Akshay Mittal
PhD Researcher • IEEE Senior Member
MLOps World | GenAI Summit 2025 • Austin, Texas
Note: Good morning everyone! I'm excited to be here at MLOps World in Austin - my home city. I'm Akshay Mittal, an IEEE Senior Member and PhD researcher focusing on cloud-native AI security. Today I'll be presenting key results from my ongoing dissertation research on solving the critical gap in agent trust infrastructure for AI systems.
Traditional ML Pipeline:
Data → Train → Deploy → Monitor
👤 Human-supervised at every step
Agentic AI Reality:
🤖 Concept Drift Detection Agent → 🤖 Auto-Retraining Agent
→ 🤖 Deployment Agent → 🤖 Monitoring Agent
Note: We're witnessing a fundamental shift in how we architect MLOps workflows. My research focuses on the agentic transition—where autonomous systems manage entire lifecycles and coordinate amongst themselves. But when orchestration is agent-driven, a new trust gap emerges: how do we securely identify, discover, and authorize these agents?
Note: My dissertation work has quantified how trust breakdowns trigger cascading failures in cloud-native environments. Existing identity solutions don't solve autonomous workflows where systems spawn and destroy agents dynamically. We need an agent-native, cryptographically verifiable trust layer.
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Domain Names → IP Addresses
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Agent Names → Verified Capabilities + Trust
Note: DNS solved internet scalability by managing names; ANS solves agent ecosystem scalability by managing trust. The difference: ANS adds cryptographic verification, capability attestation, and governance support for agents.
protocol://AgentID.Capability.Provider.v[Version].Extension
a2a://alerter.security-monitoring.research-lab.v2.prod
mcp://validator.concept-drift-detection.ml-platform.v1.hipaa
acp://remediator.helm-deployment-fix.devsecops-team.v3.staging
Note: ANS borrows DNS hierarchy but adds semantic meaning. Each component tells us something critical: what protocol, what the agent does, who provides it, what version, and what environment. This enables intelligent routing - agents can discover exactly what they need.
Note: PKI provides the cryptographic foundation for agent trust. Every agent gets a unique cryptographic identity. Verifiable credentials prove what an agent can actually do. Like mTLS for microservices, but capability-aware.
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Agent → "I can access sensitive database"
Verifier → "Show me your database password"
❌ Secrets exposed during verification
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Agent → "I can prove I have database access
without revealing credentials"
Verifier → "Prove it cryptographically"
✅ Capability verified, secrets protected
Note: Zero-knowledge proofs are game-changing for agent security. Agents can prove capabilities without revealing sensitive credentials. Critical for enterprise systems - we can verify system access without exposing keys. This enables secure capability delegation between agents.
Note: ANS doesn't lock you into one communication protocol. We support all major emerging standards - A2A, MCP, ACP. This is critical as the agent ecosystem is still evolving. Organizations can migrate protocols without rebuilding their entire agent infrastructure.
Register → Validate → Deploy → Authenticate → Monitor → Rotate
Note: ANS is built Kubernetes-native from the ground up. CRDs define agent metadata and capabilities declaratively. Admission controllers enforce policy before agents start. Service mesh provides transport security while ANS handles capability trust.
Note: GitOps ensures agent deployments are declarative and auditable. Every agent deployment goes through policy validation. Sigstore provides keyless signing for supply chain security. Failed policy checks prevent dangerous agents from starting.
# Only certified agents can access production data
allow {
input.agent.certificate.issuer == "research-lab-trusted-ca"
input.agent.capabilities["data-access"] == true
input.environment == "production"
input.agent.security_clearance >= 3
}
Note: OPA provides fine-grained, auditable policy enforcement. Policies are version-controlled and tested like application code. We can enforce compliance requirements at the platform level. Dynamic policies adapt to changing security requirements.
Note: Let's see ANS in action with a real production scenario. I've prepared a live environment that mirrors our research setup. We'll simulate a concept drift detection scenario that shows how agents discover, authenticate, and orchestrate securely.
kubectl apply -f concept-drift-agent.yaml
# Watch: Real-time registration in ANS registry
Drift detected → Auto-retrainer triggered → Notifications sent
Complete workflow: <30 seconds
Note: [LIVE DEMO SECTION - 3 minutes] Watch how quickly agents discover and authenticate each other. Notice the automatic policy enforcement - no manual approval needed. The entire workflow completes in under 30 seconds with full cryptographic verification at each step.
Note: These metrics come from our research testbed and academic collaborations. 50ms authentication latency enables real-time agent orchestration. 95% reduction in misconfigurations translates to more reliable autonomous systems. Zero downtime certificate rotation ensures continuous operations.
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Note: These patterns demonstrate autonomous agent coordination enabled by ANS trust infrastructure. The concept drift pattern has shown 40% faster response times in our research. Security remediation eliminates human error while maintaining audit trails.
Note: You can start small and scale incrementally. Phase 1 can be completed in a weekend with our open-source tools. By week 8, you'll have production-grade agent security that scales with your AI infrastructure.
github.com/ruvnet/Agent-Name-Service
Note: Everything demonstrated today is open source and available for research and production use. The OWASP collaboration ensures security standards are community-driven. Active research community contributing to standards, implementations, and best practices.
Note: ANS addresses real security gaps in autonomous AI systems that will become critical as agent adoption accelerates. The research provides formal foundations while the open-source implementation enables immediate practical application.
Akshay Mittal
PhD Researcher • IEEE Senior Member
📧 Research Contact: akshay.mittal@research.edu
💼 Professional Network: linkedin.com/in/akshaymittal143
🐙 Open Source: github.com/akshaykokane
🗨️ Community: #ans-community (MLOps World Slack)
📚 Research: IEEE Senior Member Profile
Note: Thank you for your attention! I'm excited to answer your questions about the research, implementation details, security implications, or potential collaborations. Whether it's technical architecture, performance benchmarks, or research methodology - let's discuss how ANS can advance secure agent ecosystems.