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Identity in a Multi-Agentic world

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Overview

As artificial intelligence and automation evolve, we are entering a multi-agentic world. Multi-agentic implies a distributed environment where autonomous software agents, APIs, machine learning models, and human users act in concert. Identity is no longer a technical detail; it is a core requirement for system integrity, trust, and control.

Agents today write code, deploy infrastructure, triage support tickets, summarize meetings, and in some cases make decisions. How do you know who made the decision and what liability your organization faces when agents collaborate, orchestrate, and reason?

You can’t coordinate a system that you cannot trust, and trust starts with a core capability: identity!

Identity is not for users only

Single sign-on, multi-factor authentication, and directory sync are basic requirements for user identity. With hundreds or even thousands of non-human agents like retrieval bots, security assessments, AI code reviewers, and autonomous workflows active at any given time, these measures are essential.

In large organizations, understanding the workflow, accountability, and permissible actions is crucial. Identity plays a fundamental role in these situations.

The issue affects everyone. It represents a change in control systems. Identity isn’t just about who logs in; it’s about making secure, transparent decisions and taking actions on a large scale.

Why is identity core to multi-agentic systems?

Identity is crucial to multi-agent systems. Here are six important reasons why.

🔐 Trust and Authentication

In a decentralized agent ecosystem, it’s important for us to understand who or what we are interacting with.

Is this code review coming from an approved AI agent or a spoofed script? Is it a rogue bot or an authorized user that initiates the workflow?

We can’t rely on IP addresses or client secrets anymore. We need signed, verifiable agent identities that persist across time and context.

🧾 Auditability and Accountability

Without identity, there’s no provenance.

When agents approve purchases, modify infrastructure, or triage incidents, it is essential to maintain a complete and tamper-proof record of the actions taken:

  • Who acted
  • On whose behalf
  • Under what authorization
  • With what outcome

This isn’t just good practice. It is essential for security, compliance, and debugging in enterprise systems.

Remember the old Abbott and Costello routine on “Who’s on first?” Who knew that comedy routine was so prescient!

👥 3. Delegation and Agent Chaining

In human teams, we delegate work to others. In multi-agent systems, delegation becomes the norm.

For example:

  • A user asks their assistant to generate a report
  • The assistant calls a forecasting agent
  • The forecasting agent queries a data governance agent to ensure compliance

At every step, the original identity and permissions need to be preserved. We must know who initiated the action and whether each agent is authorized to act on their behalf.

🔐 4. Fine-Grained Authorization

Multi-agent systems don’t work if every agent has God mode.

Each agent needs just enough access to do its job and no more.

This means:

  • Identity-linked roles and scopes
  • Time-boxed or task-limited permissions
  • Attribute-based access policies that adjust dynamically

Without strong identity-linked authorization, we’re just building smarter ways to breach ourselves.

🧠 5. Personalization and Adaptation

Good agents don’t just act; they learn.

But learning requires context:

  • What team is this user on?
  • What systems do they interact with?
  • What are their preferences?

Identity is the gateway to this context. It allows agents to personalize their behavior, become more useful over time, and avoid making dumb, default assumptions.

🌐 6. Interoperability Across Ecosystems

As agents start to collaborate across platforms, e.g., your Jira assistant talking to a GitHub bot or a Salesforce AI; the need for interoperable identity becomes critical.

That’s where standards like OIDC, SCIM, and even emerging ideas like Decentralized Identity (DID) and Verifiable Credentials (VCs) come into play.

Imagine an agent from Microsoft Graph collaborating with one from Atlassian Forge is only possible if identity flows freely but securely between them.

What Happens When You Don’t Prioritize Identity?

Without a strong identity layer, you get:

  • Shadow agents making untraceable decisions
  • Permission creep where every agent can do everything
  • Cross-system silos that break orchestration
  • Unverifiable outputs from unknown actors

It’s a recipe for disaster in regulated, security-sensitive, or high-trust environments.

Identity Is the New Control Plane

In human organizations, identity governs org charts, responsibilities, and roles.

