AI community. The development of Generative Pre-trained Transformers (GPT), starting with GPT-1 in June 2018, was a milestone, showcasing the ability of LLMs to generate human-like text using unsupervised learning. Despite OpenAI’s dominance, DeepSeek has emerged as a formidable challenger with its innovative R1 model. These two approaches are not only advancing technology but also shaping geopolitical strategies, as nations and companies compete for AI leadership.

DeepSeek: The Open-Source Challenger

DeepSeek is making significant strides as a contender against established LLMs, particularly those of OpenAI. The R1 model is attracting attention for its impressive reasoning capabilities at a fraction of the cost. Utilizing an open-source framework, DeepSeek R1 is lauded for its transparency and flexibility for developers. This strategy enables R1 to directly challenge OpenAI’s models across numerous benchmarks, making advanced AI technologies more accessible to a wider audience. Available through DeepSeek API or free DeepSeek chat, the R1 model leverages open weights, providing a competitive edge by offering similar capabilities at a lower price point.

Key Highlights of R1’s Approach:

  • Cost-Effectiveness: DeepSeek R1 is priced between 90% and 95% cheaper than OpenAI’s o1, with an API cost of just $0.55 per million tokens compared to OpenAI’s $15. This strategy aims to increase adoption and capture a significant market share by making advanced AI capabilities accessible to a broader audience, including startups and smaller enterprises.
  • Reinforcement Learning Approach: Unlike traditional models that rely heavily on supervised learning and chain-of-thought processes, R1 primarily utilizes reinforcement learning to enhance its reasoning capabilities. This approach allows the model to self-improve by exploring different reasoning strategies and learning from the outcomes.
  • Benchmark Performance: In rigorous tests like LLM Chess, R1 demonstrated a respectable performance with a 22.58% win rate. However, it encountered challenges in maintaining protocol adherence, resulting in fewer draws and occasional illegal moves.
  • Consistency Challenges: While R1 shows promise, it struggles with instruction adherence and is prone to variations in prompts, sometimes leading to protocol violations or hallucinations, affecting its overall reliability in structured tasks.

OpenAI: The Proprietary Titan

In contrast, OpenAI maintains its proprietary model with o1, focusing on delivering controlled, high-quality performance. OpenAI’s models are renowned for their leading reasoning capabilities, as evidenced by their strong performance in LLM Chess, where o1-preview achieved a remarkable 46.67% win rate.

Key Highlights of o1’s Approach:

  • Proprietary Control for Quality Assurance: OpenAI’s closed model ensures rigorous maintenance of performance and safety standards, consistently delivering high-quality outputs and safeguarding against misuse.
  • Cost Consideration: While more expensive at $15 per million tokens, OpenAI justifies this premium by offering a model that excels in various complex tasks with greater reliability and accuracy, particularly in high-stakes environments where errors can have significant consequences.
  • Advanced Reasoning: o1 utilizes a sophisticated chain-of-thought reasoning approach, allowing it to perform deep contextual analysis and deliver nuanced outputs across diverse domains.
  • Benchmark Performance: o1 models lead in reasoning tasks, maintaining a positive average material difference in LLM Chess, reflecting their superior ability to strategize and adapt during gameplay.

Concerns and Controversies

  • Allegations of Mimicking OpenAI: DeepSeek has faced criticism for previously identifying itself as versions of OpenAI’s models. This raises questions about the originality of its technology, as it may replicate not just capabilities but also errors, or “hallucinations.”
  • Privacy and Data Security: DeepSeek’s adherence to Chinese laws, which include censorship, poses risks of manipulation and disinformation. Moreover, user data privacy is a major concern. Data stored in China under local regulations raises alarms similar to those associated with TikTok, affecting how Western users perceive and trust the platform.

Geopolitical Implications and Strategic Considerations

The competition between OpenAI and DeepSeek is a microcosm of the larger U.S.-China technological rivalry. DeepSeek’s open-source model promotes accessibility, highlighting the influence of Chinese regulatory practices. Both companies balance innovation with ethical considerations. OpenAI actively aligns itself with U.S. policymakers to support national security interests, advocating for policies that safeguard against potential cybersecurity threats and data privacy issues.

Governance and Compliance Implications

The divergent approaches of OpenAI and DeepSeek have significant implications for governance and compliance within the AI industry. OpenAI’s proprietary model is aligned with stringent compliance measures, ensuring that its AI technologies meet regulatory standards and ethical guidelines.

