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docs(falcon-ai): add Imagine feature page#641

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docs(falcon-ai): add Imagine feature page#641
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docs/falcon-imagine

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@SuhaniNagpal7
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Summary

  • New page: src/pages/docs/falcon-ai/features/imagine.mdx — covers what Imagine is (custom dashboard builder for traces), how to open it, build widgets via chat, save reusable views, and how dynamic analysis re-runs LLM widgets on every trace.
  • Sidebar entry added in src/lib/navigation.ts under Falcon AI → Features (between Using Falcon AI and Skill Builder).
  • 4 light-theme product screenshots captured at public/screenshot/product/falcon/10.png13.png: empty canvas with suggested prompts, dashboard with widgets, Save View popover (cropped), and dynamic-analysis skeleton loader.

Test plan

  • pnpm dev and verify the page renders at /docs/falcon-ai/features/imagine
  • Sidebar shows Imagine under Falcon AI → Features
  • All four screenshots load (no broken images)
  • Internal links work: Dynamic analysis anchor, Next Steps cards to chat / skills / mcp-connectors
  • No em dashes in prose; <Steps> and <Note> components render correctly

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Can you take image of something more challenging

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showing flow is like already there can you try like show me latency split or some metric split or some new analytics page view?

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---
title: "Imagine"
description: "AI-powered trace visualization builder. Chat with Falcon AI to turn any trace into an interactive dashboard, then save the view to replay on every future trace."
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replay on every future trace?


## About

Imagine is a custom dashboard builder for traces. You describe what you want to see in plain English, Falcon AI assembles the widgets against the current trace, and the resulting dashboard can be saved as a template that applies to any future trace.
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It is not only dashboard builder its custom view builder

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applies to any traces (future or existing traces) in the respective project


Imagine is a custom dashboard builder for traces. You describe what you want to see in plain English, Falcon AI assembles the widgets against the current trace, and the resulting dashboard can be saved as a template that applies to any future trace.

A dashboard is a layout of widgets: charts, tables, metric cards, span trees, markdown summaries. Building one by hand means writing custom code or running queries one at a time. Imagine replaces that with chat, then makes the result reusable so the same trace investigation can run on every trace your team opens later.
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View can be a it not necessarily "is layout"


![Open the Imagine tab](/screenshot/product/falcon/10.png)

On a fresh trace, suggested prompt chips appear on the empty canvas: *"Show me where the latency is"*, *"Visualize token usage by span"*, *"Show the agent execution flow"*, *"What's the cost breakdown?"*, *"Compare the LLM calls"*, *"Summarize this trace"*. Click a chip to send it to the chat panel, or type your own request.
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"Show me where the latency is" show or filter no?

| **Tables** | Data table, key-value list |
| **Metrics** | Metric card, heatmap |
| **Trace structure** | Span tree, timeline, agent graph |
| **Data inspection** | JSON tree, code block |
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Can you verify if this works? I doubt that is it this way

| **Metrics** | Metric card, heatmap |
| **Trace structure** | Span tree, timeline, agent graph |
| **Data inspection** | JSON tree, code block |
| **Annotations** | Markdown, screenshot |
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I dont think it works for this also

A canvas built for one trace becomes more valuable when it can replay on any trace. Save it as a view to make it reusable.

<Steps>
<Step title="Build the dashboard">
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its a view no or canvas?

<Step title="Build the dashboard">
Use the chat panel to assemble the widgets you want.

![Build a dashboard](/screenshot/product/falcon/11.png)
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You already have dashboard feature so let's not use that here..

| LLM analysis | 90 s | Up to 5 attempts with exponential backoff (5 s to 30 s) |
| Save result | 10 s | Default |

If the LLM step exhausts its retries, the widget records a failure state in the database. Click **Rerun** to try again once the model provider recovers.
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why do you want to say LLM? we are in no way saying its LLM right? we claim its our agent that does the fetching etc. right?

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