In a multi-agent system, identity governs logic, execution, and autonomy.

Here’s where we need to invest:

  • Agent Identity Lifecycle – Issue, rotate, revoke agent credentials
  • Delegation Frameworks – Secure “on-behalf-of” interactions
  • Observability Tied to Identity – Logs, metrics, and decision trees with clear attribution
  • Policy-as-Code for Access – Role and attribute-based access enforcement
  • Cross-Domain Trust – Federated or decentralized identity for external agent collaboration

Closing Thoughts

We’re moving toward a world where autonomous agents are as common as microservices—and far more powerful. But without a robust identity layer, these systems will be fragile, opaque, and untrustworthy.

As a product leader, I see it clearly: identity is no longer a backend feature. It’s an architectural foundation for the next generation of intelligent systems.

If you’re building for the future, start by asking:

“Can I trust the agents in my system?”

And then:

“Can I prove it?”

In a multi-agent world, identity serves as the foundation for all trust.

Let’s connect: If you’re working on agent frameworks, trust layers, or identity models, I’d love to trade notes. The agentic future is here, let’s build it right.

Deductive Reasoning: Humanity’s Edge on the Age of AI

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Introduction: Fear and the Fallacy

There are many stories about AI taking over human jobs. Each time technology advances, like with steam power, assembly lines, or automation, people worry about losing their jobs. AI, especially large language models, has brought back those fears. However, it’s crucial to recognize that while AI offers many benefits, it also brings big challenges. At a deeper level, deductive reasoning continues to be a lasting strength of humans.

The Three Types Reasoning and AI has challenges

To understand why, let us start with the basics of human reasoning:

Reasoning Type DescriptionExample AI Proficiency
Deductive From general rules to specific conclusionsAll planets orbit stars. Earth is a planet → Earth orbits the Sun ❌ Weak (needs Symbolic systems)
Inductive From specific observations to general rulesEarth, Mars, and Jupiter orbit the Sun → All planets orbit stars ✔️ Strong (Pattern Learning)
AbductiveBest explanation given incomplete dataThe ground is wet → It probably rained ✔️ Strong (Probabilistic modeling)


AI excels at inductive and abductive reasoning because its architecture is probabilistic and data-driven. But deductive reasoning, which underpins scientific discovery, legal frameworks, and mathematical proofs, remains deeply challenging for AI.

Why deductive Reasoning is Hard for AI

them based on training data. That’s fundamentally different from how humans deduce facts from axioms.

Key Limitations of AI in Deductive Reasoning:

  • Non-determinism: Outputs vary even with the same input due to probabilistic sampling.
  • No grounding: LLMs lack a symbolic understanding of truth or causality.
  • Memory bottlenecks: Deduction requires sustained multi-step reasoning, often exceeding token windows.
  • Computational complexity: Symbolic logic engines require significant memory and computational resources, making them unsuitable for the current transformer-first AI infrastructure.

In essence, LLMs can mimic deduction, but they cannot construct or verify deductive truths unless tightly coupled with external logic engines.

Historical Parallel: Kepler and the limits of today’s AI

Consider how Johannes Kepler derived the laws of planetary motion. He didn’t just observe planets; he deduced laws from data, noticing elliptical orbits and harmonic relationships others overlooked.

Today’s AI could ingest the same data, classify it, and perhaps fit a regression curve. But could it infer a universal law from physical patterns?

AI cannot infer a universal law from physical patterns without external symbolic tools, and it cannot do so instinctively either.

This is the crux: humans don’t just learn from labeled data; we synthesize, infer, and challenge. These are traits AI lacks.

The path to Artificial General Intelligence (AGI) requires Symbolic Intelligence

To transition from Narrow AI to General AI (AGI), our models must establish a connection between statistical learning and symbolic logic.

Emerging models that might enable deductive AGI:

  • Symbolic Logic Engines: e.g., SAT solvers, Prolog, Z3 – already used in theorem proving.
  • Neuro-Symbolic Systems: e.g., DeepProbLog, Logic Tensor Networks – fuse neural nets with logic.
  • Probabilistic Logic Models: e.g., Markov Logic Networks, Bayesian Logic – approximate deduction under uncertainty.

