Observability narrows enterprise AI agent authority gap
A contributed analysis urges firms to use continuous observability as a real-time authority engine to govern enterprise AI agents by evaluating delegators’ identity, intent and context.
Orchid, in a contributed analysis, argues continuous observability should act as a real-time authority engine to govern enterprise AI agents by evaluating the identities, intent and context of the delegators.
The paper describes AI agents as delegated actors whose authority comes from human users, machine identities, bots, service accounts and other nonhuman actors. It says agents do not have independent authority and operate with permissions granted by those delegators.
Traditional identity and access management systems focus on who has access. The analysis says those systems do not record what authority is being delegated, by whom, under what conditions, for what purpose or across what scope. That gap can allow agents to inherit hidden privileges and execution paths.
The authors identify a fragmented identity estate they call “identity dark matter”—credentials and permissions embedded across applications, APIs and unmanaged service accounts outside centralized IAM. They recommend discovering and observing all human and machine identities, documenting how they authenticate, where credentials are stored and how workflows execute.
Telemetry from that discovery should feed a live authority engine that continuously evaluates whether an agent should act and at what level. The engine would assess delegator posture, the intent behind requested actions, the target application context and the effective scope of execution. Enforcement would be dynamic: allow an action, limit the agent to recommendations, restrict available tools, or block the agent.
The paper gives examples: a human delegator with risky behavior or excessive hidden access should receive different agent authority than a tightly governed user in a controlled workflow; a broadly privileged service account should not trigger an agent with unconstrained downstream capabilities.
The paper states, “Authority that exists, operates, and often accumulates risk outside the view of managed IAM” should be brought into continuous view. It describes mapping each agent identity to the applications and workflows it can touch and using live telemetry to make enforcement decisions.
The analysis recommends a sequence for governance: first reduce identity dark matter across traditional actors, then deploy continuous observability and a real-time delegation authority layer that evaluates delegators as the primary input into agent decision making.
