Engineer the loop.
Control the outcome.
Learn how to design AI agent systems that plan, act, evaluate, recover, and stop with purpose—not by accident.
- 27
- core topics
- 5
- knowledge tracks
- 1
- human review gate
Five tracks. One operating model.
Move from definitions to production controls without losing the feedback loop that connects them.
Fundamentals
Definitions, mental models, benefits, limits, and the anatomy of an AI agent loop.
Explore track ↗02System Design
Harnesses, context, memory, evaluation, budgets, stop conditions, and observability.
Explore track ↗03Comparisons
Clear boundaries between loop, prompt, context, harness, and human-in-the-loop engineering.
Explore track ↗04Practical Workflows
Repeatable patterns for coding agents, specs, tests, Claude Code, Codex, and Ralph-style loops.
Explore track ↗05Reliability & Safety
Guardrails, failure recovery, security boundaries, cost control, and human approval patterns.
Explore track ↗Loop Engineering is the discipline of designing the system that repeatedly guides, observes, evaluates, and constrains an AI agent.
The prompt is one input. The engineered loop is the operating environment around it.
Understand the complete model →Start reading
Human-reviewed explanations and implementation guidance from across the knowledge map.
Agent Harness Explained: The Core of Reliable AI Loops
Learn what an agent harness is, why it matters, and how it supports memory, tools, loops, tracing, and evaluation.
Read article →Agent Loop Retry Patterns for Reliable Autonomous Systems
Learn how agent loop retry patterns work, when to use them, and how to avoid runaway failures in autonomous AI systems.
Read article →Agent Loop Stop Conditions: How to Make Loops End Safely
Learn how to choose stop conditions for agent loops, avoid runaway execution, and design reliable termination behavior.
Read article →Agent Loop Token Budget: How to Control Context Cost
Learn why agent loops get expensive and how to constrain context with pruning, resets, subagents, and summaries.
Read article →AI Agent Evaluation Loop: Build Reliable Systems
Learn how AI agent evaluation loops combine tracing, rubrics, and fixes to improve reliability and reduce guesswork.
Read article →AI Agent Loop Guardrails: Where to Place Them
Learn where to place guardrails in AI agent loops, what they block, and how to balance safety, context, and latency.
Read article →Evidence collected. Drafts checked. Humans decide.
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