We built and deployed an AI Agent at Salesforce designed to triage production alerts using service logs. After months of iteration, we achieved an accuracy level that earned the trust of our DevOps personnel. Today, the agent reclaims approximately 20% of weekly engineering capacity for the teams using it.
Here are our top 5 takeaways for building agents that perform consistently at high accuracy: 1. Specialization beats generalization: For high-accuracy tasks, purpose-built solutions outperform generic models every time. 2. Provide "Proof of Work": Make it effortless for humans to verify the agent's logic. Transparency builds trust. 3. Minimize friction: We used Slack as the primary interface. Don't require local setups to boost adoption and usage 4. Build in public: Operating in shared channels allows for seamless human-agent cooperation (and agent-to-agent orchestration). 5. Hosted > Local: Local scripts are great for individuals, but standardized, team-wide automation requires a hosted environment to scale.