In contrast, DeepSeek’s open-source model presents unique governance challenges. While promoting innovation and accessibility, the open-source approach may struggle with ensuring compliance with evolving regulatory standards. The lack of centralized control can lead to variations in implementation, raising concerns about the consistency of compliance across different applications. DeepSeek may need to develop robust governance frameworks to address these challenges effectively.

Final Thoughts

The rivalry between OpenAI and DeepSeek transcends technological competition; it’s a strategic and geopolitical battle shaping the future of AI. OpenAI’s proprietary stance and engagement with U.S. policymakers reflect a commitment to maintaining leadership and security in AI development. Meanwhile, DeepSeek’s open-source model, despite its potential advantages, raises valid concerns about privacy, censorship, and originality. This competition also highlights the ongoing debate between open-source and closed systems, where each approach has its benefits and challenges.

Although large language models currently dominate, the future benefits of small language models should not be overlooked. They promise to make AI more accessible and sustainable, ensuring that advanced AI capabilities can reach a wider audience while minimizing resource usage. This evolution could play a crucial role in making AI tools both powerful and universally available, potentially impacting the strategic decisions of companies like OpenAI and DeepSeek in the future.

Frank Betz, DBA, an accomplished professional at Synergetics.ai (www.synergetics.ai), is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.

Charting the Course for AI Governance: The 2024 Regulatory Framework and 2025 Proposals for Change

by Frank Betz

As we approach the end of 2024, artificial intelligence continues to transform industries globally, necessitating a regulatory framework that evolves alongside its rapid advancement. The United States is at a pivotal crossroads, having created a comprehensive regulatory environment designed to balance innovation with ethical oversight. However, as AI technologies become increasingly embedded in daily life, the need for adaptive and forward-thinking governance becomes more pressing, setting the stage for significant proposals in 2025.

Looking toward 2025, several major themes are expected to shape AI regulation. Enhanced ethical oversight and transparency will be at the forefront, requiring AI systems to be explainable and understandable. Human-in-the-loop systems will gain prominence, especially in sectors where AI impacts human lives, ensuring that human judgment remains integral to decision-making processes. Data privacy and security will see intensified focus, with stricter standards for data protection and cybersecurity.

Bias mitigation and fairness will be critical, with regulations targeting discrimination prevention in AI outcomes across various sectors. Accountability and liability frameworks will be clarified, assigning responsibilities for AI-driven actions. Environmental impacts of AI will be scrutinized, prompting measures to mitigate the carbon footprint of AI technologies.

United States Federal Regulations and Proposals

The current regulatory landscape is supported by key federal regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the Federal Food, Drug, and Cosmetic Act (FDCA). These laws establish rigorous standards for privacy and safety in healthcare-related AI applications. They are complemented by the Federal Trade Commission Act, which extends consumer protection into the digital arena, ensuring that AI applications follow fair trade practices. Additionally, the 21st Century Cures Act facilitates the integration of AI into healthcare decision-making processes by offering exemptions for clinical decision support software, maintaining necessary safeguards while promoting innovation.

Federal Legislation Proposals

  • Better Mental Health Care for Americans Act (S293): Modifies Medicare, Medicaid, and the Children’s Health Insurance Program to include AI’s role in mental health treatment. Requires documentation of AI’s use in nonquantitative treatment limitations and mandates transparency in AI-driven decisions—status: Proposed and under consideration.
  • Health Technology Act of 2023 (H.R.206): Proposes allowing AI technologies to qualify as prescribing practitioners if authorized by state law and compliant with federal device standards. Aims to integrate AI into healthcare prescribing practices.—status: Proposed and under consideration.
  • Pandemic and All-Hazards Preparedness and Response Act (S2333): Mandates a study on AI’s potential threats to health security, including misuse in contexts such as chemical and biological threats, with a report to Congress on mitigating risks.—status: Proposed and under consideration.
  • Algorithmic Accountability Act (AAA): Requires businesses using automated decision systems to report their impact on consumers.—status: Proposed.
  • Federal Artificial Intelligence Risk Management Act: Aims to make the NIST AI Risk Management Framework mandatory for government agencies.—status: Proposed.
  • TEST AI Act of 2023: Focuses on advancing trustworthy AI tools.—status: Proposed.
  • Artificial Intelligence Environmental Impact Act 2024: Measures AI’s environmental impacts.—status: Proposed.
  • Stop Spying Bosses Act: Addresses AI use in workplace surveillance.—status: Proposed.
  • No Robot Bosses Act: Regulates AI use in employment decisions.—status: Proposed.
  • No AI Fraud Act: Protects individual likenesses from AI abuse—status: Proposed.
  • Preventing Deep Fake Scams Act: Addresses AI-related fraud in financial services.—status: Proposed.

