From AI agent prototype to product: Lessons from building AWS DevOps Agent | Amazon Web Services
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From AI agent prototype to product: Lessons from building AWS DevOps Agent | Amazon Web Services
"At re:Invent 2025, Matt Garman announced AWS DevOps Agent, a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance. As a member of the DevOps Agent team, we've focused heavily on making sure that the "incident response" capability of the DevOps Agent generates useful findings and observations. In particular, we've been working on making root cause analysis for native AWS applications accurate and performant."
"Under the hood, DevOps Agent has a multi-agent architecture where a lead agent acts as an incident commander: it understands the symptom, creates an investigation plan, and delegates individual tasks to specialized sub-agents when those tasks benefit from context compression. A sub-agent executes its task with a pristine context window and reports compressed results back to the lead agent. For example, when examining high-volume log records, a sub-agent filters through the noise to surface only relevant messages to the lead agent."
AWS DevOps Agent targets automated incident resolution and proactive prevention to improve reliability and performance. The system emphasizes accurate, performant root cause analysis for native AWS applications. The architecture uses a lead agent that functions as an incident commander and specialized sub-agents that execute delegated tasks with compressed context. Sub-agents operate with pristine context windows and return compressed results so the lead agent can form concise, actionable findings. High-volume telemetry such as logs can be filtered by sub-agents to surface only relevant messages. Continuous improvement relies on mechanisms that measure failures and guide enhancements, beginning with evaluations and visualization tools.
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