Loop engineering is the operational side of agent work. Instead of asking only “what prompt should I use?”, you design the cycle: goal, action, check, feedback, retry, and stop. That loop is often more important than the wording of the prompt itself.
Why loops matter
An agent without a loop either stops too early or keeps going too long. Good loops help the system recover from mistakes, ask for clarification at the right time, and stop when the task is done. Bad loops create expensive wandering.
The basic loop
- Give the agent a concrete goal and a visible definition of done.
- Let it take one or more actions with the right tools.
- Check the result against rules, tests, or quality criteria.
- Decide whether to stop, revise, escalate, or try again.
- Record what happened so a human can review the chain.
Examples by workflow
Coding
A coding loop usually looks like: inspect repo, propose plan, edit files, run typecheck or tests, fix failures, then hand back a diff. The loop becomes unsafe when the model is allowed to keep retrying indefinitely or keep rewriting code to silence checks without understanding the failure.
Research
A research loop should gather sources, compare them, discard weak ones, identify contradictions, and only then draft a summary. Without that review step, you get confident summaries built on low-quality material.
Content
For content work, a loop might outline, draft, fact-check, tighten, and route for editorial review. This is much safer than one-shot generation, especially when the article includes factual claims.
Admin and sales ops
For admin work, loops shine in list processing: read a record, classify it, prepare an update, validate required fields, and only then write back. Sales ops benefits from the same pattern when cleaning CRM data or drafting follow-ups from structured notes.
Why loops get expensive
Every extra pass can mean another model call, another tool call, another search, and another chunk of context. If the system keeps replaying a huge prompt or calling the same tools again and again, your bill climbs quickly without improving the result.
- Cap retries for each stage.
- Cache stable instructions and tool definitions where supported.
- Use smaller models for validators, formatters, and classifiers.
- Prune context so the agent does not keep dragging old state forward.
- Escalate to a human instead of forcing infinite self-repair.
Most agent failures are not model failures. They are loop design failures.
How Dera sees it
Dera is skeptical of complexity for its own sake. The best loop is the lightest one that finishes the job reliably. If a task can be solved with one grounded model call and a human review, that is usually better than a flashy multi-agent maze.