AI Agent Testing, Monitoring and Evaluation Platform

PrototipeAI provides automated testing infrastructure for conversational AI agents, autonomous agents and agentic workflows, combining multimodal test execution, continuous monitoring, performance metrics tracking and collaborative evaluation workflows. The platform lets technical and non-technical stakeholders validate agent behavior through structured testing and feedback annotation.

Tests accept text, audio, document, image and structured data inputs with no configuration. The system automatically detects friction points in conversations, converts them into actionable feedback and applies corrections to agent prompts manually or through automated suggestion mechanisms.

Automatic Friction Detection

The platform continuously monitors agent conversations and automatically identifies friction points: tool call failures, incorrect responses, signs of user frustration, conversation loops and guideline violations. Detected issues are converted into structured feedback entries with conversation context and severity classification.

• Real-time analysis of conversation quality during testing
• Pattern recognition for common failure modes (hallucinations, refusals, incomplete responses)
• User frustration detection via linguistic markers and conversation abandonment
• Automatic categorization by failure type (accuracy, tone, relevance, safety)
• Severity scoring (critical, high, medium, low) based on impact

Collaborative Feedback System

Stakeholders from business teams, product and domain experts provide feedback on agent responses directly within test conversations. Feedback is organized by conversation thread, agent version and issue category. The platform aggregates feedback across multiple reviewers to identify systematic issues requiring prompt corrections.

• In-conversation annotation interface for response feedback
• Multi-party access with no technical knowledge requirement
• Structured feedback categories (accuracy, tone, completeness, safety, relevance)
• Feedback aggregation showing recurring issues across conversations
• Comment threads for discussion between reviewers
• Assignment workflow for domain expert review

Automatic and Manual Correction Workflows

When friction points or negative feedback reach threshold volumes, the platform suggests specific prompt modifications to resolve issues. Teams can apply corrections manually with context-aware editing or enable automatic correction where the platform updates prompts based on feedback patterns and regenerates agent versions.

• Automated suggestion of prompt corrections based on feedback patterns
• Manual correction with side-by-side comparison of current vs suggested prompts
• Automatic correction mode for approved issue categories
• Correction history showing which feedback triggered each change
• A/B testing of original vs corrected versions

Multimodal Test Input Support

The testing infrastructure accepts multiple input types with no additional configuration: text messages, audio files (automatically transcribed), documents (PDF, DOCX, TXT), images, structured spreadsheet data (CSV, XLSX) and API requests. This enables comprehensive testing of agents handling diverse user inputs.

• Text: Direct message input via chat interface
• Audio: Audio file upload or live recording, automatic transcription to text
• Documents: PDF, DOCX, TXT parsing for document-based queries
• Images: Image upload with vision model integration for visual understanding
• Structured data: CSV/XLSX upload, automatic conversion to a queryable format
• API: REST endpoints for programmatic test execution

Conversation History and Analysis

Complete conversation logs are organized by agent version, test session, user and date. Each conversation includes full execution tracing: user inputs, agent responses, tool calls made, memory state, token usage and latency measurements. Conversations are searchable and filterable for pattern analysis.

• Complete conversation history with unlimited retention
• Organization by agent version for version comparison
• Searchable by keywords, user, date range, feedback status
• Filtering by conversation outcome (successful, friction detected, user abandoned)
• Execution tracing showing LLM reasoning steps and tool invocations
• Export to CSV or JSON for external analysis

Performance Metrics and Monitoring

The dashboard displays quantitative metrics tracking agent performance over time: response accuracy rate, user satisfaction scores, friction occurrence frequency, average response latency, token consumption per conversation and cost per interaction. Metrics are segmented by agent version, conversation type and time period.

• Accuracy metrics: Correct response rate, hallucination frequency, tool call success rate
• User experience: Satisfaction scores, conversation abandonment rate, retry frequency
• Performance: Average latency, P95/P99 latency, timeout rate
• Cost metrics: Token usage per conversation, cost per successful interaction
• Quality trends: Metric comparison across agent versions and time periods
• Alerts: Threshold-based alerts for metric degradation

Domain Expert Participation

Non-technical domain experts take part in agent validation without requiring prompt engineering or AI knowledge. Experts test agents through simple conversation interfaces, provide natural language feedback and review agent responses for domain accuracy. The platform converts expert feedback into structured training data.

