README.md

loop-engineering

A beginner-friendly guide to understanding Loop Engineering in agentic AI systems, how to structure feedback loops, and how to prevent cycle drift.

loop engineering is the discipline of structuring the perception-action-reflection cycle with clear exit gates, iteration budgets, and cycle-drift detection

Table of Contents

• The Simple Idea
• The Anatomy of an Agentic Loop
• Loop Hazards: Infinite Loops & Cycle Drift
• Loop Engineering Techniques
• My Learning Notes
• Common Misunderstandings
• Related Concepts

The Simple Idea

An agent without a well-engineered loop is either a single prompt-response call or a runaway process stuck repeating the exact same broken action.

Loop Engineering focuses on building stable, metered loops that move closer to the goal with every cycle:

Input Goal -> [Perceive -> Select Tool -> Execute -> Observe -> Reflect -> Exit Check] -> Outcome

The loop continues only as long as state improves, and terminates when verification conditions are met.

The Anatomy of an Agentic Loop

1. Perception: Reading fresh state from environment (logs, test outputs, AST).
2. Action Selection: Model picking the optimal tool to advance state.
3. Execution: Safe runtime execution of the tool call.
4. Observation: Capturing exact tool outputs without truncation.
5. Reflection: Evaluating if the outcome brought the agent closer to the goal.
6. Exit Verification: Checking deterministic exit conditions (e.g. go test passes).

Loop Hazards: Infinite Loops & Cycle Drift

• Repetitive Retries: The agent tries the same broken code edit 5 times in a row.
• Infinite Oscillations: Action A fixes Issue 1 but breaks Issue 2; Action B fixes Issue 2 but breaks Issue 1.
• Goal Drift: The agent gets distracted by minor warnings and loses track of the main goal.

Loop Engineering Techniques

1. Energy / Step Budgets: Allocating max 10 steps per subtask.
2. Duplicate Action Detection: Hashing tool payloads to detect duplicate edits and force strategy changes.
3. Deterministic Exit Gates: Requiring explicit verification tests before exiting the loop.
4. Reactive Wakeups: Suspending the loop during long async tasks and resuming only on notification events.

My Learning Notes

Early agent loops were simple for loops wrapped around model calls.

Real stability requires engineering the loop control flow:

never trust an open-ended loop — set iteration energy caps, track state deltas, and enforce hard exit criteria

Common Misunderstandings

"A longer loop is a smarter agent."
No. Efficient loop engineering minimizes the number of steps required to reach a verified outcome.

Related Concepts

Previous: agentic-ai
Next: graph-engineering
Related: agentic-runtime, evaluation