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Agentic Loops

Agentic loops

An agentic loop is the pattern behind autonomous AI: reason → act → observe → repeat until a stopping condition. The agent takes an action, sees the result, decides the next move, and keeps going without a human between steps. The current wisdom — “loop engineering” — is that the loop, not the model, is what separates a reliable agent from a demo.

almyty’s autonomous agents are governed agentic loops. You bring the model; almyty gives you the loop — and the controls that make it safe to leave running.

The loop, in almyty

Each autonomous run is a loop of steps, and you can see every one:

  1. Reason — the primary model decides what to do next.
  2. Act — it calls a tool (an operation on a connected API, an npm/SDK function, a runner command).
  3. Observe — the tool’s result feeds back into context.
  4. Evaluate & repeat — it continues until the task is done or a stop condition fires.

A real run from the demo’s support agent: tool_call → Lookup Order Status (NW-10428) → observe → draft → verify → the reviewer caught an inconsistency → 1 revision → pass. Three-to-six steps, visible end to end.

Loop engineering — the knobs almyty gives you

The differentiator isn’t that agents loop; it’s that you can engineer the loop:

  • Self-correction — a cross-vendor verifier panel re-checks each result; on failure the agent revises and retries. See verification.
  • Termination — a revise budget (e.g. 2 revisions, any_fail_blocks) is the stop condition, so a loop can’t spin forever.
  • Guardrails inside the loop — always-on constraints, some learned from past failures, bound what each iteration may do.
  • Multi-agent loops — collaboration runs several agents: sequential chains, parallel fan-out, race first-to-finish, or debate (rounds of argument settled by a judge).
  • Human-in-the-loop — request_approval pauses the loop for a person before an irreversible step, then resumes.
  • Loops that compound — memory carries context across runs, and a good run can be promoted to a reusable skill.

Why it matters

A bare model call answers once. An agentic loop keeps working — checking itself, correcting, and stopping when it should. almyty makes that loop observable (every step in the run view), bounded (revise budgets, constraints), and governed (verification, approvals, audit) — so you can put a loop in production, not just a notebook.

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