Feature Focus Series: Agentic AI Observability

Agentic AI governance starts with discovery. Before an organization can control what AI agents access, do, retrieve, or expose, it needs visibility into which agents exist, where they are running, and what systems they connect to.

In multi-cloud environments, agents are often deployed independently across platforms, teams, and business units. Without unified discovery, each platform can become a blind spot. A new agent may be created, connected to internal systems, and begin operating before security, IAM, or governance teams know it exists.

That is why discovery is the first step toward authorization. Once agents are visible, they can be classified, connected to policy, and governed at the moment access happens.

 

Consider this situation in your organization.

Your security team has deployed an AI governance policy. Agents are registered. Guardrails are in place. The policy covers everything in scope.
Then a developer in another business unit spins up a new agent in AWS. It is connected to internal systems. It starts operating. And no one outside that team knows it exists.
That gap is where agentic AI governance can break down.
This is the new business reality. The question is not whether new agents will appear. The question is whether your authorization layer sees them when they do.
To govern AI agents, organizations first need to discover them, understand their context, and bring them into a central view where authorization policies can be applied consistently.

Discover Agents Across Platforms

Governance starts with knowing what exists. PlainID connects directly to enterprise agent platforms, AWS AgentCore and Microsoft Foundry among them, and surfaces every discovered agent, along with its associated gateways and targets, into a single unified agent registry and access graph.

See how PlainID connects to AWS AgentCore and Microsoft Foundry to build a unified view of agents, gateways, and targets across your enterprise environment.

Turn Agent Metadata into Authorization Policy 

Knowing an agent exists is necessary. It is not sufficient.

What line of business does this agent serve? That single attribute can become a live policy condition.

PlainID allows teams to extend agent metadata with custom attributes that carry direct policy weight. Once an agent is tagged, it is automatically grouped with others sharing the same attribute and governed accordingly, evaluated in real time at the moment of access.
This is not hardcoded, per-agent configuration, but a dynamic policy built on business context.

See how PlainID extends agent metadata with custom business attributes and uses them to build dynamic policy groups.

Keep Authorization Current with Continuous Discovery 

An inventory that is not being continuously updated becomes a potential attack surface.

In an agentic AI environment, new agents can be created after policies are written. Existing agents can change. Platform registries can update. If discovery does not stay current, governance and authorization may no longer reflect what is actually running in the environment.
PlainID’s discovery can be triggered manually or run on an automated schedule through connected platforms. New agents are detected, their native metadata pulled from the source, and they enter the registry under centralized visibility.

See how PlainID detects a newly deployed AWS agent, pulls its native metadata, and updates the agent registry automatically on the next scheduled scan.

The Governance Outcome

PlainID allows organizations to safely scale agentic AI and allow employees to utilize agents from different platforms, bringing them into an aggregated view and extending their metadata to be used as access policy building blocks.

The result is displayed in a single unified organizational access graph: every identity involved in the agentic flow, agents included, their attributes, related policies and relations to assets, all visible in a single view.