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WEBINAR
Modern microservice applications depend on APIs, event streams, and third-party services that aren’t always available when testing needs to begin. Service virtualization removes these bottlenecks by simulating dependent systems, allowing teams to validate applications earlier and deliver software faster.
Watch this demo session to explore practical strategies for testing complex microservice workflows without relying on fully integrated environments.
You’ll see how teams reduce dependency bottlenecks, simulate unavailable services, and use agentic AI workflows to accelerate the creation of API mocks and virtual services—enabling earlier, more reliable validation across functional, negative, performance, and resilience test scenarios.
You’ll learn how to:
The challenge with testing microservices isn’t a lack of testing effort—it’s that the system is constantly changing. Services evolve independently, shared environments become bottlenecks, external dependencies introduce availability and rate-limit issues, and event-driven workflows add another layer of complexity through timing and sequencing.
As a result, testing often becomes a waiting game. Teams wait for environments to stabilize, dependencies to become available, and other services to reach the right state before meaningful validation can begin. The challenge isn’t testing itself—it’s coordinating a constantly changing ecosystem.
A more effective approach is to shift from thinking in terms of live vs. virtual services to a continuous model that uses both.
When real services are available, testing runs against them as normal. But when they’re not—due to outages, instability, or incomplete environments—testing doesn’t stop. It seamlessly fails over to virtual representations of those dependencies.
The goal is uninterrupted validation regardless of system state, enabling teams to test continuously instead of waiting for environments to align.
Modern microservice applications typically rely on two types of service interactions, each requiring a different service virtualization approach. The first is synchronous communication, such as REST and gRPC, where applications send a request and expect a direct response. These virtual services often need to simulate stateful behavior, allowing responses to change based on previous requests or specific input conditions.
The second is asynchronous communication, including event-driven architectures built on technologies like Kafka. Instead of request-response interactions, these systems exchange events across multiple services, requiring virtual services to simulate message flows, event sequencing, delays, retries, and other real-world conditions.
While REST-based synchronous workflows remain common, the growing adoption of event-driven architectures makes support for asynchronous workflows equally essential. Recent AI advancements are also making it much easier to create and manage REST-based virtual services, reducing the manual effort traditionally required to build realistic test environments.
Traditionally, building virtual services required manually interpreting API specifications, defining responses, and configuring test data. Even for relatively simple REST APIs, this could slow teams down and often requires some expertise in API mocking.
Agentic AI is changing that. By using natural language prompts, API definitions, or example requests and responses, teams can now generate REST-based virtual services along with supporting test data in a fraction of the time.
Engineers stay in control of the process, reviewing and refining outputs, but the heavy lifting—initial service creation and setup—is automated. This makes it easier to stand up simulations quickly and begin validating services earlier in the development lifecycle.
These capabilities can be used directly within the Virtualize UI or integrated into broader workflows through its MCP server, enabling teams to connect service virtualization into the same agentic tools and LLM-based environments they already use for code development.
As development accelerates through AI-driven code generation, this capability becomes increasingly important. It allows teams to keep testing aligned with the pace of change, without waiting for dependent systems to be fully built or available.
Agentic AI doesn’t just accelerate how individual developers create virtual services—it also changes how those services are provisioned across the delivery pipeline.
In many organizations, service changes are already tracked in systems like Jira. Instead of treating Jira purely as a planning tool, it can also act as a trigger for automation. When a new service is defined or an existing one is updated, an AI agent can access Jira programmatically through its MCP server, detect the change, extract the relevant context, and initiate downstream actions.
From there, the agent can connect to service virtualization capabilities through an MCP server, enabling programmatic generation and deployment of API mocks based on the latest service definition.
This effectively removes manual steps from the process. Virtual services are created and deployed automatically, without waiting for downstream systems or environment readiness.
The result is continuous execution within CI/CD pipelines, where testing can proceed even as dependent services are still being developed. Once complete, results can be fed back into systems like Jira, closing the loop between development, testing, and planning.
In this model, instead of environment readiness dictating when testing can happen, the pipeline itself becomes capable of provisioning what it needs on demand.
