Handit.ai

Product Introduction

1. Handit.ai is an open-source optimization engine designed to enhance AI agent performance through automated evaluation, prompt generation, and deployment control. It operates in production environments to monitor decisions, test improvements via A/B experiments, and deploy validated fixes with human oversight.
2. The core value lies in eliminating manual tuning by automating the entire improvement lifecycle—from identifying failures to deploying optimized versions—while maintaining full user control over live deployments.

Main Features

1. Real-Time Monitoring: Continuously tracks every AI component (models, prompts, agents) across environments, detecting bottlenecks, regressions, and performance drift through live dashboards and granular failure tagging.
2. Automatic Evaluation: Scores outputs using LLM-as-Judge grading, custom business KPIs, and latency benchmarks, enabling data-driven quality assessments without manual intervention.
3. Self-Optimization A/B Testing: Generates improved prompts and datasets, tests them as versioned pull requests, and provides side-by-side performance comparisons (accuracy, success rates) for informed deployment decisions.
4. Controlled Deployment: Offers one-click production rollout of winning versions, instant rollback capabilities, and impact dashboards linking AI improvements to measurable business outcomes like cost savings or user retention.

Problems Solved

1. Manual Optimization Overload: Addresses the inefficiency of human-driven prompt tuning and dataset curation by automating iterative improvements and validation.
2. Mission-Critical AI Failures: Targets teams deploying high-stakes AI agents (e.g., customer support, fraud detection) that require zero tolerance for silent failures or performance degradation.
3. Version Control Gaps: Solves the lack of auditable, production-safe deployment workflows for AI updates by introducing pull-request-style reviews and versioned A/B testing.

Unique Advantages

1. End-to-End Automation: Unlike monitoring-only tools (e.g., LangSmith), Handit.ai closes the loop by auto-generating fixes, testing them, and enabling controlled deployment—reducing mean time-to-repair (MTTR) from days to hours.
2. LLM-as-Judge Integration: Combines custom metrics with GPT-4/Claude-based evaluation to grade outputs contextually, ensuring alignment with both technical and business objectives.
3. Open-Source Flexibility: Provides full visibility into optimization logic, allowing enterprises to customize evaluation pipelines, integrate proprietary models, and audit safety-critical changes before deployment.

Frequently Asked Questions (FAQ)

1. How does Handit.ai integrate with existing AI stacks? Handit.ai connects via API to major frameworks (LangChain, LlamaIndex) and cloud platforms, requiring only a SDK installation and configuration file to start monitoring and optimizing agents in production.
2. What evaluation metrics are supported? The system supports LLM-as-Judge scoring (using GPT-4 or Claude), custom Python-defined KPIs (e.g., response relevance), latency tracking, and business-specific metrics like conversion rates or error reduction.
3. How are A/B-tested changes deployed safely? Optimized versions are containerized and tested in isolated production slices; users review performance dashboards and approve merges via a GitHub-like interface, with automatic traffic routing and rollback safeguards.
4. Can Handit.ai handle multi-agent workflows? Yes, it maps dependencies between agents, models, and external APIs, enabling root-cause analysis for complex failures and coordinated updates across interconnected components.
5. Is there offline testing capability? All optimizations are first validated against historical production data before A/B testing in live environments, ensuring fixes generalize beyond synthetic datasets.