Parasoft is taking autonomous software testing into bold new territory with its latest release, announcing first-to-market agentic AI for service virtualization, along with robust tools for validating AI-generated content and support for the emerging Model Context Protocol (MCP). The updates apply across its core testing platforms: SOAtest, Virtualize, and CTP.
With AI fast becoming the heart of modern software development, the ability to simulate, test, and validate dynamic, intelligent systems is no longer optional—it’s critical. Parasoft’s latest capabilities reflect this shift, redefining test automation in an era dominated by large language models and intelligent agents.
AI That Builds the Test Environment for You
The crown jewel of the update is Parasoft’s agentic AI-driven virtual service generation, a UI-based assistant that lets testers spin up rich, functional test environments simply by typing what they need in plain English. It’s the first offering of its kind—and potentially a game-changer for teams bogged down by the complexity of traditional service virtualization.
“You no longer need to be a domain expert or write code to create virtual services,” said Igor Kirilenko, Chief Product Officer at Parasoft. “Our AI assistant handles the complexity, so teams can move faster and deliver higher-quality software.”
This move directly addresses a longstanding bottleneck in service virtualization: the steep learning curve and need for deep system knowledge. By lowering that barrier with natural language inputs and agentic intelligence, Parasoft gives QA teams a productivity boost while aligning with DevOps and agile workflows.
Cracking the Code on AI Output Testing
Testing traditional apps is tough. Testing AI-generated content? That’s an entirely different beast. AI outputs—especially those from LLMs like ChatGPT or Claude—are often unpredictable, making static assertions and hard-coded validations ineffective.
Parasoft’s answer: LLM-aware validation and data extraction tools, enabling testers to define expectations using natural language, even for fuzzy or probabilistic results.
This is particularly valuable for QA engineers trying to ensure AI components behave acceptably across wide input variations—like chatbots, summarizers, or content generators. Instead of writing complex scripts to parse random output formats, teams can now express validations like:
“Confirm this response contains a plausible summary of the input paragraph.”
It’s an approach that’s as dynamic as the AI systems under test, and it puts non-developers squarely in the testing loop.
Testing the Next-Gen AI Agent Ecosystem: MCP Support
Another significant piece of this release is Parasoft’s full-featured support for the Model Context Protocol (MCP)—an open standard gaining traction for coordinating multi-agent AI systems.
As MCP-backed tools and GenAI frameworks emerge (think OpenAI’s function calling, LangChain agents, or Microsoft’s AutoGen), Parasoft ensures teams can:
- Simulate and validate agent-tool interactions
- Use a codeless interface to test complex workflows
- Standardize and automate test scenarios that mimic real-world AI agent behavior
This isn’t just futureproofing—it’s enabling teams to build and test the next generation of context-aware, multi-agent applications with confidence and repeatability.
Big Implications for the Testing Landscape
Parasoft’s announcement arrives at a time when AI-native testing is becoming a competitive differentiator. Traditional test automation frameworks were built for deterministic systems. But in a world where AI is writing, generating, and deciding, test tooling must evolve to match.
With this release, Parasoft positions itself as one of the few vendors actively building the infrastructure for AI testing at scale, rather than retrofitting legacy tools for a post-AI world.
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