These frameworks begin to touch the nuance humans process instinctively. But they remain research-heavy and highly compute-intensive, limiting their real-world scalability today.

AI is a tool: It raises the floor and the roof

Yes, AI will eliminate certain types of entry-level cognitive work, much like robots replaced repetitive tasks on factory floors. But just as factory workers evolved into process engineers, robot maintenance technicians, and quality optimization experts, so too will today’s workforce evolve to supervise, audit, and extend intelligent systems.

The issue is not about job loss but job transformation.

  • Raising the floor: Automating routine tasks, freeing humans from grunt work.
  • Involves the creation of new domains, such as reasoning over AI outputs, validating symbolic inferences, or designing new logic-based systems.

Just as programming evolved from assembly to C++ to Rust, AI evolves the way we interact with computation. But it doesn’t replace our capacity to reason. It extends it.

The real jobs of the future: Observation, Inference, and oversight

As AI improves, our role will change to:

  • Monitoring outputs for bias, hallucination, and logical consistency
  • Observing systems and inferring gaps in their logic
  • Scaling knowledge across domains that require deductive precision
  • Securing systems where probabilistic behavior may lead to unpredictable or adversarial outcomes

These are not “basic tasks.” They’re deeply human responsibilities.

Conclusion: Our future is not post-human, it is Post-redundancy

AI won’t replace us; it will make us more essential. By handling repetitive tasks, we can concentrate on our unique ability, the capacity to think critically.

Deductive reasoning is more than a method; it’s a way of thinking. It has supported scientific advancements, philosophical ideas, and legal systems. Even in the age of AI, it remains our greatest competitive advantage.

Crossing the Chasm with AI: Why Security, Privacy, and Transparency Will Drive Mainstream Adoption

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Artificial intelligence (AI) dominates headlines and boardroom conversations. From chatbots to copilots, AI feels everywhere. But if we apply Geoffrey Moore’s classic “Technology Adoption Lifecycle,” we see a different story: despite the hype, AI still sits with Innovators and Early Adopters. The Early Majority, the pragmatic users who drive true mainstream adoption, remain cautious. Why? They demand trust, and trust in AI hinges on three pillars: security, privacy, and transparency.

Security First: The Foundation of Trust

AI changes the security landscape. Traditional software already faces a barrage of attacks, but AI introduces new risks. Imagine an AI agent with the power to automate tasks across a business. If attackers exploit a vulnerability or misconfiguration, the consequences could be catastrophic: privilege escalation, data exfiltration, or even manipulation of business decisions.


Security must come first. Enterprises, especially in regulated industries, will not trust AI until it proves resilient against both old and new attack vectors. AI systems must defend against prompt injection, adversarial attacks, and unauthorized data access. Companies need robust controls, continuous monitoring, and clear incident response plans.

Pros of investing in AI securityCons and challenges
Reduces the risk of breaches and attacksSecurity investments can slow down deployment and innovation
Builds trust with enterprise and regulated customersIncreased complexity and cost
Protects against new AI-specific threats Overly restrictive controls may limit AI’s capabilities
Extra security measures can introduce friction for end users


The bottom line: Without strong security, AI will never cross the chasm to the Early Majority.

Privacy: The Competitive Edge

Organizations and individuals hold deep concerns about privacy. Companies hesitate to use proprietary data to train public models, fearing they’ll lose their competitive edge. Consider a manufacturer with unique processes or a retailer with exclusive customer insight; these are valuable assets, not mere inputs for public AI models.


On the personal side, AI blurs the boundaries of privacy. In the past, searching Google for symptoms allowed you to maintain a certain sense of anonymity. Now, if you share health information with an AI chatbot, that data might reinforce the model’s learning. Suddenly, your private details could influence future predictions, raising the specter of data misuse, just as search engines and social platforms have long monetized our data.


