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Loop Engineering for Agentic AI
Published 8/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 74 Lectures ( 5h 49m ) | Size: 9.8 GB
Build reliable agent loops with tools, memory, context, guardrails, multi-agent workflows, Claude Code, and labs.
What you'll learn
Explain how agentic loops differ from single LLM calls Build a tool-calling agent loop using Python Manage agent state, memory, checkpoints, and context Design reliable termination conditions and guardrails Detect repetition, non-progress, and context drift Debug and verify agents using traces, tests, and evaluators Implement sub-agent, handoff, and orchestrator-worker patterns Apply Loop Engineering using Claude Code, skills, plugins, MCP, hooks, automations, and worktrees Add human approval checkpoints and production controls Build a reliable, auditable issue-resolution agentRequirements
Basic Python programming knowledge is helpful A computer capable of running Python and a code editor Basic familiarity with LLMs and prompting No previous experience building AI agents is required No paid AI subscription is required for the core exercisesDescription
This course contains the use of artificial intelligence.
Build Reliable Agentic AI Systems
Agentic 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 ofLoop 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 Build
You 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 schemas State, memory, checkpoints, and recovery Context-window management and compaction Termination conditions and resource limits Guardrails and permission boundaries Tracing, verification, and debugging Multi-agent orchestration and handoffs Human 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 Learn
Explain how an agentic loop differs from a single LLM call Design the goal-act-observe-evaluate cycle Build a working tool-calling agent loop in Python Create clear tool contracts and validate agent actions Handle tool errors, retries, timeouts, and invalid requests Manage state and memory across agent iterations Checkpoint, resume, and recover interrupted agent runs Control context growth and prevent context drift Define reliable success, failure, blocked, and escalation outcomes Detect repetition, oscillation, and non-progress Apply permissions, guardrails, and risk-based approvals Verify outcomes using tests, validators, and reviewer agents Trace, replay, diagnose, and repair failed runs Implement sub-agent, orchestrator-worker, and handoff patterns Apply Loop Engineering concepts with Claude Code Prepare agentic systems for safe production useClaude Code and Multi-Agent Workflows
A 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 Format
Concise, focused theory Progressive guided labs Python coding exercises Decision-based role-play activities Section quizzes Two full-length practice tests Reusable templates and checklists One 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 for
AI engineers building reliable agentic applications Software developers moving beyond basic LLM prompting Solution architects designing tool-driven AI systems Technical leads evaluating agent and multi-agent architectures Automation engineers creating controlled AI workflows Claude Code users interested in skills, plugins, MCP, hooks, automations, sub-agents, and worktreesHomepage
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