Learnings from Building AI Agents

10 months ago

Initial AI code review agent was too noisy, generating many low-value comments and false positives.
Three major architecture revisions reduced false positives by 51% without sacrificing recall.
Initial single-agent architecture led to excessive false positives and loss of user trust.
Explicit reasoning logs were introduced to trace AI decision-making and improve accuracy.
Toolkit was streamlined to essential components, improving precision.
Specialized micro-agents replaced generalized rules, enhancing focus and efficiency.
Real-world outcomes included fewer false positives, reduced comments per PR, and smoother review processes.
Key lessons: require explicit reasoning, simplify toolset, and specialize with micro-agents.

Hasty Briefsbeta