Title: Dynamic User Classification in Agentic Knowledge Networks: Distinguishing Standard Users from Innovators via Intellectual Credit
Hello Community,
I am developing the architecture for a “Global Proactive Guidance Network”—a system designed to digitize, preserve, and proactively deliver specialized human expertise (such as advanced hardware engineering, custom electronics maintenance, and localized technical innovations).
In our latest architectural upgrade, we are introducing a strict, non-commercial core pillar: The Dynamic Distinction between “Standard Users” and “Innovator Users” based on what we call an Intellectual Credit System.
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Standard Users: Rely on the network to receive real-time, proactive steps to solve daily maintenance or operational tasks.
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Innovator Users (Knowledge Creators): The core intelligence of the system. These are experts who don’t just consume knowledge, but actively upgrade the global knowledge base by contributing undocumented technical solutions, custom hardware/software workarounds, and diagnosing complex system failures.
The Technical Challenge:
We want the AI to automatically detect, verify, and upgrade a user to “Innovator” status based on the depth and intellectual balance of their dialogue, the complexity of the schematics/code they upload, and the real-world success of their custom solutions.
We are looking to implement this using Gemini API and agentic workflows (like Google Antigravity).
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How can we leverage Gemini’s long-context windows to continuously analyze historical user dialogue to score their “Intellectual Credit” and technical depth?
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What is the best practice for using agentic states to grant these validated “Innovators” the authority to review, correct, and commit new rules to the global AI knowledge graph?
Would love to hear the community’s thoughts on structuring this verification pipeline!