sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

Observability (for AI systems)

Observability (for AI systems): Logging and monitoring of inputs, model and tool calls, outputs, human decisions, latency and cost so AI behaviour can be traced and improved.

Why it matters

Without observability, an AI system fails silently: quality drifts, costs creep up and nobody notices until a customer complains. The minimum is logging every input, output, model version and decision; a dashboard of volume, quality, latency and cost; and alerts when any of them moves outside agreed limits.

  • Evals (evaluation suite): A repeatable test set of real cases with known good outcomes, run against an AI system before each release to measure quality and catch regressions.

In practice

Every Sudo Sapient build ships with this in place before go-live, because it is part of what separates a demo from a production system. See how the studio works.

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