Most agent systems are static: fixed prompts and fixed rules that never improve. EverMind treats the agent workforce like a self-improving organization - an Orchestrator routes work to specialist agents (Research / Data Quality / Rule Evolution), each returns structured outputs, and the framework continuously learns from real outcomes to govern and refine its own rules.
Inspiration & What it does
EverMind was built to solve a real pain: most agent systems are static, fixed-prompt pipelines that never get smarter. It turns the agent workforce into a self-improving organization.
How we built it
Built with Google ADK as the orchestration backbone and Gemini 3.5 Flash for semantic reasoning, deployed to Google Cloud Run for serverless scaling.
Challenges we ran into
Coordinating multiple specialist agents and converting real measured outcomes back into rule-credit updates without polluting the core.
Accomplishments that we're proud of
A governance layer that validates agent outputs, plus a feedback loop that converts measured outcomes into rule-credit updates - so the system genuinely improves over time rather than just appearing to.
Built With
- data-quality-governance
- fastapi
- gemini
- google-adk
- google-cloud
- multi-agent-orchestration
- python
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