Most agent tutorials teach you to build agents that work on the happy path.

Ticker is valid. API returns 200. Model doesn't hallucinate. Tool schema hasn't changed. Input is well-formed. Everyone's polite.

Then you ship it.

And at 3am on a Tuesday, your agent is stuck in a loop calling the same broken tool 47 times, burning through your API budget, returning confidently wrong answers to your users, and you're the one who has to fix it.

This course is about the other 90% of the job.

I'm going to teach you how to build an agent that detects its own failures, diagnoses why it failed, rewrites its own broken tool calls, modifies its own system prompt, switches strategies when one approach stops working, remembers its mistakes so it doesn't repeat them, and knows exactly when to escalate to a human instead of pretending it has the answer.

Not error handling. Genuine self-correction.

Over 14 modules, you'll build every component from scratch in Python —

• The execution monitor,

• The silent failure detector,

• The diagnostician,

• The tool repair layer,

• The prompt self-modifier,

• The strategy switcher,

• Session and long-term memory,

• Recovery scoring,

• Graceful degradation, and

• human escalation.

Then at the end, you'll run it against an adversarial test suite — 20 deliberate attacks designed to break your agent in every way agents break in production. Typos. Flaky tools. Prompt injections. Contradictory instructions. Hallucination bait. Poisoned memory.

If your agent recovers from all 20, you ship it.

By the end of this course, you will have built something most production teams haven't figured out yet — an agent that gets harder to break every single time it fails.

Your agent will fail. Teach it to fix itself.

1. Agents that survive production.

2. Self-Healing AI Agents in Python.

3. Build an agent that gets harder to break every time it fails.
   

   Let's build it.