State-Level Legislation and Proposals

A variety of innovative legislation at the state level addresses diverse regional needs. For instance, California’s AI Transparency Act mandates disclosure and enhances public awareness of AI-generated content. This strengthens the existing California Consumer Privacy Act (CCPA), a landmark legislation enacted in 2018, that provides California residents with enhanced privacy rights and consumer protection concerning the collection and use of their personal data by businesses. Illinois has strengthened its Human Rights Act to prevent AI-driven discrimination in the workplace, while states like Massachusetts and Rhode Island focus on ethical AI integration in mental health and diagnostic imaging services. Colorado has also made strides with legislation like SB24-205, requiring developers of high-risk AI systems to use “reasonable care” to prevent algorithmic discrimination and mandating public disclosures, effective February 1, 2026.

The following legislative efforts underscore the evolving regulatory landscape, aiming to harmonize technological advancement with ethical responsibility, setting the stage for significant regulatory proposals and changes in 2025:

  • Northeast U.S.
    • New Hampshire (HB 1688): Prohibits state agencies from using AI to surveil or manipulate the public, protecting citizens’ privacy and autonomy. Effective Date: July 1, 2024.
    • Massachusetts (An Act Regulating AI in Mental Health Services H1974): Requires mental health professionals to obtain board approval for using AI in treatment, emphasizing patient safety and informed consent.—status: Proposed and pending approval.
    • Rhode Island (House Bill 8073): Proposes mandatory coverage for AI technology used in breast tissue diagnostic imaging, with independent physician review.—status: Pending.
  • Southeast U.S.
    • Tennessee (HB 2091 ELVIS Act): Targets AI-generated deepfakes by prohibiting unauthorized use of AI to mimic a person’s voice, addressing privacy concerns and protecting individuals from identity theft and impersonation. Effective Date: July 1, 2024.
    • Virginia (HB2154): Requires healthcare facilities to establish and implement policies on the use of intelligent personal assistants, ensuring responsible integration into patient care and protecting patient confidentiality.—status: In effect since March 18, 2021.
    • Georgia (HB887): Prohibits healthcare and insurance decisions based solely on AI, requiring human review of AI-driven decisions to ensure they can be overridden if necessary.—status: Proposed and pending approval.
  • Midwest U.S.
    • Illinois (HB 3773): Amends the Illinois Human Rights Act to regulate AI use by employers, prohibiting AI applications that could lead to discrimination based on protected classes.
    • Safe Patients Limit Act (SB2795): Limits AI’s role in healthcare decision-making, ensuring registered nurses’ clinical judgments are not overridden by AI algorithms, emphasizing human oversight.—status: Reintroduced in 2024 and pending approval.
  • Southwest U.S.
    • Utah (SB 149): Establishes liability for undisclosed AI use that violates consumer protection laws. Mandates disclosure when consumers interact with generative AI and establishes the Office of Artificial Intelligence Policy to oversee AI applications in regulated sectors like healthcare. Effective Date: May 1, 2024.
  • West U.S.
    • California:
      • SB-942 California AI Transparency Act: Requires developers of generative AI to provide AI detection tools and allows revocation of licenses if disclosures are removed. Effective Date: January 1, 2026.
      • AB 2013: Obligates large AI developers to disclose data summaries used for training generative AI, fostering transparency. Effective Date: January 1, 2026.
      • Assembly Bill 3030: Requires healthcare facilities using generative AI for patient communication to disclose AI involvement and provide human contact options.
      • Senate Bill 1120: Mandates that medical necessity decisions be made by licensed providers and requires AI tools in utilization management to comply with fair standards.
      • Senate Bill 896 (SB-896): Directs the California Office of Emergency Services to evaluate the risks of generative AI, coordinating with AI companies to mitigate public safety threats.
      • Assembly Bill 1008 (AB-1008): Extends privacy laws to generative AI systems, ensuring compliance with data use restrictions.
      • Assembly Bill 2885 (AB-2885): Establishes a legal definition for artificial intelligence in California law.
      • Assembly Bill 2876 (AB-2876): Requires AI literacy considerations in education curriculums.
      • Senate Bill 1288 (SB-1288): Tasks superintendents with evaluating AI use in education.
      • Assembly Bill 2905 (AB-2905): Mandates AI-generated voice disclosures in robocalls.
      • Assembly Bill 1831 (AB-1831): Expands child pornography laws to include AI-generated content.
      • Senate Bill 926 (SB-926): Criminalizes AI-generated nude image blackmail.
      • Senate Bill 981 (SB-981): Requires social media to facilitate reporting of AI-generated deepfake nudes.
      • Assembly Bill 2655 (AB-2655): Mandates labeling or removal of election-related AI deepfakes.
      • Assembly Bill 2839 (AB-2839): Holds social media users accountable for election-related AI deepfakes.
      • Assembly Bill 2355 (AB-2355): Requires political ads created with AI to include clear disclosures.
      • Assembly Bill 2602 (AB-2602): Requires studios to obtain consent before creating AI-generated replicas of actors.
      • Assembly Bill 1836 (AB-1836): Extends consent requirements to estates of deceased performers for AI-generated replicas.
  • Colorado
    • SB24-205: Requires developers of high-risk AI systems to use “reasonable care” to prevent algorithmic discrimination and mandates public disclosures. Effective Date: February 1, 2026.
  • Other U.S.
    • West Virginia (House Bill 5690): Establishes a task force to recommend AI regulations that protect individual rights and data privacy, with implications for healthcare settings where sensitive patient data is involved.—status: Enacted.

