AI Agent Context Still Misses the Product Layer

The article discusses how the AI agent development ecosystem has evolved, focusing more on the surrounding system rather than just the model quality. However, it argues that even the better AI agent stacks still miss an important layer - the product context.

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The article discusses the evolution of the AI agent development ecosystem, where the conversation has shifted from focusing on prompts and raw model quality to the surrounding system - repo rules, memory, harnesses, evals, and monitoring. This shift is seen as correct, as the serious work is now happening one layer above the model. However, the article argues that even the better AI agent stacks still miss an important layer: the product context. The modern agent stack is getting better at telling agents how to work, but it still does a poor job at telling them what the product must continue to do. An agent can follow every repo rule, use the right harness, pass the tests, and still break the product by changing something that looked reasonable from the code alone. This is because most product decisions are not explicit in the repo, and the agent sees implementation but not the product intent, trust level, or business significance. The missing layer is 'product truth' - capturing the confirmed product behaviors, forbidden states, deliberate edge cases, and areas still being explored. Without this layer, agents are forced to infer product meaning from implementation details, leading to potential product drift.