Study Guide · Chapter 3: Execution Loop · 6 min read
Execution Loops in the Real World
Loops power the agents that feel genuinely useful — coding assistants, incident responders, research analysts. Here is what they look like deployed, and the failure modes to expect.
An incident-triage agent at work
When monitoring raises an alert, a triage agent plans: pull recent deploys, check error rates by service, run a query on affected accounts, and draft a diagnosis. Each step observes reality and may reshape the plan.
The loop shines because incidents are non-linear. Evidence found in step two — say, a deploy that correlates with the spike — sends the investigation down a path no fixed script would have predicted.
Failure modes you will meet
The classic pathology is oscillation: the agent alternates between two failing approaches forever. Detection is straightforward with loop budgets; prevention usually requires forcing an explicit reflection step after repeated failures.
Another is premature completion — declaring victory while tests still fail. Requiring objective evidence (passing test output, confirmed API responses) before 'done' closes most of that gap.
Making loops observable from day one
Log every iteration's reasoning summary, action, and observation. When quality drops, these traces show whether planning, tools, or feedback signals degraded.
Teams that treat loop traces as first-class data iterate dramatically faster than those debugging agents from final outputs alone.
Key Points
- Dynamic tasks — incidents, research, coding — benefit most from adaptive loops.
- Oscillation and premature completion are the two failure modes to instrument for.
- Require objective evidence of success before an agent declares done.
- Loop traces are your richest debugging asset — capture them from the start.
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