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Copy file name to clipboardExpand all lines: docs/product/insights/ai/agents/dashboard.mdx
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-**Traffic**: Shows agent runs over time, error rates, and releases to track overall activity and health
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-**Duration**: Displays response times for your agent executions to monitor performance
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-**Recommended Issues**: Highlights recent errors and problems that need attention, including agent failures and exceptions
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-**LLM Generations**: Shows the number of language model calls with breakdowns by specific models (claude, 4o-mini, etc.)
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-**Tool Usage**: Shows which tools your agents use most frequently
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-**Token Usage**: Tracks token consumption over time with breakdown by model
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-**Issues**: Highlights recent errors and problems that need attention, including agent failures and exceptions
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Underneath these widgets are tables that allow you to view data in more detail:
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Underneath these widgets are three segments that offer focused widgets and a detailed distribution table:
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-**Traces**: Recent agent runs with duration, errors, number of LLM and tool calls and token usage
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-**Models**: Traffic, duration, token usage and errors grouped by model
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-**Tools**: Number of requests and their usual durations grouped by tool
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-**Traces**: LLM Calls, Tokens Used, and Tool Calls widgets, as well as recent agent runs with duration, errors, number of LLM and tool calls, and token usage
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-**Models**: Model Cost, Tokens Used, and Token Types widgets, as well as all used models with durations and token usage
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-**Tools**: Tool Calls and Tool Errors widgets, as well as all used tools with durations and errors
Copy file name to clipboardExpand all lines: docs/product/insights/ai/agents/index.mdx
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description: "Learn how to use Sentry's AI Agent monitoring tools to trace and debug your AI agent workflows, including agent runs, tool calls, and model interactions."
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<Includename="feature-stage-beta.mdx" />
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Sentry's AI Agent monitoring tools help you understand what's going on with your AI agent workflows. They automatically collect information about agent runs, tool calls, model interactions, and errors across your entire AI pipeline—from user interaction to final response.
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