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.
About Today's Guest
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.
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