From Toy to Tool

TL;DR

The gap between demo and production isn't just infrastructure - it's reliability. Pika includes built-in capabilities that catch errors before users see them (self-correcting responses), learn from real usage (AI-generated feedback), and help you iterate rapidly (instruction engineering tools). These aren't add-ons - they're core to making agents production-ready.

The Reliability Gap

Building a demo agent is easy. Making it reliable is hard.

What Happens in Practice

Week 1: Demo works great in controlled scenarios

"Show me the weather in San Francisco" → Perfect response
"What's my order status?" → Looks up order correctly
Leadership is impressed

Week 2: Real users find the edge cases

"What's the weather like for my trip next week?" → Confused, asks for location again
"Where's my order?" → Which order? Doesn't ask for clarification
"Cancel my subscription" → Tries to look up order, doesn't have cancel capability

Week 3-8: Constant firefighting

Adding edge case handling
Refining prompts
Discovering gaps in tools
No systematic way to improve

The problem: You're learning by user complaint, not proactively.

Pika's Approach: Built-In Intelligence

Pika includes features specifically designed to bridge this gap:

Self-Correcting Responses

Catch errors before users see them with independent verification and automatic retry

AI-Generated Feedback

Learn from every conversation with LLM-powered analysis and improvement suggestions

AI-Driven Insights

Understand patterns across thousands of sessions with automated analytics

Instruction Engineering Tools

Rapidly iterate on agent behavior with assistance and augmentation features

Let's dive into each one.

Self-Correcting Responses

The Problem

Agents make mistakes:

Hallucinate facts
Misunderstand questions
Use tools incorrectly
Give incomplete answers
Miss important details

Users see these mistakes. Trust erodes. You get support tickets.

How Self-Correction Works

An independent LLM evaluates every response:

Agent Generates Response
Your agent uses tools, reasons, and generates an answer.
Verifier Evaluates
A separate LLM model reviews the response:
- Does it answer the question?
- Is it accurate based on tool outputs?
- Does it follow guidelines?
- Is it complete and helpful?
Grade Assigned
Verifier assigns a grade:
- A: Excellent response
- B: Good response
- C: Acceptable but could be better
- F: Fails to answer or has errors
Auto-Reprompt (Optional)
If enabled and grade is below threshold (e.g., C or F):
- Verifier provides feedback on what's wrong
- System reprompts agent with feedback
- Agent gets second chance
- Up to 3 attempts
User Sees Best Response
Either the original (if good) or the improved response (if corrected).

Real Example

User question: "What's the refund policy for orders over $100?"

First attempt (Grade: C):

Agent: According to our policy, refunds are available within 30 days.

Verifier feedback:

Response is incomplete. The user specifically asked about orders over $100,
but the answer doesn't mention the special handling for large orders.
The tool output included a threshold field that wasn't used.

Second attempt (Grade: A):

Agent: For orders over $100, we offer refunds within 45 days (extended from
our standard 30-day policy). Additionally, orders over $100 qualify for free
return shipping. You can initiate a refund through your account page or by
contacting our support team.

What the user sees: The second, better response.

Configuration Options

features: {
  verifyResponse: {
    enabled: true,
    userTypes: ['external-user'],        // Who gets verification
    autoRepromptThreshold: 'C'           // Auto-fix if C or F
  }
}

Strategies:

Conservative (threshold: C): Auto-fix mediocre responses
Aggressive (threshold: B): Only accept excellent responses
Audit-only (no threshold): Grade but don't auto-fix (for monitoring)

When to Use It

Customer-Facing Agents

Use case: External users where accuracy is critical

Why: Catch errors before customers see them. Worth the extra latency (~2-3s) for quality.

High-Stakes Domains

Use case: Financial, medical, legal information

Why: Hallucinations and errors have serious consequences. Verification adds a safety layer.

Complex Multi-Step Tasks

Use case: Agents that use multiple tools or complex reasoning

Why: More steps = more opportunities for errors. Verification catches logic mistakes.

