AI agent loop orchestration

Agent Loops & Orchestration

Control the observe-decide-act loop with explicit state, budgets, and termination conditions.

DIFFICULTY
Advanced
EST. TIME
60 min
UPDATED
2026-08-19
On this page
  1. 01Definition
  2. 02Why it matters
  3. 03Step by step
  4. 04Build it
  5. 05Failure modes
  6. 06Production notes
  7. 07Sources

Learning objectives

  • Implement a bounded agent loop
  • Separate orchestration state from model conversation
  • Handle retries, repeated actions, and non-progress

Prerequisites

Tool use · Context and state · Structured outputs

Answer first

Canonical definition

An agent loop repeatedly assembles state and observations, asks a model for a next action, executes an approved action, records the result, and stops when a goal, limit, or escalation condition is reached.

Operating context

Why it matters

The loop is where a useful one-step model call becomes an adaptive system—and where cost, repeated actions, and compounding errors appear.

Reliable orchestration makes progress and failure visible outside the model transcript.

Method

Step by step

  1. 01

    Model the state

    Store goal, completed work, outstanding constraints, tool results, approvals, and terminal status in application state.

  2. 02

    Bound every run

    Set maximum steps, wall time, tokens or spend, retries, and side-effect counts.

  3. 03

    Detect non-progress

    Stop repeated tool calls, unchanged plans, circular delegation, and outputs that fail validation more than a defined number of times.

  4. 04

    Escalate with context

    When the loop stops, give a human the goal, evidence, attempted actions, current state, and recommended next decision.

Hands-on lab

Implement a bounded research loop

Let an agent search an approved corpus until it has enough evidence or reaches a step and confidence limit.

Deliverable

A loop controller, state schema, budget guard, repetition detector, trace, and escalation packet.

What breaks

Common failure modes

  • F1Letting the model decide whether cost and policy limits apply
  • F2Retrying the same failing action without changing state
  • F3Encoding orchestration entirely inside a prompt

Beyond the demo

Production notes

  • Persist state before and after side effects so a retry can determine what already happened.
  • Use deterministic orchestration for policy, budgets, and state transitions; reserve model reasoning for uncertain decisions.

Further reading

Sources

  1. [1]Anthropic: Building effective agents

Related Tenten resources

From capstone to production

A working demo is the beginning of the engineering problem.

Tenten helps teams scope one agentic workflow, integrate real data and permissions, define evals and handoffs, and own the path through launch and on-call.