incident_management · saas · workflow
Fuzzy Labs builds an autonomous SRE agent using FastMCP and Claude
SRE teams spend significant time manually chasing root causes of production incidents by sifting through logs, inspecting Kubernetes services, and hunting for errors before communicating findings to the wider team.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · CloudWatch detects error
AWS CloudWatch detects a CRITICAL 500 error and alerts the agent.
Tools used
FastMCPClaudeGitHub MCP ServerSlack MCP ServerCloudWatch
Outcome
The team implemented a custom MCP client running a fully autonomous SRE agent that diagnoses production incidents end-to-end and posts findings to Slack; tool caching reduced cost per diagnosis by 83%.
What failed first
Relying on Claude Desktop and Cursor as MCP clients was insufficient for fully autonomous workflows: Anthropic gate-kept token usage and the tools required users to accept tool calls on the agent's behalf, preventing full autonomy.
Results
Cost replaced83%
Grounding & classification
Source type: technical build writeup
21 fields verified against source quotes, 2 dropped as unverifiable.
agentic workflowai agentsummarizationbuilder submittedfailure mode describedmetric backedworkflow describedsoftwarecost reductiontechnical build writeupincident managementit supportagentic task executionautonomous resolutionmonitor detect alert