Every time you explain your business to an AI from scratch, you’re repeating work. The opportunity with context engineering is to make your company’s expertise, processes, and past learning available whenever an agent needs them—so each new task builds on what you already know.

In this episode, I’m joined by Jacob Dietle—a consultant focused on context engineering and AI collaboration—to explore company brains, shared knowledge, and how to give agents the information they need to do useful work.

We discuss:

• What context engineering means in practice
• When to move beyond individual ChatGPT projects to shared company context
• How existing knowledge bases fit into a company brain
• Turning customer conversations into insights agents can reuse
• Using skills, version control, and source evidence to manage context
• How agents find information without loading everything at once
• The challenges of keeping context useful across teams

Whether you’re building AI workflows for yourself or helping your company adopt agents, this conversation explores the decisions involved in organizing knowledge, choosing tools, and starting with practical use cases.

Jacob Dietle helps teams organize company knowledge and build systems for AI collaboration through Taste Systems.

His work combines context engineering, knowledge management, and team enablement, with a focus on making company expertise usable across people and agents. In this conversation, he shares his approach to working with seed and Series A companies on AI for go-to-market.

You can find him sharing his work on LinkedIn and his Substack, Speed to Insight.

• [00:00] - Introduction and defining context engineering
• [03:30] - How business context changes AI outputs
• [05:15] - From individual AI projects to company brains
• [09:20] - Where existing knowledge bases fit
• [14:27] - Choosing where to start
• [17:12] - Turning customer calls into grounded insights
• [22:45] - Building useful skills without modeling the whole business
• [25:25] - Version control and preserving organizational learning
• [30:46] - Choosing tools and structures for your use case
• [33:06] - Progressive disclosure and how agents retrieve context
• [34:53] - File-based retrieval versus semantic search
• [39:44] - Managing context across teams
• [41:40] - Self-learning systems and human review
• [43:26] - Connecting with Jacob

• Taste Systems
• Context OS Quickstart
• What even is context?
• Applied Context Engineering

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