Harshvardhan Parmar
Maintainer @Microcks
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Harshvardhan is an enthusiastic open-source contributor, a passionate advocate for cloud-native technologies and maintainer of Microcks, an open-sorce CNCF Sandbox project. He was Google Summer of Code (GSoC) 2024 student and LFX mentee in 2025 mentorship under Microcks. He leads Microcks-CLI, a CLI tool for managing Microcks.
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AI Writes the Spring Boot API, the Contract Keeps It Honest
AI coding agents can now generate Spring Boot APIs incredibly quickly. But speed creates a new problem: how do we know the generated implementation actually matches the API we designed?
A service can compile, start successfully, and even pass generated tests while still drifting from its contract through incorrect fields, enum values, status codes, validation rules, or response behaviour.
In this session, we’ll explore a spec-driven development workflow where the API contract remains the source of truth. We’ll start with a reviewed API specification, use it to guide an AI agent while building the Spring Boot implementation, and continuously validate the running service against the expected contract.
We’ll also look at how contract failures can be fed back into the coding agent to create an iterative repair loop until the implementation behaves as expected.
Dynamic Mocking for Event-Driven APIs: A Cloud-Native Approach with Kubernetes
Mocking event-driven APIs is much harder than mocking REST endpoints. In asynchronous architectures powered by Kafka, MQTT, or AMQP, messages are contextual, time-sensitive, and schema-driven — making static, hardcoded mocks quickly obsolete. To truly support developer velocity and early integration, teams need dynamic mocks that behave like real event producers and consumers.
In this session, we’ll explore how to bring realistic, dynamic mocking to event-driven systems using Kubernetes-native tooling. You’ll learn how to automatically generate and deploy mocks from AsyncAPI contracts, simulate message streams over Kafka, MQTT, or AMQP all inside your Kubernetes environment.
We’ll discuss:
- Managing and generating dynamic mocks from AsyncAPI specifications
- Running event producers and consumers as Kubernetes-native components
- Using Kubernetes-native tools like Microcks to orchestrate and scale mocks dynamically
- Keeping mock behavior realistic as schemas and topics evolve
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