Key Global Regulations

China AI Regulations: Mandates transparency and prohibits discriminatory pricing in AI, requiring clear algorithm explanations. Effective Date: March 1, 2022.

European Union AI Act: Categorizes AI systems by risk, imposes oversight on high-risk applications, and bans unacceptable-risk systems. Effective Date: August 1, 2024.

International alignment and standards will guide the harmonization of national regulations with global AI governance practices. The influence of the European Union’s AI Act and China’s stringent AI policies continues to shape U.S. strategies, underscoring the need for international alignment in AI governance. The World Health Organization (WHO) has issued guidelines for integrating large multi-modal models in healthcare, emphasizing ethical considerations and governance that align with international standards. Additionally, there will be specific attention to AI’s role in employment, workplace surveillance, and healthcare, ensuring ethical use and protecting individual rights. These frameworks underscore transparency, accountability, and fairness, setting benchmarks that U.S. regulations aim to meet or exceed.

Key Themes Shaping the Future of AI Regulation

Enhanced Ethical Oversight and Transparency: As AI systems become more integrated into critical decision-making processes, there will be a stronger emphasis on ethical oversight. This includes requiring transparency in AI algorithms, ensuring that decisions made by AI systems are explainable and understandable to users and regulators alike.

Human-in-the-Loop Systems: There will be increased implementation of human-in-the-loop systems, particularly in sectors where AI decisions can significantly impact human lives, such as healthcare, finance, and criminal justice. This approach ensures that human judgment and ethical considerations are factored into AI-driven decisions.

Data Privacy and Security: Strengthening data privacy and security measures will continue to be a priority. Regulations will likely mandate stricter data protection standards, including minimizing data collection, ensuring data anonymization, and enhancing cybersecurity measures to protect against breaches and misuse.

Bias Mitigation and Fairness: Addressing and mitigating biases in AI systems will remain a central theme. Regulatory frameworks will focus on ensuring fairness in AI outcomes, particularly in areas like employment, lending, and law enforcement, where biased algorithms can lead to discrimination.

Accountability and Liability: As AI systems gain more autonomy, assigning accountability and liability for AI-driven actions becomes crucial. Regulations may define clear responsibilities for developers, operators, and users of AI systems to ensure accountability for outcomes.

Environmental Impact: With growing awareness of environmental sustainability, there may be increased focus on assessing and mitigating the environmental impact of AI technologies. This includes energy consumption and the carbon footprint associated with training and deploying large AI models.6ti[ 

International Alignment and Standards: As AI is a global phenomenon, there will be efforts to align national regulations with international standards to facilitate cross-border cooperation and ensure consistency in AI governance globally.

AI in Employment and Workplace Surveillance: Regulations may address the use of AI in employment decisions and workplace surveillance to protect workers’ rights and prevent invasive monitoring practices.AI in Healthcare: There will likely be specific guidelines on using AI in healthcare to ensure patient safety, informed consent, and the ethical use of AI in diagnostics and treatment planning.

Strategies to Work Within the Framework of Regulations

To effectively navigate this complex regulatory landscape, organizations should consider:

Establish Clear Governance and Policies: Create governance frameworks and maintain compliance documentation.

Understand Regulatory Requirements: Conduct thorough research and adopt compliance frameworks (e.g., ISO 42001) to manage AI risks.

