Agent Control Loop

Mental model

An agent is not a long prompt. It is a state machine that repeatedly observes an environment, selects an allowed action, executes through a controlled boundary, verifies the result, and either continues, stops, or escalates.

Theory

The application owns the loop. It constructs context, exposes a bounded tool set, validates the model's proposed action, applies authorization, records the result, and decides whether another iteration is allowed. The model contributes judgment under uncertainty; it must not become the source of truth for permissions, completion, or side effects.

Every loop needs explicit budgets: maximum steps, elapsed time, model spend, tool retries, data volume, and consequence. Stopping conditions include a verified goal, an unrecoverable error, exhausted budget, repeated state, policy refusal, or a human checkpoint. Verification should inspect environment state or deterministic tool output rather than trusting a narrative claim that the task is complete.

Alternatives and trade-offs

A fixed workflow is easier to test and should be preferred when steps are known. An agent earns its additional latency, cost, and risk only when the path cannot be specified in advance and environmental feedback changes the next useful action. Hybrid systems use deterministic outer workflows with small agentic regions.

Failure modes and misconceptions

Unbounded retries compound cost and error. Feeding every observation back into context creates noise and attack surface. A model choosing a tool does not authorize that tool. A final answer is not evidence of task completion. Multi-agent designs multiply coordination and observability demands rather than automatically improving quality.

Knowledge check

Decision scenario

A purchasing agent may search approved vendors and draft an order. The application limits it to eight steps, read-only search tools, a fixed spend estimate, and a verified quote. Submission is a separate authorized action requiring a human approval token.

Learning outcomes

• Explain Agent Control Loop as a system mechanism rather than a slogan.
• Compare its alternatives, trade-offs, and production failure modes.
• Apply the concept to a decision and identify evidence that would validate it.

Trade-offs

Using Agent Control Loop can improve capability or control, but it also introduces cost, latency, complexity, and failure modes that must be measured against an explicit objective.