AI agents are no longer limited to suggestion or support. In production environments, they schedule work, trigger actions, update records, and initiate downstream processes. They operate continuously, at speed, and often without direct human supervision. This is not a future scenario. It is already happening. And as agents move into live environments, a quiet constraint is emerging. Not model quality. Not tooling. Identity.
In early deployments, AI agents are usually framed as helpers. They prepare responses. They gather information. They assist a human decision.
But production systems do not stay in that mode for long. At some point, an agent is allowed to act on behalf of someone else.
Approve a step.
Trigger a workflow.
Update a system of record.
That is the mome…
AI agents are no longer limited to suggestion or support. In production environments, they schedule work, trigger actions, update records, and initiate downstream processes. They operate continuously, at speed, and often without direct human supervision. This is not a future scenario. It is already happening. And as agents move into live environments, a quiet constraint is emerging. Not model quality. Not tooling. Identity.
In early deployments, AI agents are usually framed as helpers. They prepare responses. They gather information. They assist a human decision.
But production systems do not stay in that mode for long. At some point, an agent is allowed to act on behalf of someone else.
Approve a step.
Trigger a workflow.
Update a system of record.
That is the moment the problem appears.
Because most enterprise technology stacks were not designed for non-human actors that carry delegated authority.
Enterprise identity frameworks assume a simple model.
There is a user.
That user has a role.
That role grants permissions.
This works when the actor is human. It becomes fragile when the actor is software.
An AI agent does not behave like a user. It does not log in once and perform a bounded set of actions. It operates across systems, over time, often reacting to conditions rather than instructions.
Copying a human role and assigning it to an agent may get a pilot running. It does not survive scale.
In production terms, an AI agent is not a tool.
It is not a feature.
It is not a user.
It is a delegated actor.
It acts on behalf of a principal. Sometimes that principal is a person. Sometimes it is a system. Often it is an organisational function.
Once that is acknowledged, the technical challenge becomes clearer.
The issue is not intelligence.
The issue is authority.
Traditional systems fail in visible ways.
They stop responding.
They throw errors.
They slow down.
Delegated systems fail differently.
They continue to operate.
They produce outcomes.
They follow rules that were technically correct.
When an agent’s authority is unclear, failure does not look like a breakdown. It looks like confusion after the fact.
Four delegation bottlenecks that appear in production
Across early deployments, the same bottlenecks surface repeatedly.
1. Who is the principal?
When an agent takes an action, whose authority is it exercising? A named individual? A team? A policy? Without a clear principal, accountability dissolves quickly.