Incorporate Privacy by Design: Use data minimization, anonymization, and encryption to align with legal standards.

Enhance Security Measures: Implement robust security protocols and continuous monitoring.

Focus on Ethical AI Development: Mitigate biases and ensure transparency and accountability.

Implement Rigorous Testing and Validation: Use regulatory sandboxes and performance audits. A notable innovation in this regard is the use of AI sandboxes, such as the National Institute of Standards and Technology (NIST) AI sandbox initiative, which provides a controlled environment for testing AI technologies in various sectors.

Engage Stakeholders and Experts: Form cross-disciplinary teams and consult stakeholders.

Continuous Education and Adaptation: Keep teams updated on regulatory changes.

Conclusion

As the regulatory landscape evolves, 2025 promises to be a transformative year, with proposals that seek to refine and enhance AI governance. This overview explores the current state of AI regulations in the U.S., the proposals poised to reshape them, and the implications for the future of AI technology as we strive to harmonize innovation with ethical responsibility. An emerging trend among companies is the adoption of comprehensive AI governance frameworks that mirror the European Union’s efforts to protect human rights through fair and ethical AI practices. By embedding “human-in-the-loop” systems, especially in critical decision-making areas involving human lives, organizations not only bolster ethical oversight but also shield themselves from potential liabilities. This integration underscores a commitment to responsible AI development, aligning technological advancements with global standards of transparency and accountability.

Frank Betz, DBA, an accomplished professional at Synergetics.ai (www.synergetics.ai), is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.

Navigating Regulatory Challenges of Digital Twins with Agentic AI

by Frank Betz

In an era where digital innovation is transforming industries, digital twins represent a pinnacle of technological advancement. Initially conceptualized by Michael Grieves in 2002, digital twins have evolved from their industrial roots to become ubiquitous across various sectors. This evolution reflects the increasing complexity of regulatory landscapes, especially as digital twins incorporate decentralized agentic AI, paving the way for autonomous, intelligent systems.

Evolving Definition and Applications of Digital Twins

Digital twins were originally designed to replicate physical objects for enhanced monitoring and optimization. Today, they have evolved into comprehensive models that integrate personnel, products, assets, and processes, offering unprecedented insights. This transformation is particularly evident in the gaming industry, where non-player characters (NPCs) use AI to adapt and respond to players, illustrating digital twins’ potential to become sophisticated autonomous agents.

Decentralized Technologies in Digital Twins

Digital twins leverage decentralized technologies like blockchain and Directed Acyclic Graphs (DAGs) to revolutionize multiple sectors. Blockchain-based digital twins are integral to the virtualization of physical systems, gaming, and agentic AI. They use blockchain technology alongside Non-Fungible Tokens (NFTs) to simulate, monitor, and optimize systems. NFTs act as certificates of authenticity, ensuring each asset or data point is uniquely authenticated and securely recorded on the blockchain. This framework enhances trust, transparency, and operational efficiency within digital twin ecosystems.

Applications in Physical Systems

In real-world physical systems, digital twins enhance supply chain management by using NFTs to verify goods’ authenticity and facilitate seamless transactions. This approach boosts transparency and significantly reduces fraud. In smart cities, digital twins enable real-time monitoring and optimization, with NFTs representing specific assets for precise tracking. In healthcare, they manage patient data and medical equipment, ensuring record integrity and streamlining secure exchanges. These applications offer enhanced data integrity, security, and operational efficiency.

Impact on Gaming

In gaming, blockchain-based digital twins redefine asset ownership and player interaction. NFTs provide players with unique ownership of digital assets, while tokens enable transactions within decentralized marketplaces. This paradigm shift allows players to securely own and trade digital assets, fostering true ownership and control. Additionally, NFTs ensure the authenticity and history of digital assets, preventing fraud and creating novel revenue models and economic opportunities.

Role in Agentic AI

In the domain of decentralized agentic AI, technologies like blockchain-based digital twins play a pivotal role by using NFTs to secure data exchanges and transactions. This ensures all interactions are authenticated and recorded with unmatched integrity, supporting automated decision-making. Beyond blockchain, DAGs, such as those used by platforms like IOTA, offer scalable and feeless environments ideal for real-time data processing. These technologies empower businesses to optimize workflows, enhance customer engagement, and drive innovation, creating resilient infrastructures with reduced points of failure.

