In 2024, the tech world was obsessed with building autonomous agents. An engineer writes an instruction, starts an agent, reads the result, notices what went wrong, and writes the next instruction. That workflow is useful while exploring a problem. It does not scale when the same class of work arrives every day.
The moment agent work repeats — CI failures, support tickets, stale pull requests, or monitoring alerts — you are no longer designing a single response. You are designing a control system.
An agent can report “done” while the test is still red. It can retry the same failed action, consume its budget, and leave behind no durable explanation of what happened. In production, those are loop failures — not merely prompt failures.
Most agent systems are still designed one prompt at a time. We add instructions like “think step-by-step,” or “double-check your work.”
The following layers are complementary, but distinct:
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Prompt engineering: What should the model do in this turn?
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Context engineering: What should the model see?
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Harness engineering: How should one run execute safely?
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Loop engineering: How should repeated work move toward a verified outcome?
Prompt engineering only optimizes reasoning. It does absolutely nothing for execution. A well-designed loop still needs good prompts and useful context, and a harness still needs to constrain each run. But loop engineering adds the outer control system that makes the work repeatable and prevents infinite, unverified spinning.
We are undergoing a massive architectural shift: moving from conversational interfaces to autonomous control systems.
The distinction matters because more turns do not automatically mean more progress. An agent can repeat the same action, accumulate irrelevant context, consume its budget, and still report success without producing a verified result.
When you deploy an autonomous agent, you are removing the human loop. You must replace it with a strict, engineered control loop that manages its state, enforces its boundaries, and validates its work.
**Loop Engineering **is the practice of designing repeatable agent workflows that discover work, hand an agent a bounded task, observe the result, verify it with evidence, persist what happened, and decide whether to continue, stop, or escalate.
It decides when the agent should wake up, what work it should receive, what evidence counts as progress, where the result is recorded, and what happens when the work cannot be completed. The model is an important component of the loop, but it is not the whole system.
A reliable loop is a small state machine, even when the implementation uses an agent framework. One useful model is the seven-stage core loop:
1. Discover
Identify new, in-scope work and suppress duplicates. A loop needs a defined source of work (a CI failure, a support ticket) with a stable identity so it doesn’t create a new task every time it sees the same unchanged failure.
2. Handoff
Convert an event into a bounded task. Define the goal, evidence, permissions, acceptance criteria, working location, and stop rule. Without this contract, “fix the failure” becomes permission to redesign the system.
3. Execute
Let the agent act inside an explicit boundary. Isolation is part of the loop contract — whether that’s a disposable worktree, a branch, a container, or a sandbox. The loop should make the boundary visible rather than relying on the model to respect it. 4. Observe
Capture tool-produced evidence, not just the agent’s narration. Useful observations include test output, compiler errors, changed-file lists, API responses, and diff summaries. “The change looks good” is not an observation.
5. Verify
Check the resulting artifact independently. The agent that made a change should not be the only component deciding that the change is correct. Use unit tests, type checking, or a second evaluator with a separate rubric. The verification result should be structured JSON, not a free-form paragraph.
6. Persist
Make the next run start with state. Record the attempt, evidence, decisions, and unresolved risks. The model context is not a durable database. A loop should remember what affects the next decision, not accumulate an ever-growing transcript.
7. Decide
Retry only with new evidence; otherwise stop, escalate, or close. “Keep trying until it works” is not a stopping rule. Retries should be bounded. Human intervention belongs in the design from the beginning.
Case Study: Building a Reliable CI Failure Loop
Consider a loop that helps maintain a software repository by fixing CI failures.
The Naive Version:
An agent receives a failing CI notification and is told to fix it. It searches broadly, edits several files, runs one test, sees a failure, and tries again. After a few turns it reports that the issue is fixed. A human discovers that the original check never passed on the final commit. The loop had no stable work identity, no change boundary, and no independent completion state.
The Engineered Version:
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**Discover: **A webhook records the repository, commit, workflow, and normalized failure signature.
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Handoff: The agent receives the failure log, a disposable worktree, allowed commands, and a two-attempt budget.
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Execute: The agent investigates and proposes a minimal patch, editing only allowed files.
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Observe: The loop records the diff, test output, exit codes, and elapsed time.
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Verify: A fresh test run executes against the resulting commit independently of the agent’s summary.
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Persist: The work item stores the commit, evidence bundle, and attempt number.
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Decide: If checks pass, it prepares a pull request. If they fail with actionable new evidence, it retries. If the budget is exhausted, it stops as needs_review.
Common Failure Modes
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**The loop has no real trigger: **If a person still has to restart the task every time, the system is an agent session, not a loop.
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The loop confuses a claim with evidence: “Done” and “tested” are claims. They must be backed by current evidence.
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The loop retries without changing the information state: Rewording the same instruction does not create progress. Detect repeated observations and escalate.
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The evaluator shares the generator’s blind spot: A second model with the same context may repeat the first model’s mistake. Add deterministic checks.
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The loop silently expands its scope: Enforce file, tool, and time boundaries.
Practical Checklist
Before automating a recurring agent workflow, answer these questions:
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[ ] What exact source discovers the work, and how are duplicates identified?
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[ ] What is the smallest bounded task the agent can receive?
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[ ] Which files, tools, systems, and actions are in scope?
- [ ] What external state survives the run?
- [ ] What evidence proves progress and completion?
- [ ] How many attempts, tokens, and minutes are allowed?
- [ ] What happens when the loop is blocked or reaches a terminal state?
Conclusion
Loop engineering is not the replacement for prompt engineering, context engineering, or harness engineering. It is the discipline that connects them across repeated work.
The goal is not an agent that never makes a mistake. The goal is a system that notices what happened, gathers new evidence, limits its authority, records its state, and knows when to stop.
Design the trigger. Bound the handoff. Verify the artifact. Persist the evidence. Escalate before the risk becomes irreversible. That is how an agent workflow becomes an engineered loop.
Read the original deep-dive on [[Loop Engineering]] on my engineering blog.
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Loop Engineering: The Anatomy of Reliable Agentic AI was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.