AI BriefingSystem over prompt
Key point
The solution to agent problems is not the prompt but the operating structure.
Details
This piece is based on the experience of actually running a 6-agent system, entrusting it with Gmail, Gcal, financial data, budgets, weekly research, filling out work forms, and even sleep/health data.
The core message is that swapping models or tweaking prompts alone has limits, and actual performance is determined by architecture and systems engineering.
The first method that actually worked was separating the prompt by role:
- durable identity file: fixes who the agent is and what it can access
- operating model: defines routing, escalation, and handoff rules
- task-specific objective: the task goal that changes with each execution
It also states that instead of writing one giant manual prompt, it's important to compile modularized source files together at runtime. This way, policy changes are applied in bulk to all subsequent executions, reducing the problem of context being unnecessarily mixed together.
In summary, the claim is that agents stalling, forgetting, and getting tangled up is mostly not a matter of "a smarter model," but a problem of context management and operating structure design.
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