When one compromised agent brought down our entire 50-agent ML system in minutes, I realized we had a fundamental problem. We were building autonomous AI agents without the basic trust infrastructure that the internet established 40 years ago with DNS.

As a PhD researcher and IEEE Senior Member, and I’ve spent the past year building what I call “DNS for AI agents” — a trust layer that finally gives autonomous AI the security foundation it desperately needs. What started as a research project to solve authentication problems in multi-tenant ML environments has evolved into a production system that’s changing how organizations deploy AI agents at scale.

The transformation from traditional machine learning to agentic AI represents one of the most significant shifts in enterprise technology. While traditional ML pipelines require human oversight at every step — data validation, model training, deployment and monitoring — modern agentic AI systems enable autonomous orchestration of complex workflows involving multiple specialized agents. But with this autonomy comes a critical question: How do we trust these agents?

The cascading failure that changed everything

Let me share what happened in our production environment that crystallized this problem. We were running a multi-tenant ML operations system with 50 agents, handling everything from concept-drift detection to automated model retraining. Each agent had its own responsibility, its own credentials and its own hardcoded endpoints for communicating with other agents.

On a Tuesday morning, a single agent was compromised due to a configuration error. Within six minutes, the entire system collapsed. Why? Because agents had no way to verify each other’s identity. The compromised agent impersonated our model deployment service, causing downstream agents to deploy corrupted models. Our monitoring agent, unable to distinguish legitimate from malicious traffic, dutifully reported everything as normal.

This wasn’t just a technical failure — it was a trust failure. We had built an autonomous system without the fundamental mechanisms for agents to discover, authenticate and verify each other. It was like building a global network without DNS, where every connection relies on hardcoded IP addresses and blind trust.

That incident revealed four critical gaps in how we deploy AI agents today.

1. There’s no uniform discovery mechanism — agents rely on manual configuration and hardcoded endpoints.
2. Cryptographic authentication between agents is virtually nonexistent.
3. Agents can’t prove their capabilities without exposing sensitive implementation details.
4. Governance frameworks for agent behavior are either nonexistent or impossible to enforce consistently.

Building trust from the ground up

The solution we developed, called Agent Name Service (ANS), takes inspiration from how the internet solved a similar problem decades ago. DNS transformed the internet by mapping human-readable names to IP addresses. ANS does something similar for AI agents, but with a crucial addition: it maps agent names to their cryptographic identity, their capabilities and their trust level.

Here’s how it works in practice. Instead of agents communicating through hardcoded endpoints like “http://10.0.1.45:8080,” they use self-describing names like “a2a://concept-drift-detector.drift-detection.research-lab.v2.prod.” This naming convention immediately tells you the protocol (agent-to-agent), the function (drift detection), the provider (research-lab), the version (v2) and the environment (production).

But the real innovation lies beneath this naming layer. We built ANS on three foundational technologies that work together to create comprehensive trust.

1. Decentralized Identifiers (DIDs) give each agent a unique, verifiable identity using W3C standards originally designed for human identity management.
2. Zero-knowledge proofs allow agents to prove they have specific capabilities — like database access or model training permissions — without revealing how they access those resources.
3. Policy-as-code enforcement through Open Policy Agent ensures that security rules and compliance requirements are declarative, version-controlled and automatically enforced.

We designed ANS as a Kubernetes-native system, which was crucial for enterprise adoption. It integrates directly with Kubernetes Custom Resource Definitions, admission controllers and service mesh technologies. This means it works with the cloud-native tools organizations already use, rather than requiring a complete infrastructure overhaul.

The technical implementation leverages what’s called a zero-trust architecture. Every agent interaction requires mutual authentication using mTLS with agent-specific certificates. Unlike traditional service mesh mTLS, which only proves service identity, ANS mTLS includes capability attestation in the certificate extensions. An agent doesn’t just prove “I am agent X” — it proves “I am agent X and I have the verified capability to retrain models.”