AI must respect privacy. Curated, local, or federated models that do not leak sensitive information will win trust. Privacy-preserving techniques, such as differential privacy, data minimization, and on-device processing, will become essential.

Pros of prioritizing privacyCons and trade-offs
Protects user and organizational data Inadequate data may reduce the accuracy of the model.
Preserves Competitive Advantage Limits the scope of AI learning and generalizing.
Reduces the risk of regulatory penalties Can complicate data management and integration
Builds user trust and willingness to adoptIncreased privacy controls may require more resources to implement

If we want the Early Majority to embrace AI, we must treat privacy as a feature, not an afterthought.

Transparency: The Art of Questioning

AI models, particularly large language models, function as opaque entities. They generate answers by calculating probabilities based on weights, biases, and vast training data. As users, we risk outsourcing our thinking to these systems unless we demand transparency.

Transparency empowers users. When AI provides clear reasoning or explanations, we can evaluate, question, and challenge its outputs. This art of questioning keeps us in control and prevents blind trust in machine-generated answers.
But transparency has its limits. Too much openness can reveal proprietary methods or make it easier for bad actors to manipulate the system. We must strike a balance: enough transparency to foster trust and accountability, but not so much that we expose the system to new risks.

Pros of transparencyCons and risks
Increases user trust and understandingMay expose proprietary methods or intellectual property
Facilitates regulatory compliance and auditingCould be exploited by adversaries to game the system
Encourages responsible and ethical AI useCan overwhelm users with too much information
Enables better debugging and error correctionMay slow down model deployment if explanations are required

How Curated Models will shine!

The next wave of AI adoption will not come from bigger models or more data alone. It will come from curated, secure, and privacy-preserving AI systems. Whether in software or manufacturing supply chains, organizations want to protect their unique value. They will not willingly use their competitive advantage to train public models.
Curated models, trained on carefully selected, private, or domain-specific data, offer a path forward. These models can deliver high performance while respecting privacy and security requirements. They also provide clearer transparency, as their scope and training are well defined

Build Trust: The path to Early Majority

To win over the Early Majority, the AI community should:
• Focus on strong security to combat threats
• Make privacy integral to design, not an add-on
• Ensure transparency so users can understand AI decisions
We also need to educate users: AI is a tool, not a prophet. When an AI provides answers, we should continue asking questions. Does the reasoning add up? Can we follow the logic? Only then can we use AI wisely and with confidence.

Conclusion

AI is close to becoming widely used. The Early Majority is looking for evidence that AI systems are safe, private, and clear. By focusing on these aspects now, we can make the leap and create a strong base for long-lasting, responsible innovation.

Vibe Coding: The unintended consequence

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Introduction

AI-assisted software development is growing, and as someone who enjoys empowering people with technology, I find this trend exciting. It involves creating or changing software using clear ideas, natural language prompts, or basic frameworks. It’s quick, impressive, and quite freeing.

And yet, when you look at the bigger picture, something feels off.

Statistical patterns vs grounded in secure practices

While vibe coding is a useful tool for sharing ideas, creating prototypes, exploring, and onboarding, it has weaknesses that technical leaders and engineering teams must address. The foundational models trained on Python, TypeScript, and other programming languages often learn from vast amounts of public code, which may not always represent secure or maintainable engineering practices. Some patterns they derive are simply statistical trends and lack a basis in solid software design principles, such as secure design and zero trust. This dependence on potentially flawed data can lead to misunderstandings about best practices, causing developers to adopt insecure or inefficient coding habits without realizing it. As technology changes quickly, relying on outdated or poorly written examples can stifle innovation and weaken the integrity of software projects.

The illusion of safety in noise reduction

Auto-complete features and noise reduction methods in AI coding depend on making patterns in the training data look smoother instead of being based on proven engineering principles. The purpose of these coding solutions is to mitigate friction in the realization of ideas rather than to impose constraints. An unfortunate consequence of this approach is the semblance of correctness: the code appears polished, and the functions seemingly operate as intended; yet, the foundational logic may be flawed, insecure, or incompatible with operational requirements. I draw upon my experience with large enterprises in guiding them toward low-code solutions, and this was a common concern expressed by many of them.

