Always-on agents run production without the on-call tax — Justin Smith, Resolve AI
The talk introduces the concept of 'always-on agents' designed to automate operational tasks in complex production environments, thereby reducing the burden of manual on-call work. While CI/CD handles baseline checks well, the biggest gap is monitoring non-alerted changes—such as feature flag rollouts or infrastructure updates—that require continuous context understanding. Background agents can run autonomously (on schedules, events, or messages) to perform deep analysis, root cause investigations, and proactive health checks across systems like Kafka pipelines.
Key takeaways
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The Operational Bottleneck
2:05
A significant portion of an engineer's time (estimated at 70%) is spent running code in production—maintaining platforms, debugging incidents, and handling alerts—rather than writing it. This complexity increases with the velocity of change driven by AI.
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Background Agents vs. Incident Response
10:40
While on-call agents handle immediate alerts and incidents, background agents address the 'long tail' of operational work—such as routine health checks, summarizing handoffs, or watching for subtle performance drifts (e.g., P99 drift) that don't trigger an alert.
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The Importance of Context
12:00
Execution is easy; production context is hard. The value lies in building knowledge systems that can determine if a metric 'smells wrong' or understand the causal chain impact of a change, rather than just loading a dashboard.