In this episode of the Cloud Native Testing Podcast, Ole Lensmar sits down with Sahana Nagabhushan to tackle the complexities of software lifecycle inefficiencies and developer productivity.

Drawing from her extensive product management experience at Fidelity Investments and Workday, Sahana breaks down why companies burn millions of dollars and years of engineering time building custom Internal Developer Portals (IDPs) like Backstage. Instead of relying solely on generic frameworks, she advocates for implementing a "production readiness layer"—a critical bridge between writing code and shipping it confidently to live environments.

Ole and Sahana also dive deep into the realities of AI in modern software development. They discuss why AI-generated code still desperately requires human oversight, the current limitations of AI in generating deterministic tests, and the strategic value of integrating existing top-tier tools (like Testkube) to maintain high-quality standards without reinventing the wheel.

Key Takeaways:

• The Cost of DIY Platforms: Why building internal developer portals from scratch can cost companies up to $16 million with delayed ROI.
• Defining Production Readiness: How companies can create a "trust layer" to ensure code is fully tested, governed, and truly ready for deployment.
• Backstage vs. Purpose-Built Tools: A candid discussion on why generic plugin frameworks might not solve core quality and testing issues.
• AI's Role in Testing: Why human context, sentiment, and oversight remain crucial when navigating AI-generated code and test creation.

Welcome to the first edition of the Cloud Native Testing Podcast for 2026! In this episode, host Ole Lensmar is joined by Yacine Kheddache to dive deep into Microcks, a CNCF Sandbox project dedicated to API mocking and simulation.

As cloud-native architectures grow more complex, the need to decouple services during development is critical. Yacine explains how Microcks serves as a "Swiss Army Knife" for developers, offering a single solution to mock and test REST, gRPC, GraphQL, and Event-Driven protocols (like Kafka and NATS). They discuss the tool's evolution from a centralized Kubernetes operator to a developer-friendly utility that runs natively in IDEs and pipelines, enabling true "shift left" testing.

Later in the conversation, they explore the intersection of API testing and Artificial Intelligence. Yacine details how Microcks is embracing the AI era by using Copilots to generate mock data and leveraging the Model Context Protocol (MCP) to make existing APIs accessible to LLMs.

Key Topics Discussed:

• The CNCF Journey: Microcks’ status as a community-driven Sandbox project.
• Polyglot Support: Mocking REST, GraphQL, gRPC, and AsyncAPI with one tool.
• The Testing Lifecycle: How to reuse mock data artifacts for automated contract and conformance testing in CI/CD.
• Shift Left: Moving testing from QA environments to local developer laptops and IDEs.
• AI & MCP: Generating datasets with AI and exposing APIs as tools for AI Agents using the Model Context Protocol.

In this episode, Ole Lensmar joins Richard Li, founder of Polar Sky, to explore how testing strategies must evolve for the age of AI. They discuss the journey of cloud-native tools like Telepresence and Richard's key insight for agentic coding: using automated tests as the "context" to guide AI behavior, rather than relying on complex prompts to describe system rules.

The conversation also digs into the mechanics of building AI-driven software, emphasizing why "evals" are critical for measuring success. Richard shares practical strategies for treating evaluations as data management problems and explains how his team uses simple AI agents to "babysit" CI pipelines and automatically resolve flaky tests.

Topics discussed:

• The Evolution of Telepresence: Moving from local debugging to headless execution in CI pipelines.
• Tests as Context: Why running a test suite is more effective than complex prompting for AI agents.
• Agentic Coding Strategy: Shifting focus from unit tests to integration and behavioral verification.
• AI Evals: Why evaluation is the most critical aspect of building reliable AI products.
• Babysitting CI: Using simple AI agents to identify and retry flaky tests automatically.