Learning Feature

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Introduction

The Learning feature helps your agents improve over time. When an agent completes a task particularly well, you can capture that success as a “learning” that guides future runs.

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What is Learning?

Learning allows you to tell an agent: “This is how you should handle this type of task.” By marking a successful run as a learning, you help the agent understand what good looks like. Think of it as positive reinforcement—showing the agent an example of excellent work so it can replicate that success.

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Why Use Learning?

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Improve Consistency

When an agent handles a task perfectly, capture that approach so future runs follow the same pattern.

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Handle Edge Cases

If an agent successfully navigates a tricky situation, save that learning so it knows how to handle similar cases.

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Refine Over Time

As you work with an agent, you can continuously improve it by adding learnings from its best performances.

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Reduce Instruction Complexity

Instead of writing detailed instructions for every scenario, let the agent learn from examples of good work.

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How to Create a Learning

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How Learning Works

When you create a learning: The learning becomes part of how the agent approaches its work—like institutional knowledge that improves performance.

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Best Practices

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Choose Representative Examples

Select runs that:

• Completed the task correctly
• Handled the typical case well
• Demonstrate the approach you want
• Produced the output quality you expect

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Avoid Edge Cases Initially

Don’t make unusual situations the learning:

• Start with standard, successful runs
• Edge cases can create unexpected patterns
• Build a foundation with typical examples

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One Learning at a Time

Each agent carries only one learning:

• Creating a new learning replaces the old one
• Choose your best example
• Update when you find a better example

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Review Results

After creating a learning:

• Monitor subsequent runs
• Verify the learning improves performance
• Adjust if results aren’t as expected

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Real-World Examples

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Example 1: Email Responses

Your agent drafts customer responses. One response was particularly well-written—professional tone, addressed all concerns, and followed brand guidelines perfectly. Create a learning from this run. Now future responses will follow this high-quality example.

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Example 2: Data Processing

Your agent extracts data from invoices. One run correctly handled a complex invoice with multiple line items and applied the right categorization. Create a learning from this run. The agent now knows how to handle similar complex invoices.

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Example 3: Report Generation

Your agent creates weekly reports. One report had exactly the right structure, formatting, and level of detail. Create a learning from this run. Future reports will follow this successful template.

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Limitations

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Things to Know

• Learning helps agents improve through positive examples
• Only one learning can be active per agent
• Creating a new learning replaces the previous one
• Learnings work alongside your written instructions
• Choose runs that represent your best outcomes