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Nora is a platform for building AI agents you can trust in production. Agents run on Nora, and every production answer is recorded as a trace. When an answer is wrong, Nora turns it into a signal, helps diagnose the cause, tests candidate fixes in simulation, ships approved changes as versions, and keeps the fix in memory so the same mistake does not keep coming back. The Nora loop: Build, Detect, Evaluation, Simulation, and Refine on a dashed circle, running back into Build

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The Nora improvement loop

Set up, evaluate, improve — it runs itself.
1

Build

Plug in the coding agent you already use, and build the whole stack — memory, knowledge, and guardrails, not just prompts.
2

Detect

Nora checks every answer for failures, false claims, and unsupported sources. Answers that look like failures pile up as signals.
3

Evaluation

Review the signals Nora catches, one by one. Confirm the real failures and leave feedback on why an answer was wrong. Similar confirmed failures are grouped into clusters, and become simulation-ready eval data.
4

Simulation

Nora weighs cost, accuracy, and regression risk before proposing the next fix. Run a simulation against past traces and datasets, and see the result production would give you before you ship.
5

Refine

Approve the release, and the change ships safely as a version. The fix is remembered, and accumulated beliefs get re-checked in Revalidation.

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Next steps

Core concepts

The core concepts for using Nora

Quickstart

Build an agent and get an answer in 10 minutes