Answer Reasoning & Verification

Answer Reasoning, also known as Traces, provides complete transparency into how AI agents think through problems. See the agent's step-by-step reasoning, tool invocations, and decision-making process in real-time as responses stream.

What It Does

When enabled, users can see detailed traces of the agent's thought process:

• Step-by-step reasoning as the agent works through problems
• Tool invocations with inputs and outputs
• Decision points and why the agent made specific choices
• Quality assessments and confidence levels
• Error explanations when things go wrong

The reasoning begins streaming back long before the answer itself arrives, helping the user feel results are coming quickly and providing insight into how the agent is approaching the problem. This is particularly helpful for long-running queries where the user may not be able to see the progress.

Why It Matters

AI agents can feel like black boxes - they give answers but you don't know how they arrived at them. This makes it difficult to:

• Trust the responses - Hard to verify correctness without seeing the work
• Debug issues - Can't identify where things went wrong
• Improve agents - Don't know what to fix without visibility
• Educate users - Can't explain how the AI works

Transparent reasoning solves all of these problems, building trust and enabling continuous improvement.

How It Works

Streaming Reasoning

As the agent processes a request, reasoning events stream to the UI:

1. User sends message

   Question is sent to the agent

2. Reasoning starts streaming

   Agent's thought process appears in real-time:

   • "Analyzing the user's question..."
   • "I need to look up order information..."
   • "Calling order-lookup tool..."

3. Tool invocations shown

   Tool calls with inputs and outputs displayed:

   • Tool name and purpose
   • Input parameters
   • Execution time
   • Returned results

4. Final answer synthesized

   Agent explains how it used tool results to formulate the answer

5. Verification (if enabled)

   Independent verifier's assessment with grade and reasoning

Two Levels of Detail

Basic Traces - General thought process:

• High-level reasoning steps
• Tool names and purposes
• Success/failure indicators
• User-friendly explanations

Detailed Traces - Complete technical detail:

• Full tool schemas and parameters
• Execution timing and performance
• Model invocation details
• Internal state and context
• Error stack traces

Trace Visibility Control

Role-Based Access

Control who sees traces:

const chatAppConfig: ChatAppConfig = {
    featureOverrides: {
        traces: {
            enabled: true,
            allowedRoles: ['pika:content-admin', 'customer-support']
        },
        detailedTraces: {
            enabled: true,
            allowedRoles: ['pika:site-admin']  // Admins only
        }
    }
};

User Type Separation

Different visibility for different audiences:

// Internal users see full traces
traces: {
    enabled: true,
    allowedUserTypes: ['internal-user']
}

// External users see no traces (default)
traces: {
    enabled: false,
    allowedUserTypes: ['external-user']
}

Per-Session Toggle

Users can toggle trace visibility:

• Show traces when debugging
• Hide traces for cleaner reading experience
• Persists preference per user

Use Cases

Agent Development and Debugging

Essential during development:

• See exactly what the agent is thinking
• Identify where reasoning goes wrong
• Verify tool calls are working correctly
• Test edge cases and failure modes
• Measure response quality

Customer Support Training

Help support teams understand agents:

• Learn how agents solve problems
• Understand tool capabilities
• Train on effective agent usage
• Identify when to escalate

Quality Assurance

Monitor agent performance:

• Review traces for problematic sessions
• Identify patterns in failures
• Verify policy compliance
• Assess response quality

User Education

Teach users how AI works:

• Demystify AI responses
• Build trust through transparency
• Show capabilities and limitations
• Explain tool usage

Trace Information

Reasoning Steps

What the agent is thinking:

Thinking: The user is asking about order #12345.
I should use the order-lookup tool to get the current status.

Tool Invocations

Complete tool execution details:

Tool: order-lookup
Input: { orderId: "12345" }
Status: Success
Duration: 243ms
Output: {
  orderId: "12345",
  status: "shipped",
  trackingNumber: "1Z999AA1...",
  estimatedDelivery: "2024-11-01"
}

Decision Points

Why the agent chose a specific path:

Decision: Based on the order status being "shipped",
I'll provide the tracking information and estimated delivery date.

Verification Results

Quality assessment with reasoning:

Verification: Grade A
Reasoning: Response accurately conveys the order status,
provides tracking information, and sets appropriate delivery expectations.
All information matches the tool output.

Advanced Features

Search and Filter Traces

Find specific information:

• Search within trace content
• Filter by tool name
• Find successful/failed invocations
• Locate specific reasoning patterns

Export Traces

Extract for analysis:

• Download full trace logs
• Export as JSON for processing
• Include in support tickets
• Archive for auditing

Trace Annotations

Add context to traces:

• Mark important decision points
• Flag issues for review
• Add explanatory notes
• Link to related documentation

Performance Considerations

Minimal Overhead

Traces add negligible latency:

• Streaming happens in parallel
• No additional LLM calls required
• Storage is append-only (fast writes)
• UI updates are asynchronous

Storage Requirements

Traces are stored with sessions:

• Average 1-5KB per session
• Compressed in DynamoDB
• Indexed in OpenSearch
• Subject to retention policies

Best Practices

Start with Internal Users

Roll out visibility gradually:

1. Phase 1: Enable for developers and admins
2. Phase 2: Extend to power users and support staff
3. Phase 3: Consider carefully before showing to external users
4. Phase 4: Never show detailed traces to external users

Use for Debugging

Essential debugging tool:

• Always enable traces during development
• Review traces when users report issues
• Compare traces for good vs bad responses
• Document trace patterns for training

Protect Sensitive Information

Be cautious with trace visibility:

• Traces may contain sensitive data
• Tool outputs might have PII
• Reasoning may reveal business logic
• Restrict access appropriately

Leverage for Training

Use traces to educate:

• Train support staff on agent capabilities
• Document common reasoning patterns
• Create examples of good agent behavior
• Build internal knowledge base

Integration with Other Features

Self-Correcting Responses

Traces show the full correction loop:

1. Initial agent reasoning and response
2. Verifier's analysis and grade
3. Reprompt with verifier feedback
4. Improved reasoning and final answer

LLM-Generated Feedback

Feedback references specific trace points:

• "In step 3, the agent should have considered..."
• "The tool invocation at timestamp X failed because..."
• "The reasoning in the final synthesis missed..."

Insights

Traces feed into insights generation:

• Goal completion inferred from reasoning
• Confidence derived from explicit statements
• Issues identified from error traces
• Complexity measured by reasoning depth

Getting Started

Enable Traces

Configure trace visibility for your chat apps.

How-To Guide →

View in Weather Sample

See traces working in the sample application.

Explore Sample →

Understand Trace Architecture

Deep dive into how traces work.

Read Concepts →

Related Capabilities

Self-Correcting Responses

Verification results appear in traces with full reasoning.

Learn More →

LLM-Generated Feedback

Feedback references specific trace elements.

Learn More →

Admin Site

Review traces for all sessions in one place.

Learn More →