May Walter - From Blind Spots to Merged PRs: Runtime Intelligence for Continuous Agentic Performance
The talk details implementing a runtime intelligence layer for coding agents to enable continuous performance optimization in mature codebases. The approach moves beyond reactive bug fixing by running sensors in production to capture deep forensic context (e.g., function execution frequency, failure modes). This allows agents to proactively surface high-ROI fixes—such as N+1 queries or missing database indexes—scored by complexity and impact, enabling tech leads to prioritize improvements before sprint planning.
Key takeaways
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Automating the Investigation Phase
17:52
Instead of waiting for performance degradation to become a crisis (a 'leaky bucket' problem), the goal is to automate the investigation phase. This allows teams to identify potential optimizations and their estimated impact/cost without dedicating unpredictable engineering time.
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Prioritizing Impact over Task Completion
25:30
The focus shifts from merely generating pull requests (PRs) to identifying the highest impact, lowest risk changes. The system must provide human-readable context that justifies attention, preventing 'PR fatigue' from agents.
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Defining Business Context
22:00
Effective agentic workflows require more than just code analysis; they need business context. The system must map production activity (endpoints, event consumers) to function-level context to understand the true impact of a potential fix.