A huge onboarding prompt is an understandable response to a real problem: a new coding agent begins with none of the context a teammate would accumulate over months.
It is also a poor long-term interface.
The longer the prompt becomes, the harder it is to know what is current, what is important for this task, and what should have been a real document instead. The agent receives a wall of context before it has even asked a question.
Try a three-layer onboarding model instead.
Layer 1: Instructions in the repository.
Keep stable rules in CLAUDE.md, AGENTS.md, and project docs: test commands, style, ownership boundaries, and security rules.
Layer 2: A small first-task checklist.
Tell the agent which docs to read and what it must verify before changing code.
Layer 3: Recallable working knowledge.
Let the agent retrieve relevant decisions, pitfalls, and recent handoffs when the task calls for them.
This creates an onboarding flow that pulls context on demand instead of shoving every past lesson into every session.
The practical check is simple: give a new agent a normal task and ask it to explain the relevant architecture decision before it writes code. If it cannot find the answer, either the source doc is missing or the shared-memory habit is not established yet.
The point is not to eliminate human judgment. It is to stop spending human judgment on the same orientation speech over and over.
StremAI can act as the third layer through MCP: connected agents recall project knowledge when it is relevant, while your repository remains the source of truth for instructions and code.
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