The future of observability won’t be one proprietary AI agent. It will be thousands built by teams.

Summary

Observability vendors are increasingly betting on a universal SRE agent that combines a platform, telemetry, and natural-language investigation. The post argues that debugging depends on team-specific systems, trusted signals, failure history, runbooks, workflows, and institutional knowledge that cannot be packaged into one vendor’s agent. Agents can compare more time windows and pursue more hypotheses than human investigators, creating demand for low-latency query infrastructure and more complete, high-fidelity historical data. It proposes a more open model in which teams build specialized agents on headless observability foundations, with APIs, data storage, and an observability MCP, while persistent investigation surfaces preserve work for collaboration and reuse. Humans remain responsible for direction, judgment, and action, so the near-term future is presented as human-agent collaboration rather than autonomous incident resolution.

Takeaways

1. 1

   Agents can investigate many more possibilities than humans, such as comparing twenty time windows or issuing ten queries where an engineer might make two dashboard clicks, increasing demands on API and storage layers.

2. 2

   Agent conclusions are limited by the context supplied to them: discarded historical data, heavily sampled telemetry, and undocumented tribal knowledge can remove evidence needed for reliable investigation.

3. 3

   The proposed near-term model keeps humans in the control plane: agents gather evidence and explore hypotheses, while people provide direction, weigh risks, and decide what action to take.