What is an agent loop?

The repeated control cycle in which an agent observes state, reasons about the next action, invokes a tool or produces output, evaluates the result, and decides whether to continue.

Agent loop should be understood as a system boundary, not a marketing label. Its inputs, outputs, state, permissions, and failure behavior need explicit contracts.

In production, the definition also includes the surrounding control plane. Logging, identity, policy evaluation, retries, and observability determine whether the capability is dependable.

A useful test is whether two engineers can implement the same behavior from the specification. If the term only describes an outcome without interfaces or constraints, the definition is incomplete.

The loop provides adaptability but is also where runaway costs, repeated actions, tool misuse, and false completion can occur.

The practical value of Agent loop appears when volume, model diversity, customer context, or operational risk grows beyond what a manual process can handle.

It also changes system economics. Teams can separate expensive reasoning from routine execution, measure successful outcomes, and apply controls at the layer where decisions are made.

The strongest implementations connect technical metrics to business results. Accuracy alone is insufficient if latency, cost, handoff quality, or auditability makes the system unusable.

The runtime assembles context, calls the model, parses an action, executes it, adds the result to state, evaluates progress, and repeats until stop.

A production implementation starts with typed inputs and an explicit state model. Each transition should record what was observed, which policy applied, what action was selected, and what evidence came back.

The execution path needs deterministic boundaries around model calls. Tool schemas, timeouts, idempotency keys, rate limits, and permission checks should be enforced by code rather than left inside prompts.

Evaluation closes the path. Traces should make it possible to replay failures, compare versions, detect drift, and distinguish a model error from stale data, a broken tool, or an incorrect policy.

A coding agent reads a failing test, edits a file, runs the test, inspects the error, revises the code, and stops when checks pass.

The important part of this example is the chain of state changes. Every lookup, decision, tool call, response, and handoff should be attributable to one request and one customer or system identity.

A robust implementation handles the unhappy path as deliberately as the successful path. Missing context, ambiguous identity, provider failure, duplicate events, and low confidence should lead to bounded retries or human review.

The example can be tested with a replayable fixture. Teams should verify expected output, side effects, latency budget, cost budget, and the audit record before enabling the flow for live traffic.

Set maximum steps, token and time budgets, duplicate-action detection, idempotency, completion evidence, error classes, backoff, and human checkpoints.

Start with the smallest closed path that creates measurable value. Define the owner, inputs, allowed actions, completion evidence, rollback behavior, and escalation route before adding autonomy.

Instrument the path from day one. Capture structured traces, policy decisions, model and tool versions, token and latency costs, user feedback, and whether the final outcome was accepted or corrected.

Security and governance are architectural requirements. Apply least privilege, isolate secrets, minimize retained data, enforce regional and channel policies, and require approval for irreversible or high-impact actions.