E80: Build Better AI Agents: Context Engineering Over Prompts (Pt. 1)

Your AI agents work—but they’re not smart. They follow instructions, yet fail on edge cases, forget context mid-task, and need constant supervision.Malcolm Werchota reveals why your invoice automation, podcast metadata generation, and business workflows keep breaking down—and why it’s not the AI’s fault. The missing piece is context engineering, a concept most people have never heard of.In this two-part series, Malcolm breaks down Anthropic’s groundbreaking research on how to build AI agents that actually think ahead. Learn why prompt engineering is no longer enough, how attention budget silently kills your automations, what context rot does to long-running tasks, and how the orchestrator pattern allows AI agents to spawn helper agents on demand.You’ll hear how Malcolm’s team cut invoice processing time from 45 minutes to zero human intervention—and why feeding your AI more data can actually make it dumber. This episode isn’t about magic prompts. It’s about designing the entire environment your AI operates in.Key topics: AI agent automation challenges, context window vs. attention budget, why mega-prompts fail, orchestrator pattern design, system prompt architecture, tool-calling strategies, and scalable AI workflows.Perfect for professionals implementing Claude AI, automating business processes, or frustrated with unreliable AI agents. Malcolm’s “Ship First, Study Later” approach means real implementation—not theory.Part 2 dives into advanced system prompts, minimal tool sets, and managing long-running tasks without context explosion.WHAT YOU’LL LEARNWhy functional AI agents still fail at business automationThe difference between prompt vs. context engineeringHow attention budget and context rot sabotage your workflowsThe orchestrator pattern: when agents build their own helpersReal-world cases: invoices, podcasts, and process automationWhy mega-prompts make AI dumber—and what to do insteadAnthropic’s context engineering frameworkHow to design information architecture for Claude and other LLMsTOOLS & PLATFORMSClaude Code (Anthropic)Claude Sonnet 4.5 (1M token window)Gemini 2.5 (1M token window)ChatGPT (100–200k token window)10 Valley OS (TenVOS) – context engineering case studyRESOURCESAnthropic Research: Effective Context Engineering for AI AgentsPrevious Episode: Building Claude Code AgentsPrevious Episode: 10 Valley OS – Context Engineering in ActionMALCOLM’S KEY INSIGHTS“It’s like having an employee who follows orders perfectly—but never takes initiative or thinks ahead.”“Context engineering manages everything the model uses: system instructions, tools, message history—not just the prompt.”“The challenge now isn’t crafting perfect prompts. It’s curating the information within the model’s limited attention budget.”“Don’t feed it a billion files. Use the smallest, clearest, highest-signal inputs possible.”COMING IN PART 2Advanced system prompt structureMinimal tool sets for reliabilityHandling long-running tasks without context explosionPractical implementation blueprints🔗 WHERE TO FIND MALCOLM WERCHOTALinkedIn → linkedin.com/in/malcolmwerchota Website → werchota.ai YouTube → youtube.com/@werchota X → x.com/malcolmwerchota Facebook → AI Cookbook by Malcolm Werchota Instagram → @malcolmwerchotaai TikTok → @malcolmwerchota📧 Get in touch: Questions, feedback, or transformation stories → [email protected] Episode ideas → [email protected]🎓 Upgrade your AI skills: Check out the AI Fit Academy, Malcolm’s hands-on program that gets professionals shipping working AI workflows by Week 2—or your money back. Learn more → werchota.ai/ai-fit-academy