Designing Your Own Loop

Building an autonomous loop from scratch can feel overwhelming. Use a simple, repeatable sequence: define a narrow goal, verify it with a concrete scorekeeper, get one small saved win, then extend only as real needs appear. This approach reduces risk, speeds iteration, and keeps costs under control.

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Step one — Write the goal and the scorekeeper

Make the goal small, specific, and finishable — not a long wishlist. Then define the scorekeeper: the exact check (pass/fail criteria) that proves the goal is met. Write the goal and scorekeeper before any code or automation. The goal guides the design; the scorekeeper validates it.

• Goal: one focused outcome the loop should produce.
• Scorekeeper: an objective test that returns a clear pass or fail.

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Step two — Start the loop and get one small win saved

Follow the “green-first” rule: implement just enough for the loop to produce one real result that passes the scorekeeper and is persisted (saved). That initial green proves the loop runs end-to-end and gives a stable baseline to iterate from.

• Implement the minimal path from input → process → score → persist.
• Persisting the first successful result prevents regression and makes refactoring safe.
• Treat this as a thin vertical slice, not a full product.

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Step three — Add parts only as they are needed

Extend the loop incrementally and only to solve observed problems. Common, justified additions include:

• A skill/guard to enforce the correct step when the loop repeatedly makes the same mistake.
• Durable memory when runs grow long or need to pause/resume across sessions.
• A focused sub-agent when a particular job requires concentrated effort (large search, deep review).

Avoid bolting on features “just in case.” Let real failure modes drive changes.

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Cost, human handoffs, and when not to build a loop

Be mindful of token and compute costs. Each run consumes resources and may spawn sub-agents; frequent or fast runs increase expense. Build a loop only when:

• Work repeats often enough to amortize costs, and
• A scorekeeper can reliably validate outcomes.

For one-off tasks, a well-crafted prompt may be simpler and cheaper. Decide up front when the loop should pause and ask a human: blockers it cannot clear, risky or irreversible actions, or decisions requiring judgment are all good reasons to hand off. A loop that asks for human confirmation at the right time is safer than one that proceeds unchecked.

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Common mistakes and traps — how to detect and fix them

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A habit that ties it all together

Keep every change small and saved. Small, incremental changes are easy to test, reason about, and revert. This safety net allows you to run a bolder loop without undue risk.

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Quick recap — Design order and checklist

Checklist:

• Is the goal narrow and finishable?
• Is the scorekeeper objective and difficult to game?
• Does the loop produce one saved green result?
• Are new parts added only to fix observed failures?
• Have you evaluated token cost and human handoff points?
• Do you limit scope and gate risky actions?

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Further reading

• Prompt engineering best practices
• Designing safe agent systems
• Practical automation: when to script vs. when to build a loop

Keep loops small, testable, and auditable — and add complexity only in response to real, observed needs.