Regulatory and Legal Challenges: 10 Key Considerations

As digital twins integrate with agentic AI in business contexts, they face unique regulatory and legal challenges. Unlike gaming, which focuses on player interaction and data privacy, business applications require compliance with intricate regulatory frameworks due to sensitive data and operations. Here are ten key considerations:

1. Understanding Regulatory Requirements: Businesses must navigate diverse legal environments to deploy digital twins effectively. This requires adhering to international trade regulations and standards while ensuring data privacy compliance, such as with GDPR.

2. Incorporating Privacy by Design: Especially crucial in sectors like healthcare, privacy by design involves integrating data anonymization and encryption to prevent unauthorized access and ensure compliance with regulations like HIPAA.

3. Consent Management: Implementing robust consent management systems is essential to handle complex data ownership and usage rights, as well as maintaining transparency and trust with clients and partners.

4. Enhancing Security Measures: Industries like real estate and healthcare require robust security measures to protect against cyber threats, including continuous monitoring and advanced threat detection.

5. Focusing on Ethical AI Development: Avoiding biases and ensuring fairness in AI development is critical. Businesses should implement AI governance frameworks with bias detection and mitigation strategies.

6. Implementing Rigorous Testing and Validation: Regulatory sandboxes allow businesses to test new digital twin applications in controlled environments, refining AI behaviors and ensuring compliance before full-scale deployment.

7. Engaging Stakeholders and Experts: Cross-disciplinary collaboration with legal, ethical, and industry experts is vital to ensure applications meet regulatory requirements and maintain ethical standards.

8. Continuous Education and Adaptation: Investing in ongoing education helps businesses keep pace with regulatory changes and technological advancements, ensuring continuous compliance and innovation.

9. Establishing Clear Governance and Policies: Defining data ownership, usage rights, and compliance responsibilities is crucial for managing digital twins, drawing on established governance models from industries like finance and healthcare.

10. Addressing Algorithmic Transparency: Ensuring algorithms are transparent and explainable is essential for building confidence in AI-driven outcomes and adhering to emerging regulatory standards focused on AI accountability.

Conclusion: Harmonizing Innovation and Regulation

As digital twins and decentralized agentic AI continue to evolve, it is imperative that regulatory frameworks adapt to address emerging challenges. While current regulations primarily focus on data protection and privacy, future frameworks must anticipate and accommodate the autonomous capabilities of AI. For organizations, aligning corporate policies with these regulatory advancements is crucial to maintaining trust and fostering responsible innovation.

Platforms like Synergetics.ai play a pivotal role in advancing AI integration with regulatory frameworks by utilizing specific Ethereum Request for Comments (ERC) standards. This approach forms part of an explainable AI strategy, facilitating trusted interactions within digital ecosystems and ensuring transparency and accountability.

The transformative potential of decentralized agentic AI, particularly in the realm of digital twins, necessitates careful navigation of regulatory landscapes. By embracing ethical AI development and implementing robust governance practices, organizations can ensure that digital twins progress responsibly. Aligning corporate strategies with evolving regulatory standards is essential to fostering innovation while safeguarding ethical principles and public trust.

Frank Betz, DBA, an accomplished professional at Synergetics.ai (www.synergetics.ai), is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.

Building a Governance System for Explainable Decentralized AI: Tools, Frameworks, and Operational Practices

by Frank Betz

As artificial intelligence (AI) continues to evolve, the need for robust governance systems has become increasingly vital. The integration of AI across various sectors requires organizations to ensure their systems are not only effective but also ethical and accountable. This is particularly critical for explainable decentralized AI, which empowers users and systems to make informed decisions collaboratively. The unique features of decentralized AI, such as its distributed nature and reliance on community governance, present distinct challenges that necessitate tailored governance strategies. In this blog post, I will explore the practices necessary for implementing a governance system for explainable decentralized AI, along with the tools and frameworks that support these practices, all while focusing on compliance with U.S. and EU laws and regulations.

Understanding the Regulatory Landscape

Navigating the regulatory landscape for AI is crucial for organizations operating globally, as different regions have established distinct frameworks to manage AI deployment. In the United States, the regulatory environment is still nascent and evolving, presenting complexities due to a patchwork of federal initiatives and state laws. For example, the AI Bill of Rights promotes essential principles such as privacy, non-discrimination, and transparency, signaling a shift toward prioritizing individual rights in the development of AI technologies.

Additionally, the Algorithmic Accountability Act proposes mandatory impact assessments and audits to enhance fairness and mitigate bias in AI systems. This act reflects a growing recognition of the need for accountability in AI deployment. State-level regulations, such as the California Consumer Privacy Act (CCPA), further enforce strong data protection rights, showcasing the diverse legal landscape that organizations must navigate.