Trade-Offs

Pros:

Catches obvious errors automatically
Improves response quality measurably
Transparent grades in traces for debugging
Reduces user complaints

Cons:

Adds 2-3 seconds latency (verification + potential reprompt)
Doubles Bedrock costs for verified messages
Not perfect - verifier can miss issues

Recommendation: Start with internal users, expand to external once calibrated.

AI-Generated Feedback

The Problem

After deploying an agent, you need to know:

What's working well?
What questions are users actually asking?
Where is the agent struggling?
What improvements would help most?

Manual review doesn't scale. You can't read every conversation.

How Feedback Works

After a session completes, an LLM analyzes the entire conversation:

Session Completes
User ends conversation or it times out.
Background Analysis
EventBridge triggers feedback generation (non-blocking):
- LLM reviews full conversation
- Analyzes tools used
- Evaluates responses
- Identifies patterns

Feedback Generated

Structured output:

{
  "sessionGoal": "User wanted to check order status and modify shipping address",
  "outcome": "Partial success - status checked, address not modified",
  "strengths": [
    "Quickly located order using order ID",
    "Clearly communicated current shipping status"
  ],
  "issues": [
    "Failed to offer address modification option",
    "Didn't verify new address before confirming"
  ],
  "suggestions": [
    "Add 'modify_shipping_address' tool",
    "Update prompt to proactively offer related actions",
    "Consider address validation tool integration"
  ]
}

Indexed for Exploration
Feedback stored in OpenSearch and available in admin interface.

Real Example

Session: User asks about product compatibility, agent provides generic answer

Generated feedback:

Goal: User wanted to know if Product A works with their existing Product B.

Outcome: Unsuccessful - agent provided general compatibility info but didn't
determine specific compatibility.

Issues:
- Agent didn't ask which model of Product B user owns
- Compatibility tool wasn't used effectively
- User had to ask follow-up questions to get specific answer

Suggestions:
- Update prompt to ask clarifying questions about existing equipment
- Enhance compatibility tool to accept more detailed model information
- Consider adding tool that looks up user's previous purchases

What you learn: Need better clarifying questions and enhanced tool capabilities.

Aggregating Insights

Individual feedback is helpful. Patterns across thousands of sessions are transformative.

Common patterns discovered:

"Users frequently ask X but agent doesn't have Y tool" → Build Y tool
"Agent often misunderstands Z type of question" → Refine prompt
"Tool A fails 15% of the time with timeout errors" → Fix tool reliability
"Sessions with file uploads have 40% success rate" → Improve file handling

Configuration

// Site-wide enablement in pika-config.ts
features: {
  llmGeneratedFeedback: {
    enabled: true
  }
}

// Per chat-app (inherits from site-wide)
chatAppConfig: {
  features: {
    llmGeneratedFeedback: {
      enabled: true // Can disable for specific apps
    }
  }
}

Cost considerations:

Runs asynchronously (doesn't impact user experience)
~$0.01-0.02 per session analyzed
Skip analysis for very short sessions (< 3 messages)
Configure sampling rate if needed

AI-Driven Insights

Beyond Individual Sessions

Feedback analyzes single conversations. Insights aggregate across all sessions.

Metrics automatically tracked:

Session Metrics

Total sessions per day/week/month
Average session length
Completion rate (did user get answer?)
Abandonment points (where do users give up?)
Return user rate

Sentiment Analysis

Overall sentiment per session
Sentiment change throughout conversation
Frustration indicators
Satisfaction patterns

Goal Completion

What users are trying to accomplish
Success rate per goal type
Common failure modes
Time to completion

Agent Performance

Tool usage patterns
Tool success rates
Common tool combinations
Verification grades distribution

Dashboard Views

Admin interface shows:

Trend graphs (sessions over time, sentiment trends)
Top questions asked
Most common user intents
Agent performance metrics
Cost per session

Actionable insights:

"Top 10 questions the agent handles poorly" → Focus prompt improvements here
"80% of frustrated users had this issue" → Priority fix
"Tool X is used but fails 30% of the time" → Reliability issue
"New topic emerged in last week" → Content gap

Real-World Use Case

E-commerce support agent:

Week 1 insights:

500 sessions
65% completion rate
Top frustration: "Can't modify order after placement"

Action: Add modify_order tool

Week 4 insights:

800 sessions
82% completion rate
New top question: "When will back-ordered items ship?"

