Loop Engineering for Agentic AI

This course contains the use of artificial intelligence.Build Reliable Agentic AI SystemsAgentic AI is more than an LLM responding to a prompt. A reliable agent operates through a controlled loop: it interprets a goal, selects an action, uses a tool, observes the result, evaluates progress, and continues until it reaches a verified outcome.This hands-on course teaches the foundations of Loop Engineering for Agentic AI. You will learn how to design, build, control, debug, and evaluate agent loops that perform meaningful work without becoming unpredictable, repetitive, or unsafe.What You Will BuildYou will build one evolving Python project throughout the course. Starting with a minimal tool-using agent, you will progressively add:Tool calling and validated action schemasState, memory, checkpoints, and recoveryContext-window management and compactionTermination conditions and resource limitsGuardrails and permission boundariesTracing, verification, and debuggingMulti-agent orchestration and handoffsHuman approval checkpointsThe final capstone is a reliable issue-resolution agent that can inspect a repository, use development tools, preserve progress, detect non-progress, delegate verification, request approval, and produce an auditable execution report.What You Will LearnExplain how an agentic loop differs from a single LLM callDesign the goal–act–observe–evaluate cycleBuild a working tool-calling agent loop in PythonCreate clear tool contracts and validate agent actionsHandle tool errors, retries, timeouts, and invalid requestsManage state and memory across agent iterationsCheckpoint, resume, and recover interrupted agent runsControl context growth and prevent context driftDefine reliable success, failure, blocked, and escalation outcomesDetect repetition, oscillation, and non-progressApply permissions, guardrails, and risk-based approvalsVerify outcomes using tests, validators, and reviewer agentsTrace, replay, diagnose, and repair failed runsImplement sub-agent, orchestrator-worker, and handoff patternsApply Loop Engineering concepts with Claude CodePrepare agentic systems for safe production useClaude Code and Multi-Agent WorkflowsA dedicated section demonstrates how Claude Code can support Loop Engineering through project instructions, skills, plugins, MCP integrations, hooks, automations, specialized sub-agents, permissions, and Git worktrees.You will compare single-agent and multi-agent designs, implement an orchestrator-worker workflow, define reliable handoff contracts, and prevent delegation loops or conflicting work.Hands-On Course FormatConcise, focused theoryProgressive guided labsPython coding exercisesDecision-based role-play activitiesSection quizzesTwo full-length practice testsReusable templates and checklistsOne integrated capstone projectA mock LLM adapter supports no-cost practice. An optional live-model adapter is included for learners who want to experiment with a real model provider.Who This Course Is ForAI engineers building agentic applicationsSoftware developers moving beyond basic promptingSolution architects designing reliable AI systemsTechnical leads evaluating agent architecturesAutomation engineers creating tool-driven workflowsLearners interested in Claude Code and multi-agent developmentBasic Python knowledge is helpful, but prior experience building AI agents is not required.Course OutcomeBy the end of the course, you will understand not only how to make an agent act, but also how to make it stop correctly, recover safely, verify completion, escalate intelligently, and remain under meaningful human control.