AI and agents · Architecture and operations · 5 min

Loop engineering: reliable cycles for AI agents

An explicit cycle of plan, action and evaluation keeps autonomy from turning into unproductive repetition.

Loop engineeringAutonomyEvaluation

Autonomy needs an exit

A reliable agent goes through a controlled cycle: it receives evidence, forms a plan, runs a permitted action and evaluates whether that action reduced uncertainty. The goal is not to generate activity but verifiable progress.

The design must account for success, insufficient information, repeated errors and an exhausted budget. Each condition leads to a different exit: finish, ask for context, change strategy or escalate to a person.

Plan
Act + evaluate
Finish or escalate
Step budget · time · cost

Measure progress instead of counting iterations

A step limit is necessary but not sufficient. It is also worth detecting identical calls, the absence of new data and results that do not meet acceptance criteria.

These signals turn the loop into an operable unit: bounded, auditable and aligned with the quality the business process expects.

Reading the diagram

How to read this system

  1. 01The input expresses a goal or new information; the agent forms a hypothesis and a next action.
  2. 02The environment returns evidence, not just text. Evaluation checks completeness, authorization, quality and budget.
  3. 03If the goal is verified, the flow stops; in the face of contradiction, missing evidence or persistent error, it escalates.
  4. 04Returning to the agent is only justified when there is new information or a different strategy.

Essential concepts

Technical terms

Loop engineering
Explicit design of plan, action and evaluation.
Acceptance criterion
Verifiable condition of sufficiency.
Stagnation
Repetition without progress or new evidence.

Architecture

Implications and controls

The evaluator must receive structured state and evidence, not rely on the agent’s self-declaration. Persisting the start, execute, evaluate, finish and escalate states makes the cycle auditable.

Risks to consider

  • Infinite loops or repeated calls.
  • Cost and time without a budget.
  • False completion with incomplete evidence.

Checklist

  • Set limits on steps, time and tool use.
  • Record the reason for each transition.
  • Detect identical calls without new context.
  • Test ambiguity, errors and budget exhaustion.

Conclusion

A reliable cycle does not maximize iterations: it maximizes justifiable decisions within defined limits.