What is loop engineering and how do you build agent loops?

Loop engineering is the practice of designing the plan-act-observe cycle an agent runs on, instead of crafting single prompts. You define how the agent plans a step, acts, observes the result, and self-corrects on the next pass. The prompt becomes one small part of a repeating, stateful control loop.

Why prompting stopped being the job

A prompt is a single request and a single response. It works when the task is one shot: summarize this, classify that. It breaks the moment the task needs multiple dependent steps, tool calls, or a check against reality. Real work is rarely one shot. Fixing a bug means reading files, forming a hypothesis, editing, running tests, and reacting to the failure. No prompt captures that. A loop does.

The people building serious coding agents stopped optimizing the wording of a request and started optimizing the cycle the agent runs. The prompt still exists, but it is now one input to a repeating process. The skill shifted from phrasing to control flow: what the agent does after it sees the result of its last action.

The anatomy of an agent loop

Every reliable agent loop has the same skeleton. Learn it once and you can build one in any framework or none.

Plan

The agent decides the next single step toward the goal. Not the whole plan, just the next action. Planning one step at a time keeps the loop responsive to what actually happened, instead of committing to a stale plan made before it saw any real output.

Act

The agent invokes a tool: read a file, run a query, call an API, execute a test. The action is concrete and produces an observable result. Tools are the loop's hands. Without them the agent is just talking to itself.

Observe

The result comes back: a test passes, a file is empty, an API returns an error. This is the signal that makes the loop intelligent. A prompt never gets to see the consequence of its own output. A loop does, and that is where self-correction comes from.

Decide

Given the observation, the agent chooses: continue to the next step, retry a different way, or stop because the goal is met or unreachable. This branch is the part most beginners skip, and it is the part that separates an agent that finishes from one that spins forever.

Self-correction is a loop property, not a prompt trick

You cannot prompt a model into reliably catching its own errors in a single pass. It has nothing to check against. Self-correction only appears when the loop feeds a real observation back in. The agent writes code, the test fails, the failure text enters the next iteration, and now the agent has evidence it was wrong.

This reframes accuracy. You stop trying to get a perfect answer in one shot and start designing a loop that converges. A loop that runs a test after every edit is more accurate than the smartest single prompt, because it grounds each step in a verifiable result rather than the model's confidence.

The cost problem hiding inside every loop

Loops have a brutal economic property: cost scales with iterations, and each iteration typically hits the LLM. A 30-step loop is roughly 30 model calls, and most of those calls re-send the same context. The agent re-reads the same files, re-explains the same architecture, and re-derives the same facts on every pass. You pay full inference price to regenerate knowledge the system already produced two steps ago.

This is why naive agent loops get expensive fast. The token bill is dominated not by reasoning but by repeated context. A long-running loop that is genuinely useful can cost more than the task is worth if every iteration is a full-price regeneration.

A retrieval layer changes the loop economics

Put a retrieval step at the front of each iteration and the math changes. Before the loop calls the LLM, it searches an index for the grounded context it needs: the relevant file, the prior decision, the API contract, the earlier observation. RetroDeck's RDK does this against encrypted private chunks indexed from your own vault, so the loop pulls the fact instead of paying the model to reconstruct it.

With stacked retrieval, private vault search answers 40 to 65 percent of what an iteration needs, the public network adds another 15 to 20 percent, and the LLM handles the remaining 5 to 10 percent as fallback. Token spend on the loop drops 80 to 90 percent, because most iterations retrieve grounded context rather than regenerate it. The loop can now run for dozens of steps and stay affordable and accurate.

How to build a loop that does not run away

A useful loop must also fail safe. Design these controls in from the start:

Cap the iterations. Set a hard maximum so a stuck agent cannot burn your budget overnight. Detect no-progress states. If two consecutive observations are identical, the loop is spinning; break and escalate. Define done explicitly. The agent needs a verifiable stop condition, like a passing test suite, not a vibe. Cheap check, expensive act. Use retrieval and small models to plan and observe, and reserve the expensive model for the steps that truly need it.

These are the same disciplines you would put on any control system. Loop engineering is closer to writing a resilient service than to writing a clever prompt.

Frequently asked questions

Is loop engineering different from prompt engineering?
Yes. Prompt engineering optimizes the wording of a single request and response. Loop engineering optimizes the repeating plan-act-observe cycle the agent runs on. The prompt still exists inside the loop, but the real work is designing how the agent reacts to each observation and decides whether to continue, retry, or stop.
Why do agent loops get so expensive?
Cost scales with iterations, and each iteration usually calls the LLM. Most calls re-send context the system already produced, so you pay full inference price to regenerate the same files, decisions, and facts on every pass. A 30-step loop is often 30 near-duplicate regenerations, which is the bill nobody budgets for.
How does retrieval make a loop cheaper without hurting accuracy?
Retrieval puts a search step before each LLM call. The loop pulls grounded facts from an index instead of asking the model to reconstruct them. With stacked retrieval, private and public chunks answer most iterations and the model handles only the fallback, cutting token spend 80 to 90 percent while improving accuracy, since answers are grounded rather than generated.
How do I stop an agent loop from running forever?
Design fail-safe controls: cap the maximum iterations, detect no-progress states by comparing consecutive observations, and define an explicit verifiable stop condition like a passing test suite. When the loop cannot make progress, it should escalate to a human or a cheaper path rather than spin indefinitely.