Loop Engineering

Loop engineering treats an AI agent as a worker inside a feedback system. Instead of manually prompting every turn, you define a goal and let the runtime repeat four responsibilities: act, observe the result, adjust the next action, and test whether the finish line has been reached.

• Goal: use a specific, testable outcome instead of an open-ended instruction.
• Action: let the agent call tools, change files, gather evidence, or produce the next artifact.
• Observation and adjustment: inspect real results before choosing the next action.
• Stop: return success, a concrete blocker, or a budget-limit outcome instead of looping indefinitely.

Automations, worktrees, skills, connectors, sub-agents, and persistent state can make a loop repeatable and useful, but they are supporting infrastructure rather than the loop’s core control semantics. When those pieces gain explicit dependencies, parallel branches, checkpoints, approvals, and recovery routes, the design starts to become graph engineering.

• A harness supplies tools, files, permissions, memory, sandboxes, and feedback sensors around the agent.
• Automation can trigger a loop, while persistent state lets a later run resume from durable progress.
• Separate maker and checker roles can improve evaluation without changing the loop’s local goal.
• Graph orchestration becomes useful when several loops must coordinate as distinct work units.

Loop engineering is not a promise of autopilot quality. It is a governance layer. Without explicit quality gates, bounded tasks, and external review, an infinite loop can still produce low-quality or dangerous changes.

• The loop can increase throughput, but not automatically increase correctness.
• Maker-checker split and test gates are required inside long-running workflows.
• Verification and stop criteria are the loop’s control valves, not optional niceties.

The strongest loop design starts from conditions, not prompts. A condition must be measurable and checkable from transcript evidence or tests, and the loop should include explicit budget or turn limits.

• Ask for explicit finish lines such as “all tests pass”, “CI green”, or “issue queue empty”.
• Limit iteration count, token spend, and runtime to avoid unbounded compute.
• Pause or reassign work when the same error repeats, rather than waiting for accidental recovery.

The harness is the environment around an agent: tools, permissions, files, memory, sandboxes, and feedback. The loop is the repeated behavior inside that environment. A graph coordinates multiple loops or deterministic steps through explicit state, nodes, edges, branches, checkpoints, and human gates. A production system may use all three layers without treating them as synonyms.