> ## Documentation Index
> Fetch the complete documentation index at: https://docs.beam.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Rerunning Tasks
> Re-execute tasks for debugging, testing workflow changes, backtesting prompt improvements, and validating agent performance
Rerunning tasks allows you to re-execute completed or failed workflows with the same inputs and test modifications. This is essential for debugging, validating changes, and demonstrating improvements.
## Understanding Task Reruns
Every completed task can be re-executed to verify behavior, test changes, or debug issues. Beam provides multiple rerun strategies:
**Full Task Rerun** - Re-execute entire workflow from start with original trigger data
**Auto-run** - Automatically retry nodes that fail evaluation criteria
**Batch Rerun** - Re-process multiple tasks to backtest prompt or workflow improvements
## Manual Task Rerun
Re-execute any completed or failed task to test changes or debug issues.
**Accessing Rerun:**
1. Navigate to task execution details in Tasks page
2. Scroll to bottom of execution timeline
3. Click **"Re-run task"** button below workflow steps
**What Happens:**
* Workflow re-executes with identical trigger input (`task_query`)
* All file attachments from original task preserved
* New execution creates separate task record
* Original task remains unchanged for comparison
**Testing Workflow Changes:**
* Modified node configurations or prompts
* Updated evaluation criteria
* Changed tool selections
* Added or removed nodes
**Debugging Failures:**
* Task failed due to transient error (API timeout, network issue)
* Integration temporarily unavailable
* Want to verify fix worked
**Demonstrating Improvements:**
* Show before/after results to stakeholders
* Validate optimization impact
* Compare agent performance over time
**Preserved Elements:**
* Original trigger input data (task\_query)
* File attachments uploaded with task
* Variable configurations
**Fresh Execution:**
* New timestamps and task ID
* Current workflow configuration (reflects any edits made)
* Latest tool versions and integrations
* Updated evaluation criteria
**Important:** Rerun uses current published workflow, not the version from original execution.
Auto-run configuration
Use Auto-run to retry a node when its evaluation result does not meet the configured accuracy score.
**Configure Auto-run:**
1. Open the agent's **Flow**.
2. Select the node to configure.
3. In the node sidebar, open **Evaluation**.
4. Enable **Evaluation metrics** and add or generate the criteria the node should meet.
5. Turn on **Auto-run**, then set the **Accuracy score (%)** and **Number of re-runs**.
**Configuration Options:**
**Evaluation metrics:** Enable criteria that assess the node's output against your expected standard.
**Auto-run:** Enable automatic retries for the node.
**Accuracy score (%):** Set the score the node must meet.
**Number of Re-runs:** Set maximum retry attempts (max 3)
**Evaluation-Based Triggering:**
1. Node executes and generates output
2. Evaluation criteria assess accuracy
3. If score is below the configured threshold → Auto-run triggers
4. Step re-executes with same input
5. Repeat until passing score or max retries reached
**Example:**
* Evaluation threshold: 90%
* First execution: 75% (fails)
* Auto-run 1: 85% (fails)
* Auto-run 2: 92% (passes)
* Workflow continues with passing output
**When to Enable:**
* Steps with non-deterministic outputs (GPT-based extraction)
* Classification tasks requiring high confidence
* Data extraction from inconsistent formats
* Steps where retry often improves results
**When NOT to Enable:**
* Deterministic operations (API calls with fixed responses)
* Steps failing due to missing data (retries won't help)
* Integration errors requiring manual fix
* Final output steps (may need human review instead)
**Optimal Configuration:**
* Max 2-3 retries (more rarely helps)
* Clear evaluation criteria (specific, measurable)
* Monitor retry frequency (high retries indicate prompt issues)
**Auto-run:**
* Happens during task execution automatically
* Triggered by evaluation scores
* No human intervention required
* Limited to configured max retries
**Manual Rerun:**
* Initiated by user after task completes
* Re-runs the complete task
* Unlimited reruns available
* Useful for testing changes made after execution
* Demonstrates improvements to stakeholders
Flow context for reruns
Auto-run is configured on the selected node in the Flow sidebar's **Evaluation** tab. Keep the criteria, accuracy score, and rerun limit aligned with the outcome that node is expected to produce.