The Federal Trade Commission (FTC) plays a pivotal role in the U.S. regulatory framework by ensuring that AI technologies do not engage in deceptive practices. The FTC has issued guidelines that emphasize fairness and transparency in AI, although these regulations are not enforceable in the same way as laws. Moreover, the National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework, which provides non-enforceable guidelines for managing AI-related risks. NIST standards, such as those focusing on risk assessment and governance principles, serve as valuable resources for organizations seeking to align their practices with best practices in AI development and deployment.In contrast, the European Union’s Artificial Intelligence Act (AIA), effective in 2024, adopts a more comprehensive approach to regulation. The AIA employs a risk-based strategy, categorizing AI applications by risk levels and establishing a European Artificial Intelligence Office for compliance oversight. This framework promotes collaborative governance by incorporating diverse stakeholder perspectives into policy-making.

The Importance of Understanding Global Compliance Frameworks

As AI regulations evolve, organizations must understand global compliance frameworks to navigate varied regulatory approaches effectively. The EU’s AIA emphasizes collaborative governance and risk-based categorization, while the U.S. prioritizes consumer protection and accountability without a centralized framework. This discrepancy presents challenges for multinational companies that must comply with both the AIA’s stringent standards and the evolving state and federal regulations in the United States.

Organizations engaging with European markets must align their AI practices with the EU’s rigorous regulations, as non-compliance can lead to significant penalties and reputational harm. The EU’s focus on individual rights and privacy protections sets a precedent that influences global compliance strategies. Furthermore, organizations should monitor alliances such as the G7 and OECD, which may establish common standards impacting national regulations. By understanding the evolving global compliance landscape, companies can adapt to regulatory changes and seize opportunities for innovation and collaboration.

Key Practices for Governance

The complexities of AI governance are driven by evolving laws and regulations that vary across jurisdictions. Therefore, organizations should adopt a structured approach that prioritizes stakeholder requirements, adheres to policy frameworks, and aligns with corporate strategic guidelines. This is especially important for decentralized AI, which lacks a central authority and relies on community governance.

Staying informed about current laws and regulations, as well as anticipated changes, is essential for navigating these complexities. By remaining vigilant to regulatory developments and emerging trends, organizations can proactively adjust their governance frameworks to ensure compliance and minimize legal risks. This strategic foresight enhances an organization’s credibility and reputation, enabling it to respond swiftly to new challenges and opportunities in the AI domain.

  • Stakeholder Engagement: Actively engaging stakeholders from diverse sectors—legal, technical, ethical, and user communities—is vital for gathering a broad range of perspectives. Establishing advisory committees or boards facilitates ongoing dialogue and ensures that the governance framework reflects the needs of all relevant parties. Utilizing platforms for stakeholder collaboration can help identify and engage key stakeholders to gather feedback and ensure that AI systems meet user and societal expectations.
  • Transparency and Explainability: Organizations must prioritize transparency in AI decision-making processes. Developing mechanisms that make AI outputs understandable fosters trust and accountability. Implementing Explainable AI (XAI) techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) can clarify complex AI models, providing insights into decision-making processes.
  • Regular Risk Assessments: Conducting regular risk assessments is essential for identifying potential ethical, legal, and operational risks associated with AI deployment. Evaluating the impact of AI on employment, privacy, and security allows organizations to develop proactive mitigation strategies. The NIST AI Risk Management Framework provides structured guidelines for managing these types of risks.
  • Collaborative Governance Framework: Creating a governance structure that includes cross-functional teams and external partners is crucial. A collaborative framework encourages resource sharing and exchange of best practices, ultimately enhancing the governance of AI technologies. The establishment of the European Artificial Intelligence Board under the AIA exemplifies a governance model that promotes stakeholder collaboration.
  • Monitoring and Evaluation: Establishing metrics and Key Performance Indicators (KPIs) is essential for monitoring AI performance and ensuring compliance with regulatory standards. Continuous evaluation processes allow organizations to adapt to new challenges while maintaining regulatory compliance. Utilizing Model Cards can help document AI models, including their intended use and potential biases, thereby enhancing accountability.
  • Education and Training: Investing in training programs for employees and stakeholders is crucial for enhancing understanding of AI governance and ethical practices. Promoting awareness of responsible AI usage fosters a culture of accountability within the organization. Platforms like AI Ethics Lab provide comprehensive resources and workshops to help teams implement ethical AI principles effectively.

