E81: Build Better AI Agents (Part 2): The Five Building Blocks of Context Engineering

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After learning why AI agents fail in Part 1 (attention budget, context rot, orchestration limits), Malcolm Werchota now reveals how to build scalable, long-running AI systems using Anthropic’s framework for Context Engineering.

This episode goes beyond prompts — it’s about architecture.

Malcolm introduces the five building blocks of Context Engineering:

1️⃣ System Prompts – Define your agent’s identity, purpose, core capabilities, and quality standards.

2️⃣ Minimal Tool Sets – Stop giving 20 tools; focus on what’s essential.

3️⃣ Just-in-Time Retrieval – Only load information when it’s needed, not everything at once.

4️⃣ Long-Horizon Strategies – Extend runtime with compaction, note-taking, and delegation.

5️⃣ Examples & Patterns – Train with diverse examples, anti-patterns, and confidence scoring.

Using practical cases from Werchota.ai — like invoice automation and large-scale feedback analysis — Malcolm demonstrates how these techniques turn fragile “demo agents” into reliable production-grade systems.

Key topics: agent architecture, context optimization, compaction, token management, orchestration patterns, Anthropic Claude Code implementation, and how to scale AI workflows in production environments.

Perfect for professionals working with Claude, GPT-5, or Gemini — and anyone ready to move from prompt engineering to system thinking.

🗒️ SHOW NOTES

Episode 81, Part 2: Build Better AI Agents Through Context Engineering

Malcolm Werchota breaks down the five practical building blocks of Context Engineering, showing how to design scalable AI systems that actually think ahead — not just follow commands.

WHAT YOU’LL LEARN

The five key building blocks of Context Engineering
How to write effective system prompts that guide decision-making
Why fewer tools = better agents
How to implement Just-in-Time data retrieval
Extending AI lifespan through compaction and delegation
Using examples and anti-patterns to improve agent reasoning
Confidence scoring and note-taking for long-running tasks

KEY TAKEAWAYS

System Prompts: Define identity, purpose, and quality — short and structured (600–800 tokens).
Minimal Tool Sets: Reduce decision complexity; fewer, focused tools improve speed and reliability.
Just-in-Time Retrieval: Load only what’s needed in context; one file or task at a time.
Long-Horizon Strategies: Use compaction, external note-taking, and delegation to prevent context overload.
Examples & Patterns: Teach your agents from both successes and failures — diversity beats volume.

REAL-WORLD USE CASES

Invoice automation using Claude Code orchestration
Customer feedback summarization (10,000 → 5,000 words)
Parallel sub-agent processing (reading 10 invoices simultaneously)
Long-running report generation using compaction & note-taking

TOOLS & PLATFORMS

Claude Code (Anthropic)
Claude Sonnet 4.5 (1M-token context window)
Gemini 2.5 (1M-token context window)
ChatGPT-5 (200k-token context window)
Werchota.ai Cloud Dashboard (Episode Notes)

RESOURCES

Anthropic Research: Effective Context Engineering for AI Agents
Previous Episode: Build Better AI Agents – Part 1 (Context Engineering Basics)
Claude Code Documentation
Werchota.ai Blog: “Context Engineering in Real Workflows”