The Context Pyramid: A PM’s Framework for AI Agent Context Engineering
Why Your AI Agent Breaks: A Diagnostic Framework for Product Managers
TL;DR Context engineering is the skill that determines whether your AI agent is useful or a liability. Not model choice. Not prompt phrasing. Context. Every agent has four distinct context layers: Identity, Knowledge, State, and Task. Each breaks in a different way, for a different reason, on a different schedule. The Cadence Model (the PwA contribution here) tells you who touches each layer and when. Rarely. Occasionally. Per turn. Per task. The Diagnostic Loop maps agent failure symptoms to the exact layer that’s broken. No more guessing.
The Context Window Is Not Your Problem
Many users think that context problems are about size.
Bigger window, better agent. Use the 1M token model, problem solved.
Wrong.
The problem is not how much fits, it’s what we put in, in what order, with what structure, and how often we update each part.
Andrej Karpathy explained it like this in June 2025:
Context engineering is the delicate art and science of filling the context window with just the right information for the next step.
Not all information. Not the most information. The right information.
Most of the writing on this topic was produced by engineers for engineers. System design diagrams. RAG architecture tutorials. Vector DB benchmarks. Useful. But none of it is written from the PM seat, where the questions sound different.
Why does my agent suddenly act like it forgot the product strategy we discussed three sessions ago?
Why does it keep generating outputs we already rejected?
Why does a fresh agent outperform the one that’s been running for a week?
These are PM questions. And they require a PM-shaped answer.
That is what the Context Pyramid gives you.