Loop Engineering Fundamentals
For engineers building autonomous agentic systems that need to actually work in production
Program Overview
What This Program Covers
As AI agents move from single-turn chat assistants to autonomous problem solvers, the engineering bottleneck has shifted to designing the perfect loop. Loop engineering structures AI-assisted workflows around repeating, unattended cycles. This intensive 2-day course focuses on designing these cycles using the PACS framework (Plan, Act, Check, Stop) to architect reliable, autonomous systems. Participants will build, debug, and optimize complex loops from scratch — covering everything from stop logic and context management to multi-agent topologies and human-in-the-loop gates.
What You'll Learn
- 1Design reliable autonomous agent loops using the PACS framework — Plan, Act, Check, Stop
- 2Build verifiable stop rules that prevent runaway agents and infinite loops in production
- 3Implement state and context management across multi-turn agentic runs without state loss
- 4Apply the ReAct pattern to enable agents to reason, act, and observe feedback dynamically
- 5Apply the Reflexion pattern to teach agents to self-critique and improve across iterations
- 6Design human-in-the-loop gate architectures for high-risk autonomous decision points
- 7Build multi-agent topologies including orchestrator-worker patterns and evaluator-optimizer loops
Outline
Program Snapshot
Module 1 — The Shift to Loop Engineering
- ›The evolution from prompt engineering to context engineering to loop engineering
- ›Why single-turn prompting fails for complex software tasks
- ›The anatomy of a loop: trigger, state, verifier, and stop logic
- ›The ReAct pattern: reason, act, observe feedback, determine next action
Module 2 — The Control Plane: Safety and Economics
- ›The PACS Framework: structuring cycles around Plan, Act, Check, and Stop
- ›Verifiable stop rules: deterministic, automated checks as exit conditions
- ›Preventing infinite loops: detecting no-progress states and designing escape hatches
- ›Budgets and circuit breakers: token ceilings, hard USD caps, and preventing runaway automation
Module 3 — State and Context Management
- ›The context overflow problem: preventing reasoning degradation across long-horizon tasks
- ›Managing state across runs: offloading memory to external storage
- ›Targeted observability: equipping agents to parse logs, compiler errors, and tool outputs
- ›The Reflexion pattern: teaching agents to generate self-critiques and inject corrections
Module 4 — Advanced Topologies and Scaffolding
- ›Sub-agents and worktrees: the orchestrator-worker pattern for parallel task execution
- ›Evaluator-optimizer loops: deploying secondary models to grade and auto-correct the primary actor
- ›The HITL Gate: designing asynchronous human approval routing for high-risk decisions
- ›Handling transient failures: idempotency, API unreliability, and automatic retry logic
Who This Is For
- AI engineers building autonomous agentic systems for production deployment
- Software architects designing the infrastructure layer for multi-step AI workflows
- Senior developers moving from single-turn AI integrations to fully autonomous agent systems
- Any engineering team whose agents work in demos but fail in production
Prerequisites
- Familiarity with Python, REST APIs, and basic LLM prompt engineering
- Experience building at least one AI-powered application or integration
- General understanding of software architecture and systems design
- No prior experience with agentic systems required
Bring This Program to Your Team
Every bILTup program is fully customized to your team's tech stack, goals, and timeline. Tell us about your team and we'll design something built specifically for you.