Watch Building Realistic Service Dependencies With Your LLM Client

LLM clients have quickly become a common tool for developers writing code and working with APIs. But when it’s time to test those APIs, most teams hit a familiar wall. They rely on hand-built mocks and brittle stubs or wait on other teams to provide access to dependent services.

In this session, you’ll see how to use your existing LLM client to simulate the service dependencies you need without switching tools or learning complex workflows. By connecting an LLM client directly to Parasoft Virtualize, you can generate the API simulations for your testing use cases while letting AI drive creation, deployment, and ongoing maintenance.

Watch this session to learn how service virtualization fits into emerging AI-first development workflows. You’ll see a demo of an LLM client generating, deploying, and managing virtual services in real time.

Key Takeaways

• AI agents and LLM clients can now generate, deploy, and maintain simulated service dependencies in real time.
• This approach makes it simple to get realistic API simulations, even if a service isn’t available or finished.
• Workflows can fit directly into the tools developers already use, like GitHub Copilot, VS Code, or terminal-based interfaces.

The Slowdown: Service Dependencies in a High-Speed Development World

Developers today can spin up code at breakneck speed with the help of AI tools. But as soon as any API needs integration testing, things slow down. Why? Because those dependent services—real or mocked—aren’t always ready when you are.

Here’s what the usual process looks like:

1. Write code with your AI assistant (super fast!).
2. Hit a wall waiting for another team’s API or a downstream service.
3. Handcraft mocks or stubs, which break easily or lack realism.
4. Waste time and get frustrated.

AI promises a fix: What if you could generate service simulations using the same LLM client you use for coding?

Using LLM Agents to Simulate Service Dependencies

Parasoft Virtualize now works with popular LLM clients using something called the Model Context Protocol (MCP). Basically, it lets AI agents grab extra powers—like creating and updating virtual APIs on the fly.

Imagine working in your terminal and saying, “Hey, create a fake order service for my test app.” The LLM agent (like Copilot or Warp) pings Parasoft Virtualize, figures out what you need, asks for a name or deployment path if necessary, and sets up a virtual API that responds like the real thing.

Common steps:

• Tell the AI agent what mock or virtual service you need.
• Answer a couple of questions, like what to name it and where to deploy it.
• The AI agent can even probe a real endpoint (if it exists) to sniff out realistic sample data—or just make it up.
• Tweak behaviors, add more sample responses, or enrich the data, all with plain language prompts.

Example Table: Steps to Generate a Virtual Service

It’s flexible—AI can connect to databases, Jira, or pull data from existing code and tests, making your simulations smarter and more up-to-date.

Bringing Service Virtualization Into The Pipeline

One of the cooler parts? This can all be automated in your CI/CD pipeline. When a pull request updates an API contract or adds a new service, the LLM agent can:

• See the changes
• Automatically build a matching virtual API
• Deploy it so developers and testers can hit the ground running

You get dynamic test environments, even if real dependencies aren’t there yet.

How Teams Benefit: From One-Off Mocks to Shared Virtual Services

When teams treat virtual services as shared building blocks rather than just single-use mocks, everyone wins:

• You avoid repeating the same work in different silos.
• Testers can run their own tests without waiting on developers to build special stubs.
• The virtual services stay up to date with new code and API contracts.
• Service virtualization scales across teams and projects.

Research snapshot:
A QA Financial study found companies using Parasoft Virtualize had a 39% shorter average project timeline and a 74% drop in critical defects compared to teams not using service virtualization.

A Boost for Centers of Excellence (COEs) and Everyday Testers

A popular approach to implementing a service virtualization practice in large enterprise organizations is to form a central team (COE) that creates and maintains all the shared virtual services. Unfortunately, this can sometimes cause bottlenecks as testers must request new virtual services from the CEO team to unblock their testing when dependencies become unavailable for testing. Now, with AI agents, individual teams and even non-developers can:

• Request or create new service simulations using natural language prompts
• Update existing virtual endpoints on their own
• Generate virtual services rapidly without leaving their AI workspace enabling earlier integration testing

COEs can spend less time on basic intake work and more time on adoption, training, and supporting complex needs.

What Are Agent Skills?

Think of “agent skills” like reusable prompt recipes or instruction sets for your AI agent. Instead of rewriting commands, you set up skills like “generate security tests from static analysis results” or “synthesize 10 orders with various states for the order API.” These make it easier for agents to follow your team’s conventions automatically.

You can build skills at the personal, team, or organizational level—think mini playbooks for your AI.

Wrapping Up

AI-assisted service virtualization is shaking up how developers and testers deal with dependent services. By letting anyone generate, maintain, and use virtual services using tools they already have, roadblocks come down and testing can happen sooner and more often. Less time waiting, less manual work—and far fewer headaches.

If you’re curious about how to bring this to your team, now’s a good time to try it out—the barriers just got a lot lower.