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AI Agents & Autonomous Systems

Reliable Agent Control Loops

Build agents that can recover from tool failures, bad observations, loops, partial progress, and unclear goals

32 min read· agents· reliability· control loops· tool failures

Reliable Agent Control Loops

An agent is a control loop:

text
goal -> plan -> act -> observe -> update state -> repeat or stop

Reliable agents make this loop explicit and bounded.

The minimum loop state

Track:

  • original goal
  • current plan
  • completed steps
  • pending steps
  • tool results
  • errors
  • budget remaining
  • confidence
  • stop condition

Common failure modes

FailureSymptomFix
tool loopcalls same tool repeatedlymax call count and state diff checks
bad observationtrusts broken tool outputvalidate tool results
goal driftsolves a different taskkeep objective visible
over-agencytakes risky actionspermission gates
under-specificationguesses missing inputsask or escalate
partial success hiddenclaims done too earlyverification step

Recovery pattern

When an action fails:

text
1. classify the failure
2. decide if retry is safe
3. try a different tool or plan
4. preserve useful partial work
5. escalate if repeated failures happen

Budgeting

Every agent should have budgets:

  • max turns
  • max tool calls
  • max wall-clock time
  • max cost
  • max file changes
  • max external actions

Budgets are not just cost controls. They prevent runaway behavior.

Verification

Before an agent says "done," require evidence:

  • tests passed
  • file exists
  • API returned success
  • answer cites sources
  • human approved action
  • diff matches expected scope

Knowledge check

Q1: Why should an agent track budget?

To prevent infinite loops, surprise cost, and unsafe repeated actions.

Q2: What should happen before an agent claims completion?

It should verify the expected outcome with evidence.