From research to production reality

The real validation came when we deployed ANS in production. The results exceeded even our optimistic expectations. Agent deployment time dropped from 2–3 days to under 30 minutes — a 90% reduction. What used to require manual configuration, security reviews, certificate provisioning and network setup now happens automatically through a GitOps pipeline.

Even more impressive was the deployment success rate. Our traditional approach had a 65% success rate, with 35% of deployments requiring manual intervention to fix configuration errors. With ANS, we achieved 100% deployment success with automated rollback capability. Every deployment either succeeds completely or rolls back cleanly — no partial deployments, no configuration drift, no manual cleanup.

The performance metrics tell an equally compelling story. Service response times average under 10 milliseconds, which is fast enough for real-time agent orchestration while maintaining cryptographic security. We’ve successfully tested the system with over 10,000 concurrent agents, demonstrating that it scales far beyond typical enterprise needs.

ANS in action

Let me share a concrete example of how this works. We have a concept-drift detection workflow that illustrates the power of trusted agent communication. When our drift detector agent notices a 15% performance degradation in a production model, it uses ANS to discover the model retrainer agent by capability — not by hardcoded address. The drift detector then proves it has the capability to trigger retraining using a zero-knowledge proof. An OPA policy validates the request against governance rules. The retrainer executes the update and a notification agent alerts the team via Slack.

This entire workflow — discovery, authentication, authorization, execution and notification — happens in under 30 seconds. It’s 100% secure, fully audited and happens without any human intervention. Most importantly, every agent in the chain can verify the identity and capabilities of the others.

Lessons learned and the path forward

Building ANS taught me several lessons about deploying autonomous AI systems. First, security can’t be an afterthought. You can’t bolt trust onto an agent system later — it must be foundational. Second, standards matter. By supporting multiple agent communication protocols (Google’s A2A, Anthropic’s MCP and IBM’s ACP), we ensured ANS works across the fragmented agent ecosystem. Third, automation is non-negotiable. Manual processes simply can’t scale to the thousands of agents that enterprises will be running.

The broader implications extend beyond just ML operations. As organizations move toward autonomous AI agents handling everything from customer service to infrastructure management, the trust problem becomes existential. An autonomous system without proper trust mechanisms is a liability, not an asset.

We’ve seen this pattern before in technology evolution. In the early internet, we learned that security through obscurity doesn’t work. With cloud computing, we learned that perimeter security isn’t enough. Now, with agentic AI, we’re learning that autonomous systems require comprehensive trust frameworks.

The open-source implementation we’ve released includes everything needed to deploy ANS in production: the core library, Kubernetes manifests, demo agents, OPA policies and monitoring configurations. We’ve also published the complete technical presentation from MLOps World 2025 where I demonstrated the system live.

What this means for enterprise AI strategy

If you’re deploying AI agents in your organization — and recent surveys suggest most enterprises are — you need to ask yourself some hard questions.

• How do your agents authenticate with each other?
• Can they verify capabilities without exposing credentials?
• Do you have automated policy enforcement?
• Can you audit agent interactions?

If you can’t answer these questions confidently, you’re building on a foundation of trust assumptions rather than cryptographic guarantees. And as our cascading failure demonstrated, those assumptions will eventually fail you.

The good news is that this problem is solvable. We don’t need to wait for vendors or standards bodies. The technologies exist today: DIDs for identity, zero-knowledge proofs for capability attestation, OPA for governance and Kubernetes for orchestration. What was missing was a unified framework that brings them together specifically for AI agents.

The shift to autonomous AI is inevitable. The only question is whether we’ll build these systems with proper trust infrastructure from the start or whether we’ll wait for a major incident to force our hand. Based on my experience, I strongly recommend the former.

The future of AI is agentic. The future of agentic AI must be secure. ANS provides the trust layer that makes both possible.

The complete Agent Name Service implementation, including source code, deployment configurations and documentation, is available at github.com/akshaymittal143/ans-live-demo. A technical presentation demonstrating the system is available at MLOps World 2025.

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