Handit.ai
Handit.ai is an open-source engine that automatically evaluates AI agent decisions, generates improved prompts and datasets, conducts A/B testing, and provides controlled deployment of optimizations.
https://www.handit.ai/?ref=producthunt
Product Information
Updated:Jul 4, 2025
What is Handit.ai
Handit.ai is a comprehensive AI optimization platform designed to address the common challenges faced when running AI agents in production environments. Unlike traditional monitoring tools that only flag issues, Handit.ai takes a proactive approach by not just identifying problems but automatically generating and testing solutions. It was developed over nine months to help teams debug AI agents in production and has evolved into a powerful tool for maintaining and improving AI performance at scale.
Key Features of Handit.ai
Handit.ai is an open-source AI optimization engine that automatically monitors, evaluates, and improves AI agents in production. It tracks every agent decision, auto-generates better prompts and datasets, conducts A/B testing of improvements, and provides a pull-request-style review system for deploying optimizations. The platform enables continuous AI improvement with minimal manual intervention while maintaining human control over what gets deployed.
Real-Time Monitoring & Evaluation: Continuously tracks and scores every model, prompt, and agent decision using LLM-as-Judge, business KPIs, and latency benchmarks
Automatic Optimization: Auto-generates fixes for identified issues and conducts A/B testing to validate improvements before deployment
Controlled Deployment System: Presents improvements as versioned pull requests with full diff views and one-click deployment options, allowing teams to review and control what goes live
Performance Analytics: Provides comprehensive dashboards that tie every optimization to business impacts and ROI metrics
Use Cases of Handit.ai
AI Agent Performance Optimization: Helps teams identify and fix issues with AI agents that are silently failing or experiencing performance degradation in production
Prompt Engineering Management: Addresses prompt drift and automatically generates and tests improved prompts to maintain consistent performance
Production AI Monitoring: Enables teams to track and optimize mission-critical AI systems in production environments with minimal manual intervention
Pros
Zero manual tuning required for continuous improvement
Open-source architecture provides transparency and customization options
Comprehensive monitoring and optimization in a single platform
Cons
Requires initial setup and integration with existing systems
Some advanced features may be limited to premium versions
How to Use Handit.ai
Get Started: Create an account on Handit.ai and obtain your API key from the dashboard (dashboard.handit.ai)
Install SDK: Install the Handit SDK and configure it with your integration token. Never commit the token to version control - use environment variables in production
Add Agent Tracing: Import the HanditTracker and wrap your main agent function with the @tracker.start_agent_tracing() decorator to capture all agent activities
Configure Tracking: Set up the tracker configuration with your API key: tracker.config(api_key='your-api-key')
Implement Error Handling: Ensure your agent functions include proper try-catch blocks to capture and report errors with full context
Monitor Performance: Access the Handit dashboard to view real-time metrics, error logs, and performance trends of your AI agents
Review Improvements: Review auto-generated fixes and improvements suggested by Handit through the dashboard's PR-style interface
A/B Test Changes: Use the built-in A/B testing features to evaluate proposed improvements before deployment
Deploy Updates: Use one-click deployment to promote successful changes to production, with the ability to instantly rollback if needed
Track Impact: Monitor business impact metrics and ROI through Handit's dashboards to measure the effectiveness of deployed improvements
Handit.ai FAQs
Handit.ai is an open-source engine that automatically improves AI agents. It evaluates every agent decision, auto-generates better prompts and datasets, A/B-tests improvements, and lets users control what changes go live in production.
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