## Backtesting Prompt Changes
Re-execute multiple tasks to validate prompt improvements across representative data set.
**Backtesting Workflow:**
Identify 10-20 tasks representing common scenarios, edge cases, and failure patterns. Mark or note task IDs for batch rerun.
Update node prompts, evaluation criteria, or tool configurations based on identified improvements.
Execute rerun on each saved task individually. Beam creates new execution records for comparison.
Review evaluation scores before/after changes. Calculate improvement rate: tasks that now pass vs previously failed.
If improvement meets targets (e.g., 90%+ success rate), publish workflow changes to production.
**Criteria for Good Backtest Set:**
* **Variety**: Cover all workflow branches and scenarios
* **Failures**: Include tasks that previously failed
* **Edge Cases**: Unusual data formats or inputs
* **Success Cases**: Verify changes don't break working scenarios
* **Recent Data**: Reflects current data patterns
**Recommended Size:**
* Minimum: 10 tasks for basic validation
* Optimal: 20-30 tasks for comprehensive testing
* Large Changes: 50+ tasks for major overhauls
**Key Metrics:**
**Accuracy Improvement:**
* Before: Average evaluation score across backtest set
* After: Average evaluation score after prompt changes
* Target: 10-20% improvement in scores
**Failure Reduction:**
* Before: Number of tasks failing evaluation
* After: Number of tasks failing after changes
* Target: 50%+ reduction in failures
**Consistency:**
* Standard deviation of evaluation scores
* Lower = more consistent performance
* Target: Reduced variance in results
**Regression Check:**
* Previously passing tasks still pass
* No new failures introduced
* Target: Zero regression on working cases
**Prompt Optimization:**
* Tested new extraction prompts on 15 invoices
* Accuracy improved from 78% to 93%
* Reduced "amount" field extraction errors by 60%
**Evaluation Criteria Tuning:**
* Adjusted confidence thresholds
* Retested on 25 classification tasks
* Improved precision without sacrificing recall
**Tool Configuration Changes:**
* Modified API parameters for data lookup
* Reran 20 validation workflows
* Reduced timeout errors from 15% to 2%
## Best Practices
**Create Task Libraries:**
* Save 10-20 representative tasks per agent
* Cover all workflow branches
* Include both successes and failures
* Update quarterly with new patterns
**Organization:**
* Label tasks by scenario type
* Note which branch/node they test
* Document expected outcomes
* Track when last used for backtesting
**Systematic Comparison:**
* Keep original execution visible
* Note evaluation score changes
* Review output quality differences
* Document unexpected behavior
**Metrics to Track:**
* Execution time (faster/slower?)
* Evaluation scores (improved/degraded?)
* Branch selections (changed logic?)
* Tool errors (more/fewer issues?)
**Warning Signs:**
* Step frequently uses all 3 retries
* Auto-run happens on >30% of tasks
* Retries rarely improve scores
* Execution time significantly increased
**Action Items:**
* Review and improve evaluation criteria
* Optimize prompts causing frequent retries
* Consider if data quality is issue
* Disable Auto-run if not helping
**What to Track:**
* Which tasks were rerun and why
* Changes made before rerun
* Before/after evaluation scores
* Whether change solved the issue
**Benefits:**
* Proves ROI of optimization work
* Identifies patterns in failures
* Guides future improvements
* Demonstrates value to stakeholders
## Next Steps
Monitor task execution results before rerunning
Configure evaluation criteria that can trigger Auto-run
Use AI-powered prompt optimization before rerunning
Leverage debugging features alongside reruns
This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.