Agentic Engineering for AI — Course Outline

Welcome to the course on Context Engineering for AI. It covers the principles, techniques, and practices for building robust, reliable, and efficient AI systems — from a single well-designed prompt to autonomous agents running unattended.

2026 edition. The field reorganized substantially between 2023 and 2026. This edition follows that reorganization: it teaches four nested disciplines — prompt → context → harness → loop — and ends with a unifying architecture for agentic systems. See CHANGELOG.md for what changed from the previous edition.

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Course Structure

Nine modules. Modules 1–4 build the foundation; 5–6 make systems that act and that you can trust; 7–8 cover the frontier and the architecture that ties it together; 9 covers making structure explicit — and what happens when systems start rewriting it themselves.

• Module 1: Foundations of Context Engineering

  • Lesson 1: What is Context and Why is it Critical?
  • Lesson 2: The Evolution and Economics of Context
  • Lesson 3: Core Principles of Effective Context Design
  • Lesson 4: The Four Disciplines — A Map of the Field

• Module 2: Advanced Prompting Techniques

  • Lesson 1: The Anatomy of an Advanced Prompt
  • Lesson 2: Foundational Prompting Techniques
  • Lesson 3: Advanced Prompting Strategies

• Module 3: Retrieval-Augmented Generation (RAG)

  • Lesson 1: Introduction to Retrieval-Augmented Generation
  • Lesson 2: Building and Managing the Knowledge Base
  • Lesson 3: The Retrieval Process
  • Lesson 4: The Generation and Synthesis Process
  • Lesson 5: Agentic Retrieval and the Limits of Vector RAG

• Module 4: Optimizing the Context Window

  • Lesson 1: Mastering the Context Window
  • Lesson 2: Contextual Compression and Distillation
  • Lesson 3: Re-ranking for Relevance
  • Lesson 4: Context Engineering for Long-Horizon Agents

• Module 5: From RAG to Agents

  • Lesson 1: The Rise of AI Agents
  • Lesson 2: Designing and Integrating Tools
  • Lesson 3: Agentic Frameworks and Architectures
  • Lesson 4: MCP and Agent Skills — Packaging Capability

• Module 6: Evaluation, Testing, and Security

  • Lesson 1: Evaluating Context Quality and Agent Performance
  • Lesson 2: Testing, Tracing, and Debugging
  • Lesson 3: Security for Agentic Systems

• Module 7: The Frontier

  • Lesson 1: Multi-modal and Computer-Using Agents
  • Lesson 2: The Evolving Landscape
  • Lesson 3: The Business and Ethics of Agentic AI

• Module 8: Agentic Engineering

  • Lesson 1: The Harness — Where Agents Actually Succeed or Fail
  • Lesson 2: Loop Engineering
  • Lesson 3: Structuring AI Teams
  • Lesson 4: A Unifying Blueprint — Agentic Architecture

• Module 9: Graph Engineering and Autonomous Meta-Harness Systems

  • Lesson 1: Graph Engineering — Making Structure Explicit
  • Lesson 2: Knowledge and Memory Graphs
  • Lesson 3: Continuous Vector Memory Graphs
  • Lesson 4: Execution Graphs — Orchestration as Structure
  • Lesson 5: Standing Up the Four Graphs — A Practical Setup Guide
  • Lesson 6: Autonomous Meta-Harness Systems
  • Lesson 7: Governing Systems That Rewrite Themselves

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Runnable Code

The course ships a small, dependency-free reference harness in code/ — standard library only, no API key, no install.

cd code
python3 -m unittest discover -s tests -t .    # 134 tests
python3 examples/03_agent_loop.py             # the same model under two harnesses
python3 examples/09_meta_harness.py           # a reward hack caught by the gate

Ten auto-graded exercises turn the reading into practice — you implement, the tests grade:

python3 exercises/check.py                    # grade all ten
python3 exercises/check.py ex04               # grade one

Six optional live-model labs show what the offline mock deliberately hides — real non-determinism, real cache counters, real injection. They need an API key and cost well under $1 in total; without a key they exit cleanly. See code/labs/.

The course validates itself. Everything above, plus links, task alignment, prose code blocks, and every diagram:

python3 tools/validate_course.py              # 9 checks, the same ones CI runs

Its test suite is the course's argument in falsifiable form. Each claim has a test that fails if the mechanism is removed — that a loop must not exit on self-report, that tool errors must never raise, that injection cannot amplify absent capability, that a 20-case eval set cannot detect a 10% change, that a graph must refuse an undeclared edge type, that superseding a fact must not destroy history, and that a held-out gate catches an optimizer fitting your evaluator. See code/README.md, including its honest limitations.

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Also in This Repository

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Prerequisites

• Python, read and write. Code examples are Python; the reference harness needs 3.10+.
• Comfort with an API client — HTTP requests, JSON, environment variables.
• No ML background required. Nothing here involves training a model.
• Helpful but not assumed: having built something with an LLM API and watched it behave badly in production. If you have, several lessons will land as recognition rather than instruction.

How to Use This Course

Roughly 35–44 hours including the hands-on tasks. Each module README states its own estimate, its learning outcomes, and a short Check yourself set.

Work the modules in order; each builds on the last. Do the hands-on tasks before reading the solutions — many are designed so the intuitive answer is the wrong one, and discovering that yourself is the point.

Then do the exercises. The reading tells you a loop must not exit on the model's self-report; ex04 fails until your loop actually refuses a model that claims success without doing the work. That gap is where the learning is.

Three paths through it:

If you read only two lessons: Module 1, Lesson 4 is the map, and Module 8, Lesson 4 is the destination.

Four Things That Won't Change

Techniques churn; frameworks churn faster. These have held across every model generation so far, and they are what the rest of the course implements:

1. Context is finite and degrades. Every technique in Modules 3 and 4 exists because of this.
2. Verification must live outside the agent. A system that grades its own homework will pass.
3. Capability must be scoped. What an agent can do bounds what can go wrong — including what an attacker can make it do.
4. You can't improve what you can't measure. Without evals, every change is a guess with a confident narrator.