What Is an Agent?
An agent is more than a model thinking through an answer. Agency begins the moment reasoning starts controlling actions — reading the results, and deciding what to do next in pursuit of a goal.
It can start with a single, bounded tool call and grow into hours of independent work across real systems and real data — with permissions and checkpoints shaping every step along the way.
The model works through alternatives and constraints before answering.
Still just an answer.The model searches, calculates, or takes one bounded action.
Capability reaches past the answer.A defined sequence coordinates AI, rules, checks, and handoffs.
The path is mostly fixed in advance.The model chooses its own actions, watches the results, and adjusts toward a goal.
It helps decide what happens next.Several agents take distinct roles and coordinate or check each other's work.
Coordination adds capability — and complexity.The key distinction
Reasoning by itself stays inside the answer. An agent uses that same reasoning to decide what to do next in the world — inside whatever tools, permissions, checkpoints, and stopping rules it's been given.
A fixed workflow might always run the same three validation checks, in the same order, on a data extract. An agent asked to reconcile that same extract against a source system can decide for itself which records to re-check and when the discrepancies are worth flagging — a meaningfully different risk profile, and a different governance conversation.
Anatomy of an agent
What outcome is it actually pursuing?
A vague goal produces an agent that solves the wrong problem efficiently. The clearer the definition of success, the easier everything below is to govern.
What's doing the reasoning and choosing?
The model's own capabilities and blind spots set the ceiling for everything built around it — no amount of tooling fully compensates for a model that reasons poorly on your specific domain.
What can it read, calculate, or change?
This is where the real risk profile gets decided. Read-only access to a report is a very different conversation than the ability to update a live record.
What's explicitly off-limits?
Permissions, approval gates, and rate limits that hold even when the agent's own judgment points somewhere else.
Where does a person have to check in?
The checkpoints that catch a mistake before it reaches a patient record, a public report, or a signed contract — placed where errors are expensive or hard to reverse.
Evaluating an agentic AI proposal?
IMC holds no allegiance to any AI platform or vendor, so the read you get on how much autonomy a proposal is actually asking for is independent, and grounded in what your organization can safely govern.
Start a conversation