Is the code maintainable?

Although it is improving, vibe-coded software still lacks explainability and rationale. During service outages, particularly when the outage cascades across microservices, third-party dependencies, or cloud infrastructure, it is essential to have more than just syntactically correct code.

You need to have context, contracts, and traceability. Code that is “vibe-coded” into existence often fails the test of operational readiness. Without proper guardrails, you end up with something far worse than legacy software—there, I said it! Legacy software is an example of live software that no one understands and gets really hard to decompose and do anything meaningful.

We are already seeing early signs of this in open-source projects where AI-generated code has proliferated. There are repositories brimming with redundant logic, ambiguous abstractions, and fragile dependencies. In some cases, contributors can’t explain why a block of code exists or what might break if it changes.

Secure Coding and Zero Trust as guardrails are non-negotiable

Now, I am not saying we need to reject AI-generated code; in fact, far from it. The solution is to ground it in the enterprise secure coding principles and zero trust architectures. These should serve as rails, not brakes, on this new mode of development. Enterprises must invest in tooling, policy, and culture that elevate contextual understanding, threat modeling, and least-privilege execution.

The promise of agentic development is real. We will get to a future where intelligent systems reason about business intent, architectural constraints, and security posture before generating code. But we are not there yet. Until then, vibe coding without governance is a fast lane to spaghetti code. Code that looks modern but behaves like legacy.

Let us celebrate the creativity this new medium offers, but let us not confuse vibes with validation!


The layers of Artificial Intelligence

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I have started blogging again, and it feels great to be back! It’s an exciting time to jump in, especially with all the developments in AI (Artificial Intelligence). I am really excited because I believe that during our lives, we will be able to find a cure for cancer and tackle climate change globally with AI.

So, what makes AI systems, like chatbots, recommendation tools, or even autonomous vehicles work? The answer is layers. What do I mean by layers? To make AI work, there are many layers involved. These layers are the hidden heroes behind all the amazing things we can achieve with Artificial Intelligence. Let’s explore what makes them brilliant!

1. The Infrastructure Layer

The infrastructure layer is essential for any AI system, providing the computing power and storage to manage large data and complex tasks. Think of it as the oven and tools needed for baking a cake. Key components include cloud platforms, GPUs, and powerful servers. Without this layer, AI systems lack the strength to operate. Besides the main infrastructure, there are also important aspects like security, compliance, identity, scaling, and backup and recovery timelines.

2. The Data Layer 

Data is the raw ingredient for AI—like the flour, sugar, and eggs for your cake. The data layer involves collecting, storing, and processing data. It ensures that the data is clean, organized, and accessible for further use. Databases, data lakes, and data pipelines play a crucial role in this layer, ensuring your AI system has a steady supply of high-quality “ingredients.” 

3. The Model Layer 

Moving on, the model layer is where the real magic happens. This layer involves training and fine-tuning AI models to perform specific tasks, such as recognizing images, understanding speech, or predicting trends. Think of this as mixing and baking your ingredients into a delicious cake. Machine learning algorithms and frameworks like TensorFlow or PyTorch are the key tools in this layer. 

4. The Orchestration Layer 

This is the conductor of the AI symphony. The orchestration layer ensures that all the other layers work in harmony. It manages workflows, integrates components, and ensures scalability and efficiency. Imagine this as the recipe book and timer that guide you through the baking process. Without orchestration, the entire system can become chaotic and inefficient. 

5. The Application Layer 

Finally, the application layer is where AI meets the real world. This is the beautifully decorated cake that everyone gets to enjoy. It includes user interfaces, APIs, and AI-powered applications, such as chatbots, recommendation systems, or autonomous vehicles. This layer ensures that the end-user can interact with and benefit from the AI system effortlessly. 

Conclusion 

In summary, the layers of AI work together like a well-baked cake, with the infrastructure, data, model, orchestration, and application layers playing their distinct roles. As the orchestration layer brings harmony to the entire stack, it ensures that all components collaborate seamlessly to deliver intelligent and efficient solutions. Understanding these layers is the first step toward appreciating the brilliance behind AI systems! 

