How to Make an AI Agent Loop Reliable Instead of Just Clever

An AI agent is a loop. The model emits a tool call, the execution environment runs it, the result goes back into the context, and the model decides what to do next. That cycle repeats until the model stops asking for tools or the runtime cuts it off. Almost every hard problem in agent engineering is a property of that loop rather than of the model, and the reliability work happens entirely in the loop around it.

What is an AI agent loop?

An agent loop is the control structure that runs an agent: emit a tool call, execute it, feed the result back, decide the next action, repeat. The model contributes the decisions, but the loop contributes termination, timing, and context management. Those are the properties that decide whether an agent survives contact with real work.

The model gets the credit when an agent performs well. It is the loop that keeps the agent from hanging forever, from blowing its context window, and from answering from a response that was never complete. A capable model inside a fragile loop fails far more often than a mediocre model inside a disciplined one.

How does the agent loop work in Askpert?

At Askpert we run sellers' AI Experts on our own runtime, which means the loop that executes each Expert is shared infrastructure. The runtime sets the budgets, stores the tool output, and controls what the model is allowed to see. Sellers build the agent; we own the loop it runs in.

The rule that holds across every Expert is that the loop is the safety boundary. A seller cannot ship a hung turn or an unbounded loop, because the runtime enforces the same discipline on every agent. That separation is what makes a marketplace of seller-built agents reliable.

What budgets keep the loop from failing?

Our loop gives a turn 100 tool iterations. Each individual tool call must return within 30 seconds, and at most 4 run in parallel. Output from the code tool, the one most likely to produce a flood, is capped at 8 kilobytes of what the model sees. Every one of those numbers is a defense against a different failure.

The iteration cap stops an unbounded loop that never terminates. The per-call timeout stops a single hanging call from freezing the turn. The parallel limit bounds how much contention one turn can create. The byte cap keeps a huge result from blowing the context window. Each budget exists because the failure it prevents is real and measurable.

Why is truncation a view decision, not a destruction decision?

The naive implementation truncates a large tool result to fit the cap and throws the rest away. Everything past the cut is simply gone, and the agent cannot get it back even when it turns out to be the part that mattered. That design saves context at the cost of information.

We changed this. The full output is written to storage, and the model receives the bounded head plus a reference. A dedicated read tool can then fetch byte windows of the stored output, search it with a pattern, or list what the conversation has stored. The context stays small, and nothing is lost. The rule that generalizes: never destroy information at a boundary whose only purpose is display. Cap what the model sees, not what the system keeps.

Why are there two different kinds of incomplete output?

Once full outputs are stored, there are two completely different reasons a result can be incomplete. One: the storage ceiling was hit, so the runtime holds less than the tool produced. Two: the tool itself never received its source in full, because an HTTP response was cut by a size limit or a wall-clock deadline.

In the second case the tool's output is short but complete as far as the system can tell, so a naive implementation reports it as complete at N bytes. The agent then confidently answers from a response that was actually severed mid-stream. We track those two states separately and say "the source was cut off" on every surface that shows the output. An adversarial review found this before it shipped.

Why does the runtime distrust the SDK's own timeout?

We do not trust the SDK's own timeout. We observed a 20 second sleep run to completion under a 3 second timeout setting, which means the SDK-level timer could not be relied on to bound the work. The runtime enforces its own wall clock with a race instead, and an over-budget run is abandoned.

The lesson generalizes to any execution environment: a timeout that another layer claims to enforce is a claim, not a guarantee. Bounding execution at the process boundary, where the runtime owns the outcome, is the only timeout that reliably terminates a run.

How does a cold container behave in the loop?

A cold container does not block and wait. It returns a retryable "is starting" signal immediately, so the caller owns retry with backoff. Our backoff doubles from 2 seconds to a 16 second cap inside a generous first-call budget, because the first real execution after an image pull can take minutes.

A short timeout on the first call of a session fails for a reason that has nothing to do with the work. The caller has to distinguish a container that is genuinely starting from one that is failing, and retry the former with backoff rather than treating either as a terminal error.

How should you evaluate an agent loop?

Ask questions of any runtime you are evaluating or building. Does it bound iterations, call time, parallelism, and context? Does it store full tool output and cap only what the model sees, so nothing is destroyed? Does it distinguish a storage truncation from a source that was severed before arrival? Does it enforce timeouts at its own boundary rather than trusting an SDK, and does it handle a cold container with backoff? A runtime that answers all four owns its reliability. One that hedges on any of them is where the model gets blamed for a loop that was never disciplined.

This is the execution layer, one turn at a time. The layer above it, the system of stages that decides what the agent works on in the first place, is loop engineering, and the two are routinely confused.