Action: Integrate with inventory system for ETA tool

Week 8 insights:

1200 sessions
88% completion rate
Users asking about "order bundling" (not supported)

Action: Either add bundling feature or train agent to explain alternatives

This is continuous improvement driven by data, not guesswork.

Instruction Engineering Tools

Instruction Assistance

Agent struggling? Get AI help improving its prompt.

How it works:

features: {
  instructionAssistance: {
    enabled: true,
    userRoles: ['pika:content-admin'] // Who can request assistance
  }
}

In admin interface:

Select underperforming agent
Provide examples of bad behavior
AI analyzes agent prompt and suggests improvements
You review and apply suggestions

Example:

Current prompt: "You are a helpful assistant."

Assistance suggests:
"You are a customer support specialist for Acme Corp. Your goal is to:
1. Identify the user's specific need
2. Use available tools to find relevant information
3. Provide clear, actionable answers
4. Offer related help proactively

When using tools:
- Always verify you have required parameters before calling
- Explain what information you're looking up
- If a tool fails, explain the issue clearly"

Result: More specific, actionable prompt leading to better behavior.

Instruction Augmentation

Dynamically enhance agent context based on runtime data.

Use cases:

User Context Injection

Augment prompt with user-specific information:

Base prompt: "You are a helpful assistant."

Augmented at runtime:
"You are a helpful assistant. The user is:
- Name: John Doe
- Account type: Premium
- Recent orders: #12345 (shipped), #12346 (delivered)
- Known preferences: Prefers email over phone contact"

Agent has context without you manually providing it every time.

Dynamic Policy Injection

Add current policies or rules:

Base prompt: "Help with returns."

Augmented at runtime:
"Help with returns. Current return policy:
- Standard: 30 days
- Premium members: 45 days
- Holiday extended: Until Jan 31 (currently active)
- Exceptions: Final sale items non-returnable"

Policies change without redeploying agents.

Session Context

Include relevant session history or shared context:

Augmented with:
"Previous conversation summary:
- User previously asked about Product A compatibility
- Determined user has System B version 2.1
- User expressed interest in upgrade path"

Agent maintains context across sessions.

Putting It All Together

These features work synergistically:

Week 1: Deploy agent with self-correction enabled

Catch obvious errors immediately
Build user confidence

Week 2: Review AI-generated feedback

Identify top 5 improvement areas
Prioritize based on frequency

Week 3: Use instruction assistance

Refine prompts for identified issues
Add tools for gaps

Week 4: Monitor insights dashboard

Verify improvements worked
Discover next issues
Continuous cycle

Result: Steady, data-driven improvement instead of reactive firefighting.

Production Intelligence Checklist

Before launching an agent to real users:

✅ Enable self-correction for customer-facing responses ✅ Configure feedback generation to learn from all sessions ✅ Set up insights monitoring to track trends ✅ Grant admin access to content owners for instruction tuning ✅ Establish review cadence (weekly feedback review) ✅ Define improvement metrics (completion rate, sentiment, etc.)

The Competitive Advantage

Most teams treat agents as "deploy and hope":

Ship and see what users complain about
Manual review of select conversations
Slow, reactive improvements
Never know if you're getting better

Pika treats agents as continuously improving systems:

Automatic quality checks
Comprehensive feedback on all sessions
Data-driven improvement priorities
Measurable progress

This is the difference between a toy and a tool.