Loop Engineering Reliable Ai Agent Loops
 

Design, control, and ship production AI agent loops -- with guardrails that stop runaway cost and infinite cycles.
What you'll learn
Understand the agentic loop: reason, act, observe, decide
Make the shift from prompt engineering to loop engineering
Define verifiable goals and real termination criteria (not the agent's self-assessment)
Choose the right control pattern: ReAct, plan-then-execute, or orchestrator-worker
Manage agent state, memory, tools, and delegation safely (least privilege)
Build the guardrails that stop runaway loops: iteration caps, cost budgets, no-progress detection, circuit breakers
Trace, monitor, and evaluate loops in production
Ship an agent loop that finishes, stays on budget, and fails safely
Requirements
Comfort building with or using AI agents / calling an LLM API
Basic programming familiarity (able to read pseudocode)
No advanced machine-learning background required
Description
This course contains the use of artificial intelligence.
The hardest part of building with AI agents is no longer the prompt -- it's the loop. An agent runs in a cycle: it takes an action, sees the result, and decides what to do next, over and over until it's done. Get that loop right and you have a reliable autonomous system; get it wrong and you have infinite cycles, silent failures, and billing surprises orders of magnitude over budget. This course teaches loop engineering: the practice of designing, controlling, and operating agentic loops on purpose.
You'll start with the mental shift from prompt engineering to loop engineering, and exactly how an agentic loop works. Then you'll master the anatomy of a loop: defining verifiable goals, choosing triggers, picking the right control pattern (from ReAct to plan-then-execute to orchestrator-worker), wiring up tools and delegation, and managing agent state and memory.
The heart of the course is guardrails -- the non-negotiable safety constraints that separate a demo from production: hard iteration caps, token and cost budgets, no-progress detection, circuit breakers on tool calls, and real termination criteria based on verifiable checks rather than the agent grading its own work. You'll finish with observability and evaluation, and a capstone that engineers a production-ready loop end to end, plus a reusable checklist.
By the end you'll design agent loops that finish, stay on budget, and fail safely -- the core skill for shipping agents in 2026.
Who this course is for
Software engineers and architects building AI agents
Teams shipping autonomous/agentic features who need reliability and cost control
Anyone moving from prompt engineering to production-grade agents