AI agent context still misses the product layer

If you spend time around AI coding tools, the conversation has clearly shifted. People are talking less about prompts and raw model quality, and more about the surrounding system: repo rules, memory, harnesses, evals, and monitoring. That shift is correct. But even the better AI agent stacks still miss one important layer: product context. Now the serious work is happening one layer above the model: OpenAI is writing about harness engineering and internal monitoring for coding agents. Anthropic is writing about effective harnesses for long-running agents and even parallel agent teams building a C compiler . Every tool ecosystem now has some version of workflow rules, repo instructions, memory files, and spec-driven coding. Taken together, these point to the same conclusion: the model is only part of the system. Reliable agentic coding depends on the surrounding stack. That's real progress. But it still leaves one missing layer. The modern agent stack is getting better at telling agents