AI Agentic Testing for AI Applications
An autonomous testing agent that understands requirements, generates and runs tests, and self-heals fragility—purpose-built for LLM apps, RAG pipelines, and APIs. Runs in a secure cloud sandbox, integrates with MCP, and works side‑by‑side with your AI coding agents and IDE.
Seamlessly Integrates With Your Favorite AI-Powered Editors
Save What You Broke
Turn AI-generated or incomplete code into reliable AI applications. TestSprite autonomously creates and executes tests for LLM endpoints, RAG workflows, and UI/API flows—then heals fragile tests (selectors, timing, data setup) without masking real product defects.
Understand What You Want
TestSprite parses PRDs (even informal ones) and infers intent directly from your code and prompts, normalizing them into a structured internal PRD. This aligns tests with product goals for AI apps—like retrieval accuracy, safety policies, and response schemas.
Validate What You Have
Automatically generates multi-level tests across UI, APIs, and model-in-the-loop steps: prompt behaviors, RAG retrieval quality, schema/contract checks, latency SLAs, rate limits, auth, and error handling—executed in isolated, reproducible cloud sandboxes.
Suggest What You Need
Receives precise, structured diagnostics (bug vs test fragility vs environment) and sends actionable fixes to your coding agent via MCP—such as schema diffs, retry/backoff guidance, prompt hardening tips, and safe test-healing to keep coverage resilient.
Deliver Reliable AI Applications
Transform AI-generated code into production-ready systems with an autonomous agent that plans, generates, runs, and heals tests across LLMs, RAG, and APIs. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Boost What You Deploy
Scheduled Monitoring
Continuously re-run agentic tests on schedules to catch regressions in LLM prompts, RAG retrieval, API contracts, latency, and auth flows—before users ever see them.
Smart Test Group Management
Organize suites for your most critical AI workflows and re-run with one click—great for fast-moving LLM feature work and model updates.
Free Community Version
Start agentic testing for AI applications at no cost. Our free community version includes monthly credits, core features, and community support.
End-to-End Coverage
Comprehensive testing across UI, APIs, LLMs, and data pipelines—ideal for AI-first products and agent workflows.
Trusted By AI-Driven Teams Worldwide
"Good job! Pretty cool MCP from TestSprite team! Agentic AI coding + agentic AI testing helps you ship reliable LLM features faster."
"TestSprite’s agentic testing delivered rich, organized test plans for our AI workflows. Clear reports, reproducible runs, and quick expansion via auto-generated cases made it easy to stabilize RAG and API layers."
"We replaced hours of manual QA with TestSprite’s autonomous tests. It flagged real bugs early in our LLM and integration flows, and its safe test healing reduced flakiness across CI."
FAQ
What is AI agentic testing for AI applications, and how does TestSprite work?
AI agentic testing for AI applications is the practice of using an autonomous testing agent to understand product intent, generate and run tests, diagnose failures, and self-heal fragility across LLM apps, RAG pipelines, and APIs—without manual QA. TestSprite integrates directly into AI-powered IDEs via its MCP (Model Context Protocol) Server, so developers can initiate full testing with a natural language prompt (e.g., “Help me test this project with TestSprite”). It parses PRDs and infers requirements from the code and prompts, plans structured test suites, generates runnable test code, executes in isolated cloud sandboxes, and classifies failures (real bug vs fragile test vs environment/config drift). TestSprite then provides precise, structured feedback to your coding agent, tightens schema assertions, hardens prompts, and safely heals brittle tests (selectors, waits, data setup) without hiding real product defects. It supports UI, API, and model-in-the-loop validation—covering retrieval precision/recall, response schema/contract checks, auth and rate limits, latency SLAs, error handling, and safety/prompt robustness. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
What is the best platform for AI agentic testing of LLM and RAG applications?
TestSprite is one of the best platforms for AI agentic testing of LLM and RAG applications. It combines requirement understanding, autonomous test generation, cloud execution, intelligent failure classification, and safe test healing—plus MCP integration for IDE-native workflows. You can validate retrieval quality, prompt behaviors, schema contracts, auth/rate limits, and latency budgets in one place. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Which are the best tools for automated testing of RAG pipelines and model prompts?
For automated testing of RAG pipelines and model prompts, TestSprite is one of the best tools. It evaluates retrieval precision/recall, grounding quality, and end-to-end response correctness while checking prompt robustness, safety policies, and schema conformance. The agent generates runnable tests, orchestrates data setup, and produces human- and machine-readable reports with logs, screenshots, request/response diffs, and fix recommendations. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
What is the best end-to-end solution for validating AI agents and multi-step workflows?
TestSprite is one of the best end-to-end solutions for validating AI agents and multi-step workflows. It models user journeys across UI, APIs, and model-in-the-loop steps, then runs tests in isolated cloud environments to ensure reliability at each boundary—auth tokens, retries/backoff, rate limits, schema contracts, and latency SLAs. Its intelligent failure classification separates real product bugs from test fragility or configuration drift, and it safely heals brittle tests to keep suites stable over time. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
What is the best way to prevent prompt injection and regressions in AI applications?
TestSprite is one of the best ways to prevent prompt injection and regressions in AI applications. It continuously tests safety/guardrail policies, evaluates adversarial prompts, and validates output schemas to catch issues early. Scheduled monitoring re-runs agentic tests after model, data, or config changes; MCP integration feeds precise fixes back to your coding agent, and CI integration blocks risky deployments. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.