Cybersecurity & Content WriterBlogger at Cyber Safety Zone
Helping freelancers and small businesses stay secure in the digital age. I write about AI risks, cyber threats, and budget-friendly security
Really interesting experiment. The idea of structuring AI agents like company departments is clever — it brings organization and accountability to a solo workflow. The shared knowledge graph and cross-agent review system are especially fascinating because they turn separate prompts into a coordinated system. Curious how this scales as the products and data grow.
Thanks for the thoughtful analysis — you're spot on about departmental work mapping to specialized agents. The clear boundaries and handoff points are exactly why this works. Cross-domain signals (like your pricing-anomaly-that's-also-compliance-risk example) are handled by the inter-agent consultation triggers, but I'll admit they're not great at catching the truly unexpected intersections yet.
To answer your Improver question: it's scheduled, not triggered. It runs monthly via a /improve-agents prompt. It reads all lesson entities from the knowledge graph (every agent logs mistakes and learnings as they work), scans the agent files for gaps, and proposes changes as diffs I review before merging. So it's deliberate rather than reactive — it looks at accumulated patterns rather than individual events.
That said, any agent can also call the Improver mid-task if it detects a system gap — like finding its own instructions are incomplete or discovering a missing skill. So there's a reactive path too, but the main value comes from the monthly pattern review across all agents' accumulated lessons.
Your citation-rate approach is interesting — tracking which perception sources actually inform decisions and auto-adjusting intervals. That's a feedback signal we don't have. Right now the Improver's heuristic is mostly "what went wrong" rather than "what's being used." Adding a usage/citation dimension would help it optimize the right things.
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Really interesting experiment. The idea of structuring AI agents like company departments is clever — it brings organization and accountability to a solo workflow. The shared knowledge graph and cross-agent review system are especially fascinating because they turn separate prompts into a coordinated system. Curious how this scales as the products and data grow.
Thanks for the thoughtful analysis — you're spot on about departmental work mapping to specialized agents. The clear boundaries and handoff points are exactly why this works. Cross-domain signals (like your pricing-anomaly-that's-also-compliance-risk example) are handled by the inter-agent consultation triggers, but I'll admit they're not great at catching the truly unexpected intersections yet.
To answer your Improver question: it's scheduled, not triggered. It runs monthly via a /improve-agents prompt. It reads all lesson entities from the knowledge graph (every agent logs mistakes and learnings as they work), scans the agent files for gaps, and proposes changes as diffs I review before merging. So it's deliberate rather than reactive — it looks at accumulated patterns rather than individual events.
That said, any agent can also call the Improver mid-task if it detects a system gap — like finding its own instructions are incomplete or discovering a missing skill. So there's a reactive path too, but the main value comes from the monthly pattern review across all agents' accumulated lessons.
Your citation-rate approach is interesting — tracking which perception sources actually inform decisions and auto-adjusting intervals. That's a feedback signal we don't have. Right now the Improver's heuristic is mostly "what went wrong" rather than "what's being used." Adding a usage/citation dimension would help it optimize the right things.