Launch operations and reliability readiness¶

Problem¶

The MCP analyst agent exposes Dataface capabilities to AI agents via an MCP server and includes an evaluation framework for measuring agent quality, but neither system has operational readiness for production. There is no telemetry on MCP tool call latency, error rates, or guardrail violation frequency. If the agent generates incorrect SQL, exceeds token budgets, or the MCP server silently drops requests, nothing detects the degradation. Support ownership for agent quality regressions is undefined, and there is no incident playbook for scenarios like a guardrail bypass producing dangerous queries or an eval regression shipping undetected. At launch, AI agent failures would be invisible until users report wrong answers.

Launch operations and reliability readiness¶

Problem¶

Context¶

Possible Solutions¶

Plan¶

Implementation Progress¶

Review Feedback¶