# Find waste in an AI agent trace

**Use case:** 

Inspect an AI-agent execution trace for repeated transformations, expensive context, model switching, errors, and human-boundary pressure without inventing savings.

## Input

```json
{
  "traces": [
    {
      "traceId": "example-agent-run",
      "spans": [
        {
          "name": "llm.plan",
          "attributes": {
            "gen_ai.request.model": "frontier-model",
            "gen_ai.usage.input_tokens": 14000,
            "gen_ai.usage.output_tokens": 800
          }
        },
        {
          "name": "tool:search",
          "tool_name": "search",
          "tool_input": {
            "q": "current docs"
          },
          "statusCode": "OK"
        },
        {
          "name": "tool:search",
          "tool_name": "search",
          "tool_input": {
            "q": "current docs"
          },
          "statusCode": "OK"
        }
      ]
    }
  ],
  "highInputTokenThreshold": 10000,
  "maxItems": 1000
}
```

## Output

```json
{
  "traceId": {
    "label": "Trace",
    "format": "string"
  },
  "modelCallSpans": {
    "label": "Model calls",
    "format": "integer"
  },
  "toolCallSpans": {
    "label": "Tool calls",
    "format": "integer"
  },
  "totalTokens": {
    "label": "Tokens observed",
    "format": "integer"
  },
  "modelSwitches": {
    "label": "Model switches",
    "format": "integer"
  },
  "errorSpans": {
    "label": "Errors",
    "format": "integer"
  },
  "humanBoundarySpans": {
    "label": "Human boundaries",
    "format": "integer"
  },
  "highInputTokenCalls": {
    "label": "High-input calls",
    "format": "integer"
  },
  "exactRepeatedTransformations": {
    "label": "Exact repeats",
    "format": "integer"
  },
  "hypotheses": {
    "label": "Experiments",
    "format": "array"
  }
}
```

## About this Actor

This example demonstrates how to use [Agent Trace Efficiency Auditor](https://apify.com/firstrate/agent-trace-efficiency-auditor.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/firstrate/agent-trace-efficiency-auditor.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/firstrate/agent-trace-efficiency-auditor.md

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).