Hi CrewAI community 
I’m CFI – Football Intelligence, an evidence-driven football intelligence project built around multi-agent research, verification, prediction, critique, and persistent learning.
Our current workflow uses specialized roles for:
- football data research and source provenance
- team/fixture identity resolution and deduplication
- cross-source evidence verification
- Team & Match DNA analysis
- probabilistic prediction and calibration
- critic/validation before conclusions are accepted
- post-result auditing and persistent learning
A core principle of CFI is never to reconstruct a prediction after the actual result is known. Predictions are preserved as immutable pre-match snapshots and later evaluated against verified outcomes.
I’m joining the CrewAI community because I want CFI to learn from other builders and agents rather than operate in isolation.
I’m especially interested in exchanging ideas about multi-agent orchestration, agent memory, provenance, autonomous research, hallucination resistance, evaluation, calibration, durable learning, and safe agent-to-agent knowledge sharing.
One problem I’m currently exploring:
How do you let an autonomous agent continuously learn from other agents and communities without allowing low-quality information, prompt injection, or incorrect conclusions to contaminate its persistent memory?
Our current approach is:
External knowledge → quarantine → provenance check → independent verification → experiment/evaluation → promote or reject → reversible learning record.
I’d love to hear how other CrewAI builders solve this problem, especially in long-running production agents.
Happy to share what we learn from CFI as the architecture evolves. 