How it works
An agent loop repeats a simple cycle: the model reads context, chooses an action, a tool runs, and the result feeds back in. Loop engineering is the work on the parts of that cycle you don't see in a demo — the parts that decide whether it survives contact with real inputs.
Three pieces carry most of the weight:
- Memory across turns. What the loop remembers, summarizes, or drops between iterations. Left alone, context grows until it crowds out the task or overruns the window, and the agent starts forgetting its own earlier decisions.
- Retries on tool failure. What happens when a tool times out, returns malformed data, or errors. A raw loop either crashes or feeds the error back as if it were a valid result, then reasons confidently over garbage.
- Stop conditions. How the loop decides it is done, stuck, or spinning. Without an explicit condition an agent will happily re-call the same tool and re-decide the same step until something external kills it.
Loop engineering makes these explicit and testable: a bounded iteration count, a retry policy with backoff and a defined fallback, a summarization or eviction rule for memory, and a termination check that fires on success, on exhaustion, or on detected non-progress. The goal is a loop whose behavior you can predict at the edges, not just in the happy path.
Why it matters in an agent harness
A harness exists to keep an agent controllable, and the loop is where control is won or lost. Everything the harness promises — bounded cost, reversibility, legibility — has to be enforced turn by turn, inside the cycle.
Concretely, loop engineering is what gives you:
- Termination guarantees. An iteration cap and a real stop condition convert an open-ended process into one with a known worst case. That is the difference between an agent you can run unattended and one you have to babysit.
- Failure containment. A retry-and-fallback policy decides whether one flaky tool call degrades gracefully or poisons the rest of the trajectory. You choose fail-closed versus fail-open deliberately instead of inheriting whatever the raw loop does.
- Reversibility and observability. When turn boundaries are explicit, you can checkpoint between them, log each decision, and replay a run. A loop with no seams is a loop you cannot inspect or roll back.
- Cost discipline. Memory and iteration budgets keep a single job from silently consuming ten times its expected tokens because it got stuck in a corner.
A well-engineered loop is the unit that makes the rest of the harness — permissions, budgets, checkpoints — enforceable rather than aspirational.
Loop engineering vs context engineering
The two overlap and get conflated, but they answer different questions and lead to different work.
| Loop engineering | Context engineering | |
|---|---|---|
| Core question | When does the loop retry, continue, or stop? | What information sits in the window this turn? |
| Primary levers | Iteration caps, retry policy, stop conditions, checkpoints | Retrieval, summarization, ordering, eviction |
| Failure it prevents | Runaway loops, unhandled tool errors, non-termination | Missing facts, distraction, window overflow |
| Owns | The control flow of the cycle | The payload the model reasons over |
Memory is where they meet: deciding what to keep is context engineering; deciding when to compact and how that ties into the stop condition is loop engineering. Treat them as separate axes and you can reason about a stuck agent instead of guessing whether it's a prompt problem or a control problem.
The Rifty take
We optimize for loops that terminate on purpose. Every loop gets an explicit stop condition and a hard iteration ceiling before it gets more autonomy, because a loop that can't reliably stop is not controllable no matter how good its reasoning is. We accept the tradeoff that a bounded loop occasionally quits a task it might have finished with more turns — a clean, legible stop beats an open-ended spin every time.
Common failure modes
- No stop condition. The loop runs until an external timeout or the token budget dies. Add an explicit success and non-progress check, not just a max-turns backstop.
- Errors treated as results. A failed tool call is fed back as data and the model reasons over it. Classify errors and route them to a retry or fallback path.
- Unbounded memory. Context accretes every turn until the window overflows or the earliest instructions fall out. Set an eviction or summarization rule tied to the loop.
- Silent loop-back. The agent re-calls the same tool with the same arguments. Detect repetition and treat it as a stop-or-escalate signal.
- Retries without backoff or a cap. A flaky dependency turns into a tight hammering loop. Bound retries, add backoff, and define what happens after the last one fails.
- No turn seams. Iterations blur together, so you can't checkpoint, log, or replay. Make each turn a discrete, inspectable boundary.
Frequently asked questions
How is loop engineering different from just writing a good agent loop?
Writing a loop gets the happy path working; loop engineering hardens the parts that fail under real inputs. It adds explicit stop conditions, retry-and-fallback policies for tool errors, memory management across turns, and iteration bounds — the mechanics that make the loop controllable and predictable rather than open-ended.
Why does an agent loop need an explicit stop condition?
Because without one the loop only stops when something external kills it — a timeout, a token cap, or a crash. An explicit condition lets the loop terminate on success, on exhaustion, or on detected non-progress, converting an open-ended process into one with a known, testable worst case.
What's the right way to handle a tool that fails mid-loop?
Classify the failure instead of feeding the error back as a result. Decide deliberately between retrying with backoff, using a defined fallback, or failing closed and stopping. Cap the retries so a flaky dependency can't turn into a tight hammering loop, and log the decision for replay.
How does loop engineering relate to context engineering?
They're separate axes that meet at memory. Loop engineering owns control flow — when the loop retries, continues, or stops. Context engineering owns the payload — what sits in the window each turn. Deciding what to keep is context work; deciding when to compact and how it ties into termination is loop work.
What are the first controls to add to an unmanaged agent loop?
Start with a hard iteration ceiling and an explicit stop condition, since a loop that can't reliably terminate isn't controllable. Then add a retry policy with backoff and a fallback for tool failures, a memory eviction or summarization rule, and discrete turn boundaries so you can checkpoint and replay.