Agent Learning

The Unified Learning System enables your agents to improve over time. When users provide feedback or corrections, the system captures those insights and applies them to future conversations. Your agent literally learns from its mistakes.

How It Works

User gives feedback     →    Learning captured    →    Agent context enriched
"That's not right,           Insight stored with       Future responses
the return policy is         confidence score          incorporate the
30 days, not 14"             and metadata              corrected information

Capture — A learning is recorded from user feedback, manual input, or automated extraction.
Store — The insight is saved with metadata (type, confidence, source).
Apply — On future conversations, relevant learnings are injected into the agent’s context.
Validate — Learnings can be reviewed, confirmed, or invalidated by humans.

Learning Types

Type	Description	Source
`manual`	Manually added by a human	Dashboard or API
`feedback`	Extracted from user feedback/corrections	User conversation
`conversation`	Derived from conversation patterns	Automated analysis
`correction`	Direct factual correction	User interaction

Capturing Learnings

From the Dashboard

Go to your agent’s Learning tab.
Click Add Learning.
Enter the insight text and select a type.
Set the confidence level (0.0 to 1.0).
Click Save.

You can also capture learnings directly from conversation transcripts:

Open a conversation in Call Logs or Chat History.
Highlight a correction or feedback message.
Click Capture as Learning.

From User Feedback

When a user provides feedback during a conversation, the system can automatically extract learnings:

curl -X POST https://api.thinnest.ai/learning/capture-feedback \
  -H "Authorization: Bearer $THINNESTAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "agent_abc123",
    "query": "What is the return policy?",
    "original_response": "Our return policy allows returns within 14 days of purchase.",
    "feedback": "Actually, it is 30 days not 14 days. Please update this.",
    "session_id": "session_xyz"
  }'

The system extracts the correction and stores it as a learning.

Manual Capture

curl -X POST https://api.thinnest.ai/learning/capture \
  -H "Authorization: Bearer $THINNESTAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "agent_abc123",
    "insight": "The company holiday hours are 9 AM - 3 PM on December 24 and 31. The office is fully closed on December 25 and January 1.",
    "learning_type": "manual",
    "confidence": 1.0,
    "metadata": {
      "source": "HR department",
      "valid_until": "2027-01-02"
    }
  }'

Listing Learnings

From the Dashboard

The Learning tab shows all learnings for an agent with:

Insight text
Type and confidence
Status (active or invalidated)
Creation date
Source metadata

Via the API

curl "https://api.thinnest.ai/learning/list?agent_id=agent_abc123&status=active&sort=created_at" \
  -H "Authorization: Bearer $THINNESTAI_API_KEY"

Query Parameters

Parameter	Type	Default	Description
`agent_id`	string	Required	Agent ID
`type`	string	null	Filter by learning type
`status`	string	”active”	Filter by status (“active” or “invalidated”)
`sort`	string	”created_at”	Sort field

Response

{
  "learnings": [
    {
      "id": 1,
      "agent_id": 42,
      "insight": "Return policy is 30 days, not 14 days",
      "learning_type": "feedback",
      "confidence": 0.95,
      "status": "active",
      "metadata": {
        "original_query": "What is the return policy?",
        "session_id": "session_xyz"
      },
      "created_at": "2026-03-06T10:30:00Z"
    }
  ]
}

Learning Statistics

Get aggregate statistics about an agent’s learnings:

curl "https://api.thinnest.ai/learning/stats?agent_id=agent_abc123" \
  -H "Authorization: Bearer $THINNESTAI_API_KEY"

{
  "total": 47,
  "active": 42,
  "invalidated": 5,
  "by_type": {
    "manual": 12,
    "feedback": 25,
    "conversation": 8,
    "correction": 2
  },
  "avg_confidence": 0.87
}

Managing Learnings

Invalidate a Learning

If a learning is incorrect or outdated, invalidate it so it’s no longer applied:

curl -X PATCH https://api.thinnest.ai/learning/1/invalidate \
  -H "Authorization: Bearer $THINNESTAI_API_KEY"

Delete a Learning

curl -X DELETE https://api.thinnest.ai/learning/1 \
  -H "Authorization: Bearer $THINNESTAI_API_KEY"

Get a Single Learning

curl https://api.thinnest.ai/learning/1 \
  -H "Authorization: Bearer $THINNESTAI_API_KEY"

How Learnings Are Applied

When an agent receives a message, the system:

Searches for relevant active learnings based on the conversation context.
Injects matching learnings into the agent’s system prompt as additional context.
The agent uses this enriched context to formulate its response.

This happens automatically — no configuration needed beyond capturing the learnings.

Prompt Injection Protection

The learning system includes built-in protection against prompt injection. Learnings are sanitized before being injected into the agent context, preventing malicious users from manipulating the agent through crafted feedback.

Best Practices

Review feedback-based learnings — Automated extraction isn’t perfect. Periodically review and confirm learnings.
Set appropriate confidence levels — Higher confidence (0.9+) for verified facts, lower (0.5-0.7) for inferred patterns.
Invalidate rather than delete — Invalidation preserves the learning history for auditing.
Use metadata — Add source information, expiration dates, and context to learnings for better organization.
Monitor learning stats — A high volume of corrections may indicate your base prompt needs updating.
Combine with knowledge sources — Learnings are for dynamic corrections. Stable information should go in knowledge sources.
Pair with Dash — The Dash (Self-Learning DB) tool has its own SQL-specific learning system that complements platform learnings. Dash learns query patterns and error corrections, while Agent Learning captures conversation-level feedback.

Introduction

Getting Started

Voice Agents

Agent Capabilities

Channels

Quality & Oversight

Platform

Agent Learning

Agent Learning

How It Works

Learning Types

Capturing Learnings

From the Dashboard

From User Feedback

Manual Capture

Listing Learnings

From the Dashboard

Via the API

Query Parameters

Response

Learning Statistics

Managing Learnings

Invalidate a Learning

Delete a Learning

Get a Single Learning

How Learnings Are Applied

Prompt Injection Protection

Best Practices

​Agent Learning

​How It Works

​Learning Types

​Capturing Learnings

​From the Dashboard

​From User Feedback

​Manual Capture

​Listing Learnings

​From the Dashboard

​Via the API

​Query Parameters

​Response

​Learning Statistics

​Managing Learnings

​Invalidate a Learning

​Delete a Learning

​Get a Single Learning

​How Learnings Are Applied

​Prompt Injection Protection

​Best Practices

Agent Learning

How It Works

Learning Types

Capturing Learnings

From the Dashboard

From User Feedback

Manual Capture

Listing Learnings

From the Dashboard

Via the API

Query Parameters

Response

Learning Statistics

Managing Learnings

Invalidate a Learning

Delete a Learning

Get a Single Learning

How Learnings Are Applied

Prompt Injection Protection

Best Practices