• No-code testing interface for non-technical users
• Natural language feedback submission (no technical terminology required)
• Domain-specific evaluation criteria customizable per use case
• Expert review assignment workflow with notification system
• Feedback templates for common issue types
• Gamification features to encourage consistent expert participation

Manual Exploratory Testing

Stakeholders hold free-form conversations with agents to explore edge cases, validate behavior changes and identify unexpected responses. All conversations are logged for analysis. Testers can annotate specific responses with feedback during or after conversations.

Batch Testing with Datasets

Teams upload spreadsheets containing test scenarios (input messages and expected results). The platform runs all scenarios automatically, compares agent responses to expected results and generates pass/fail reports. It supports regression testing after prompt modifications.

API-Driven Automated Testing

Developers integrate agent tests into CI/CD pipelines via REST API. Test suites run automatically on every prompt version change. The API returns success/failure status and detailed execution logs for programmatic analysis.

Continuous Production Monitoring

After deployment, the platform continues monitoring production conversations for friction detection. Issues are automatically logged as feedback for review. High-severity issues trigger alerts for immediate investigation.

Pre-Deployment Validation

Product teams validate agent behavior before production launch through comprehensive testing with stakeholder participation. Domain experts verify the accuracy of agent responses in specialized areas (medical, legal, financial) without AI technical expertise.

Regression Testing After Prompt Changes

Engineering teams run automated test suites after each prompt modification to ensure changes fix the intended issues without introducing new ones. Batch tests with historical conversation datasets validate backward compatibility.

Continuous Quality Monitoring

Organizations monitor production agents for quality degradation over time. Automated friction detection identifies emerging issues from user conversations. Metrics dashboards track performance trends and alert teams to anomalies.

LLM Provider Comparison

Teams evaluate multiple LLM providers (OpenAI, Anthropic, Google, Mistral) with identical test scenarios to compare accuracy, cost, latency and behavior. Data-driven decisions replace vendor assumptions.

Multimodal Agent Validation

Organizations building agents that process documents, images, audio or structured data test with realistic inputs during development. Validation ensures agents correctly interpret multimodal inputs before customer exposure.

Key metrics tracked per agent version:

• Response Accuracy: Percentage of responses meeting correctness criteria
• Friction Rate: Percentage of conversations with detected issues
• User Satisfaction: Average rating from feedback annotations
• Completion Rate: Conversations reaching successful completion
• Average Latency: Average time from user input to agent response
• Token Usage: Average tokens consumed per conversation
• Cost Per Conversation: Average API cost including all LLM and tool calls
• Tool Success Rate: Percentage of successful tool/API invocations

Step 1: Friction Detection

The platform detects the agent providing incorrect product pricing in a customer support conversation. The issue is categorized as an "accuracy error" with high severity.

Step 2: Feedback Assignment

The system assigns the issue to a product team domain expert for review. The expert confirms the pricing error and adds context about the correct pricing structure.

Step 3: Correction Suggestion

The platform analyzes the feedback and suggests adding a pricing guardrail to the system prompt: "Always retrieve current pricing from the product API instead of using training data."

Step 4: Correction Application

The team reviews the suggestion and approves the modification. The platform updates the prompt in the staging environment and regenerates the agent version.

Step 5: Validation

The automated test suite runs with pricing-related scenarios. All tests pass. Manual review by a domain expert confirms the fix. The agent version is promoted to production.

Run automated tests via REST API:

POST https://api.prototipeai.com/v1/agents/{agent_id}/test
Authorization: Bearer {api_token}
Content-Type: application/json

{
  "test_cases": [
    {
      "input": "Qual é o preço do plano Pro?",
      "expected_keywords": ["R$ 99", "mensal"],
      "max_latency_ms": 3000
    }
  ]
}

Response:
{
  "results": [
    {
      "passed": true,
      "response": "O plano Pro custa R$ 99 por mês.",
      "latency_ms": 1850,
      "tokens_used": 124
    }
  ],
  "summary": {
    "total": 1,
    "passed": 1,
    "failed": 0
  }
}