How Planning and Self-Correction Work Under the Hood

Study Guide · Chapter 3: Execution Loop · 7 min read

Underneath every capable agent is a control system: checkpoints that save progress, budgets that cap effort, and escalation paths for failures it cannot fix alone.

Checkpoints make loops resumable

Each completed subtask is committed as a checkpoint — the plan state, tool outputs so far, and remaining steps. If a process crashes or a model call times out, work resumes from the checkpoint instead of starting over.

Checkpoints also enable inspection: reviewers can see exactly which step produced which decision, which turns debugging from archaeology into reading.

Reflection turns failure into signal

When a step fails, well-built agents do not simply retry identically. A reflection step asks: why did this fail — bad input, wrong tool, wrong order, or an impossible request?

The answer changes the strategy: reformat and retry, pick a different tool, reorder the plan, or escalate to a human. Escalation is not failure; it is the loop knowing its own limits.

Budgets prevent runaway loops

Every loop runs under explicit limits: maximum iterations, wall-clock time, token spend, and cost. Hitting any limit triggers a defined behavior — summarize progress, save state, hand off to a person.

This is what separates production agents from demos. Demos assume success; production systems define exactly what happens when the twentieth attempt still fails.

Key Points

Persist plan state at each step so runs are resumable and reviewable.
Classify failures before retrying — identical retries of a doomed step just burn money.
Escalation paths are a feature, not an admission of defeat.
Iteration, time, and cost budgets convert infinite risks into bounded ones.

All study guides for this chapter: Execution Loops, Explained Simply · How Planning and Self-Correction Work Under the Hood · Execution Loops in the Real World · How AI Learns From Trying, Made Simple