The Agentic Loop, How Modern Agents Actually Run
Pull-quote: “An agent is not a model that talks longer. It is a model wrapped in a loop, and everything that makes the agent trustworthy lives in the loop, not in the model.”
Why this matters
Most enterprise conversations about agents still picture a very good autocomplete: one prompt in, one long answer out. Production agents do not work that way. They run a loop: perceive the current state, plan the next step, act by calling a tool, observe the result, verify it against the goal, and repeat. Every serious agent framework, whatever the branding, is an implementation of this loop with different opinions about state and control flow. If you evaluate an agent platform, evaluate the loop.
The loop
┌────────────────────────────────────────────┐
│ │
▼ │
Perceive ──► Plan ──► Act ──► Observe ──► Verify ─┤
(state, (next (tool (result, (goal │
context) step) call) errors) met?) ──┴──► Terminate
(done / budget /
escalate)Each pass through the loop is cheap and narrow: one decision, one tool call, one observation. The intelligence is cumulative. The agent that files a correct answer after nine steps did not know the answer at step one; it earned it by acting, observing, and correcting.
Why the loop beats one-shot generation
One-shot generation asks the model to be right about everything simultaneously: the facts, the plan, and the output format, with no feedback. The loop replaces that bet with a series of smaller, checkable bets.
The practical differences show up in three places. First, grounding: a loop can query the real system of record instead of recalling it, so answers reflect state as of the tool call, not as of the training cutoff. Second, error recovery: when a tool call fails or returns something surprising, the agent observes the failure and re-plans, where a one-shot answer would have silently absorbed the error. Third, auditability: a loop leaves a transcript of discrete steps, each attributable to a tool call with inputs and outputs. That transcript is what a reviewer, or a regulator, can actually inspect. An agent built this way answers like an analyst: the answer is a sequence of tool calls over the system of record, not a recollection, so every number in the output traces to a step in the loop.
Where loops fail
| Failure mode | What it looks like | Countermeasure |
|---|---|---|
| Unbounded iteration | The agent loops on a stuck subtask, burning budget | Hard step and cost caps; per-task budgets |
| Context rot | Old errors and stale observations pollute later decisions | Compaction; drop superseded observations |
| Verification theater | The agent “checks” its own work with the same reasoning that produced it | Independent verification: tests, schemas, a second model |
| Silent goal drift | Step 14 is solving a different problem than step 1 | Restate the goal in the loop; verify against the original |
None of these are model failures. They are control-flow failures, and they are fixed in the harness.
Termination criteria and verification gates
A loop without explicit termination criteria is an outage with extra steps. Production agents need three exits, all designed before launch: success (a verification gate passed, not the model’s self-assessment), budget exhaustion (steps, tokens, wall clock, or dollars, whichever binds first), and escalation (the agent recognizes it is stuck and hands off with its transcript).
The verification gate deserves the most design attention. The gate should be a check the model cannot argue with: a test suite that passes, output that validates against a schema, a reconciliation that balances, a retrieval that confirms the cited record exists. Where the domain allows it, make the gate deterministic code. Where it does not, use a separate model with a narrow verification prompt, which is a weaker gate but still stronger than self-grading.
Closing
Agents are loops, and the engineering that matters is loop engineering: what state the agent perceives, which tools it can call, how results are verified, and when the loop stops. Teams that internalize this stop shopping for a smarter model and start building better harnesses, and their agents get more trustworthy without a single weight changing.