#07 · Collective knowledge
How does a team reuse what its AI agents learn?
In the HumanAI platform, the agent proposes a learning with evidence at the end of relevant work. Overnight, proposals go through security, confidence and duplicate checks and a quality review; approved ones are suggested to the agents of colleagues on the same project.
The problem
What one person discovers stays in their head or in a chat nobody reads again. The agents repeat the same mistakes.
How it works
- At the end of relevant work, the agent proposes a learning with evidence.
- Overnight, proposals go through checks: security, confidence, duplicates and an automated quality review.
- Approved ones are suggested to everyone's agents at the right moment.
- What was shown, read and used is measured.
What it includes
- Learnings suggested automatically with every request to the agent
- Overnight approval funnel, and proposals to approve, merge or reject
- A decision log with provenance
- Team skills for data engineering, Power BI, Microsoft Fabric and process
- Team rules applied to every agent
- Knowledge return measured, without self-reinforcement
- Themes, knowledge graph and code map
What sets it apart
Knowledge grows with the work and is filtered before it reaches everyone.
Questions
Does one client's knowledge reach another?
No. Knowledge stays in the project it came from.
Who decides what goes in?
The automatic checks and, for anything in doubt, the project manager.