ANS in motion
Let me share a concrete instance of how this works. We have a concept-drift detection workflow that illustrates the facility of trusted agent communication. When our drift detector agent notices a 15% efficiency degradation in a manufacturing mannequin, it makes use of ANS to find the mannequin retrainer agent by functionality — not by hardcoded tackle. The drift detector then proves it has the potential to set off retraining utilizing a zero-knowledge proof. An OPA coverage validates the request in opposition to governance guidelines. The retrainer executes the replace and a notification agent alerts the crew through Slack.
This total workflow — discovery, authentication, authorization, execution and notification — occurs in underneath 30 seconds. It’s 100% safe, absolutely audited and occurs with none human intervention. Most importantly, each agent within the chain can confirm the identification and capabilities of the others.
Building ANS taught me a number of classes about deploying autonomous AI programs. First, safety can’t be an afterthought. You can’t bolt belief onto an agent system later — it have to be foundational. Second, requirements matter. By supporting a number of agent communication protocols (Google’s A2A, Anthropic’s MCP and IBM’s ACP), we ensured ANS works throughout the fragmented agent ecosystem. Third, automation is non-negotiable. Manual processes merely can’t scale to the hundreds of brokers that enterprises might be working.
