Loop Engineering for Agentic AI

About this course

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 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 checkpoints

The 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 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

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