Conclusion

Navigating the complexities of deploying explainable decentralized AI underscores the critical need for a robust governance system. By prioritizing stakeholder engagement, transparency, risk assessment, collaborative governance, monitoring, and education, organizations can ensure their AI systems are ethical, transparent, and compliant with U.S. and EU laws. The journey toward effective AI governance is ongoing and requires collaboration, flexibility, and a commitment to continuous improvement. By emphasizing explainability and accountability, organizations can harness the full potential of AI technologies while safeguarding societal values and fostering public trust. As we move forward, let us embrace the opportunities that responsible AI governance presents, paving the way for a future where technology and ethics coexist harmoniously.

Frank Betz, DBA, an accomplished professional at Synergetics.ai (www.synergetics.ai), is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.

How Synergetics AI Agents in Finance are transforming the sector?

by Frank Betz

Artificial Intelligence is spreading its wings all over the world. They are becoming the backbone of every industry. Whether it’s healthcare autonomous agents or any AI agent, they are working as the invisible person who can able to process mountains of data in just a second and deliver faster, smarter solutions. But AI isn’t only about convenience; it’s more about transforming how things are done entirely, from customer service to complex risk analysis.

Like that, Synergetics AI agents are stepping up in the financial world, changing the game in numerous ways that are hard to ignore. They’re like a helpful co-worker who are always there, watching trends, finding new chances, and ensuring everything runs well. These AI agents do everything from offering personalized advice to making financial services smarter, faster, and more accessible.

Have we piqued your interest? In this blog, you will learn about how Synergetics AI agents are reshaping the financial landscape.

Why Synergetics AI Agents Are a Game-Changer in Finance?

So, you must be thinking, what’s the secret behind Synergetics’ AI agents? Well, this artificial intelligence solution is all about its ability to simplify the complex, especially in a sector like finance that deals with endless data and ever-changing market dynamics. Let’s learn the key reasons behind their impact.

  1. Streamlining Risk Management and Analysis

We know that risk management is at the heart of any financial operation. It keeps businesses ready for changes in the marketplace, stock markets, and other financial dangers, and Synergetics AI has made this a top priority. Our AI bots understand the technical aspects of financial risk and can easily analyze vast datasets. They can also alert you when changes are rapidly approaching so you can change your plan and protect your funds before it’s too late.

  1. Better & Customized Customer Service

Everyone desires customized service that matches their needs. Synergetics AI Interaction agents offer this special service 24/7. They first listen to customer preferences, then understand them, and then offer financial advice that best suits them. They are like personal financial advisors who are available 24/7. Keep in mind this is not a one-way chat but an actual exchange.

They also have a wealth management agent who can help you provide upcoming market insights, tax-efficient strategies, and much more. Check out the video to learn about this agent. 

  1. Faster, Easier Loan Approvals

Every time you applied for a loan, you had to wait days or even weeks to hear back. Well, those days are going to fade away. Synergetics AI agents speed up the loan approval process by using advanced algorithms that can assess a borrower’s creditworthiness in minutes. They look beyond just credit scores and consider other factors like spending patterns and payment histories.

This means people like freelancers or those with a short credit history have a better chance of approval. Plus, lenders get a clearer view of borrowers, leading to fewer defaults and happier clients.

  1. Staying Ahead with Real-Time Market Insights

In the financial world, things change quickly. A stock might rise one moment and fall the next. With AI agents, financial companies don’t have to worry about this. These AI agents monitor market data in real-time, providing instant insights that can help investors make the right moves at the right time.

  1. Transforming Investment Strategies

Speed and accuracy are everything in finance. One can get help from AI agents to get unique investment ideas and the possible execution. These agents can easily find profitable opportunities that ultimately increase the ROI of a company.  

The Future of Finance with Synergetics AI Agents

The future of finance with Synergetics AI agents looks bright and exciting! These smart AI tools are ready to change how we handle our money, making everything quicker and easier. For example, one can get financial advice anytime they need it or get an idea about a loan in just a few minutes.  

Conclusion

Hence, whether it’s finance or any other artificial solution, Synergetics AI agents are changing the game for the better! They are expert in making AI agents for all kinds of finance services.

From speeding up loan approvals and helping customers feel valued to giving smart advice whenever needed, they provide agents who give one-to-go solutions for any business. Check out the Synergetics today to learn about all our AI products.

Frank Betz, DBA, an accomplished professional at Synergetics.ai (www.synergetics.ai), is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.