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

# Run History

> Complete execution history

Run history provides a complete log of every agent execution — essential for debugging, auditing, and understanding agent behavior.

{/* Screenshot: Runs page showing time-series chart and list of runs */}

## What is a Run?

A **run** is a single execution of an agent in response to a customer message. Each run captures:

* The triggering message and conversation
* All steps the agent took
* Knowledge searched and retrieved
* Tools called and their results
* Response generated
* Classification applied

## Viewing Run History

<Steps>
  <Step title="Navigate to Runs">
    Go to **Analyze → Runs**.
  </Step>

  <Step title="Browse or Search">
    View the list or use filters to find specific runs.
  </Step>

  <Step title="Click for Details">
    Click any run to see full execution details.
  </Step>
</Steps>

## Run List View

{/* Screenshot: Runs list showing columns for time, status, category, preview */}

The list shows:

| Column       | Description                |
| ------------ | -------------------------- |
| **Time**     | When the run occurred      |
| **Status**   | Success, escalated, error  |
| **Source**   | Channel/deployment         |
| **Category** | Assigned category          |
| **Preview**  | First line of conversation |

## Filtering Runs

{/* Screenshot: Filter panel showing date, status, category, deployment options */}

### By Date Range

* Last 24 hours
* Last 7 days
* Last 30 days
* Custom range

### By Status

* **Success** — Completed normally
* **Escalated** — Handed to human
* **Error** — Something went wrong

### By Category

Filter by assigned category.

### By Deployment

Filter by specific agent deployment.

### By Search

Search conversations by keyword.

## Run Details

Click any run to see complete details:

{/* Screenshot: Run detail view showing conversation, steps, and metadata */}

### Conversation

Full message thread between customer and agent:

{/* Screenshot: Conversation thread in run detail */}

### Execution Steps

Step-by-step breakdown of what happened:

{/* Screenshot: Execution steps showing sequence of actions */}

1. **Message received** — Initial trigger
2. **Guardrails checked** — Safety evaluation
3. **Knowledge searched** — What was found
4. **Runbook executed** — Steps followed
5. **Response generated** — Final output
6. **Classification applied** — Category and attributes

### Knowledge Retrieved

{/* Screenshot: Knowledge section showing retrieved articles */}

See what knowledge was used:

* Articles retrieved
* Relevance scores
* Content highlighted

### Tool Calls

{/* Screenshot: Tool calls section showing tool name, inputs, outputs */}

See what actions were taken:

| Field        | Description           |
| ------------ | --------------------- |
| **Tool**     | Which tool was called |
| **Inputs**   | Parameters passed     |
| **Output**   | Result returned       |
| **Duration** | How long it took      |
| **Status**   | Success or failure    |

### Classification

See how the conversation was tagged:

* **Category:** Billing, Technical, etc.
* **Attributes:** Priority, Product Area, etc.
* **Resolution:** Resolved or not

## Use Cases

### Debugging Issues

When something goes wrong:

1. Find the problematic run
2. Review execution steps
3. Identify where it failed
4. Fix the underlying issue

### Auditing Responses

Review what the agent said:

1. Filter by date range
2. Sample runs
3. Check response quality
4. Identify improvement areas

### Understanding Escalations

Why did the agent escalate?

1. Filter by status = Escalated
2. Review the conversation
3. Check which guardrail triggered
4. Decide if escalation was appropriate

### Training Improvement

Learn from real conversations:

1. Review successful runs
2. Identify patterns
3. Create knowledge from good examples
4. Update guidelines based on findings

## Volume Chart

{/* Screenshot: Time-series chart showing run volume over time */}

See run volume over time:

* Identify busy periods
* Spot anomalies
* Correlate with events

## Exporting Runs

Export run data for analysis:

* CSV export
* Include conversation, metadata, classification
* Filter before export

## Next Steps

<CardGroup cols={2}>
  <Card title="Performance Metrics" icon="chart-line" href="/analytics/performance-metrics">
    See aggregate metrics
  </Card>

  <Card title="Alerts" icon="bell" href="/analytics/alerts">
    Get notified of issues
  </Card>
</CardGroup>