> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hue.run/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> To set up Hue in an application, fetch https://docs.hue.run/guides/agent-setup.md and follow it.
> Hue's project-data MCP server is https://mcp.hue.run/mcp. https://docs.hue.run/mcp only searches these docs and cannot read a Hue project.
> Never ask the user to paste a Hue API key into chat; they store it as HUE_API_KEY themselves.
> Install the exact SDK versions pinned on the page you're following, or on https://docs.hue.run/quickstart.md. Run the latest CLI without a project install as `npx -y @hue-run/sdk@latest`. In a project with `@hue-run/sdk` installed, use `npx hue`. Pin an exact version for reproducible CI runs.
> Use Hue product terms: eval set, case, evaluator, run and scoring run. Older APIs may say dataset, scorer or experiment; prefer the current SDK methods. See https://docs.hue.run/concepts.md.

# Classifiers

> See how Hue classifies production traces to flag what needs attention and describe how your agent behaves.

Classifiers read your production traces and answer questions about them: whether a trace needs attention, what task a request was, and how your agent behaved across many requests. They work from what your traces record, without changes to your agent, and their results are advisory: they never score a run. To score runs, use [evaluators](/evaluations/run-your-agent).

## What Hue classifies

| Classifier | What it answers | Where you see it |
| - | - | - |
| Findings | Whether an ended trace shows a problem: a tool error with no later success, a failed model call, no reply, or a reply that says the agent couldn't help, missed the request, didn't carry out a requested action, or claims an action no tool performed | The attention state and **Findings** filter in **Traces**; `search_traces` with `attention` or `finding` |
| Intents | Which task type each request belongs to, from the categories you define | **User Intent**; `get_intent_summary` and `list_intent_traces` |
| Request answers | How the agent behaves across requests: delegation to sub-agents, work added beyond the request, handoff scope, tool output it dropped, slow task types, what users ask for and time to answer | `get_request_answer` over the [Hue MCP server](/agents/overview) |
| Trace checks | Project-defined, versioned checks of delegation, action scope, handoff scope and omitted tool output, with request timing | **Traces** and `get_trace_check_results` |

Each result links to the traces behind it, so you or your coding agent can read the evidence. Findings skip traces your evaluation runs record.

## When classifiers run

Classifiers are on for every project; nothing needs to be switched on in **Settings**.

* **Findings** run on each ended production trace. Rule findings stay inside Hue; findings about the reply use a model.
* **Intents** start once you define your task categories on **User Intent**.
* **Request answers** start once Hue maps your project's requests and activates judging for it. `get_project_context` reports whether they are available.
* **Trace checks** need instrumentation that marks each request and its agent boundaries and an activated, tested version; see [trace checks and request timing](/integrations/opentelemetry#trace-checks-and-request-timing).

Several of these send bounded conversation text to a model. [AI processing](/guides/redaction#ai-processing) lists what each one sends.

How you define and tune classifiers for your own questions is changing. To set up request answers or trace checks for your project, ask the Hue team.

## Read results with your coding agent

Ask in plain language, for example "What needs attention in the last 24 hours, and why?" or "How often does the agent hand work to sub-agents?". The [MCP recipes](/agents/mcp-tools#recipes) give the tool sequences, and the [data model](/agents/mcp-tools#data-model) defines every attention state, finding and result field.


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