What Does Value Really Mean?

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What is Value?

In the realm of product management, “value” is often discussed but rarely defined with the clarity it deserves. At its core, value is the ability to improve and impact the current reality of a customer. It’s not about the prettiness of your deliverables or the meticulousness of your documentation—it’s about creating tangible outcomes that transform how customers experience the world.

The Difference Between Menus and Meals

Imagine dining at a restaurant. The beautifully designed menu teases with promises of delightful dishes. However, if the meal served fails to satisfy or create a memorable experience, the menu’s allure becomes meaningless. Similarly, in product management, gorgeous specifications and polished PowerPoint decks are the menus. They outline possibilities and articulate plans, but they aren’t the meals. The real substance comes from the actual impact these plans have on a customer’s life.

  • Menus (Outputs): These are the deliverables—specifications, designs, presentations—that provide clarity on what is intended.
  • Meals (Outcomes): These are the actual results. They are the improvements, the efficiency gains, and the enhanced experiences that customers enjoy.

If we focus solely on outputs without paying attention to the outcomes, we risk missing the point entirely. Our job is not to create beautiful documents for their own sake, but to drive meaningful change.

From Outputs to Outcomes

Many product managers fall into the trap of equating activity with progress. They become enamored with the creation of extensive documents and polished slides, believing that these outputs signal success. However, without a direct line of sight to the outcomes—the real, positive shifts in customer reality—our efforts may end up being impressive on paper but ineffective in practice.

To truly deliver value, we must:

  • Prioritize Impact: Ask yourself, “How will this feature or specification change the way a customer works, feels, or succeeds?”
  • Measure Outcomes: Track metrics that matter, such as customer satisfaction, engagement, and long-term business benefits. It’s these numbers that truly validate our efforts.
  • Stay Outcome-Oriented: Regularly reassess and pivot based on feedback. Clinging to a document that once promised a breakthrough but no longer aligns with customer needs is a surefire way to lose relevance.

The Role of Clarity in Driving Action

Clarity is the bridge between vision and execution. Wonderful documents and detailed specifications are valuable tools only because they provide clarity. They help align teams, set expectations, and serve as a reference point during the tumultuous journey of product development. But the ultimate goal isn’t to maintain clarity for its own sake—it’s to empower teams to take decisive, informed actions that bring about a new reality for customers.

Consider these strategies to ensure clarity translates into action:

  • Define Clear Objectives: Start every project with a clear statement of the intended impact. What does success look like from the customer’s perspective?
  • Communicate Transparently: Ensure that every stakeholder understands not just what is being built, but why it matters. This shared vision drives collaboration and innovation.
  • Iterate and Improve: Use clarity as a starting point, but always remain open to refining your approach. The landscape changes, and so do customer needs. Iterative feedback loops ensure that clarity evolves into meaningful progress.

Creating a New Reality

Ultimately, value is measured by the transformation it brings about. As product managers, our mission is to turn ideas into actions that reshape customer experiences. It’s about moving from theoretical blueprints to real-world results. Every beautiful specification or compelling slide should be viewed through the lens of the impact it can drive. When we focus on outcomes rather than just outputs, we empower our teams to create products that not only meet expectations but exceed them.

In conclusion, value isn’t just an abstract concept or a metric on a dashboard—it’s the very essence of meaningful innovation. By shifting our focus from outputs to outcomes, and by harnessing the power of clarity to drive action, we can truly make a difference in our customers’ lives. And that, in the end, is what real value is all about.

Rebooting my blog

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After nearly being absent for 2 plus years, I have started blogging again. I will be blogging about my favorite product management topics. I have been with Microsoft for nearly 2 and half years now and I have moved into security space. So stay tuned on future posts from me on this blog.

 

4 Mistakes Marketers Make When Trying to Go “Viral”

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Again what are the outcomes the content developers are trying to drive. That is usually the question