Everyone's talking about AI adoption. Almost nobody has the real numbers. Help us change that — and get the full report 👉 Engineers | Leaders

Project Manager

2 courses · ~6 hours· 1 certification available

phase 1 — How AI features work

Understand what your team means when they say 'tool calling' or 'agent loop.'

[ ] Understanding Tool Calling

When your team says a feature needs 'tool calling' or 'an agent loop,' you'll know what they mean, how long it takes, and what can go wrong.

4 sections · 10 lessons
  • [ ] Foundations (2 lessons)
  • [ ] How Models Decide (2 lessons)
  • [ ] Advanced Patterns (3 lessons)
  • [ ] Security and Production (3 lessons)
[→ start course]

phase 2 — Why AI projects go over budget

Understand the hard limits that aren't obvious from demos.

[ ] Context Engineering

The 'real context size' section explains why AI features take longer than estimated — the technology has hard limits that aren't obvious from demos.

8 sections · 25 lessons
  • [ ] Tokens and Inference (2 lessons)
  • [ ] The Real Size of Your Context Window (4 lessons)
  • [ ] Anatomy of the Messages Array (5 lessons)
  • [ ] Dynamic Allocation: Tool Calling (2 lessons)
  • [ ] The Ralph Wiggum Loop (3 lessons)
  • [ ] Sub-Agents: Managed Runtimes for AI (3 lessons)
  • [ ] Message Passing: The Erlang OTP of AI (3 lessons)
  • [ ] Context Management Strategies (3 lessons)

certification: Certified Context Engineer

[→ start course]

key terms

Tool Calling·Context Window·Context Engineering·Agent