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.
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.
