Harness engineering involves designing the tools, context, and feedback around an AI model. These elements guide the agent’s work and give developers ways to investigate failures and intervene.
Recorded at Agent Conf 2026 in Warsaw, this episode of React Universe On Air brings together Anisha Malde from Amazon and our host Łukasz Chludziński. They trace the move from prompt engineering to context and harness engineering, connecting these ideas to familiar practices such as testing, task breakdown, and continuous integration.
Drawing on examples from Amazon Devices Builder Tools (ADBT), Vega OS, and React Native, they discuss how platform knowledge becomes reusable agent skills. The conversation also examines how developers maintain their technical judgment as AI takes on more implementation work.
What is harness engineering?
Anisha describes an agent as a model combined with a harness: the surrounding tools, context, and feedback that shape its behavior. She connects this to the test suites and engineering processes teams already use to guide development. Prompts remain part of that system.
Anisha identifies three goals for AI engineering: getting the model to do the intended job, doing it reliably, and being able to intervene and understand failures.
AI-assisted development across TV platforms
Amazon Devices Builder Tools combines tools exposed through the Model Context Protocol (MCP) with agent skills and workflows for Amazon device development. Anisha discusses building React Native apps for Vega OS and migrating existing apps from Fire OS, alongside community efforts to share knowledge across platforms. She also explains where platform-specific requirements still need attention.
How does platform knowledge become an agent skill?
Knowing which skills and tools to give an agent requires knowledge of the platform and the task. Anisha and Łukasz discuss how individual expertise can become shared guidance, from TV focus management to React Native best practices.
Anisha describes a community agent skill for moving from Vega to Expo and explains how she combines Amazon’s tooling with Callstack’s React Native best-practices skills. They also consider which workflows teams can standardize and where developers need room to adapt.
Keeping technical judgment as AI takes on more coding
Łukasz raises a concern about maintaining platform expertise when developers spend less time working directly with APIs and implementation details. Together, they explore the tension between abstracting away routine work and retaining enough knowledge to diagnose unfamiliar bugs and choose the right tools.
Applying engineering practices to agent workflows
Task breakdown, specifications, and CI/CD return throughout the discussion. Anisha draws parallels between these established practices and the context, token budgets, and harnesses used with agents. They close by considering how AI can give developers more space to focus on the experience they want users to have.
Watch the episode to learn how harness engineering and reusable agent skills fit into development workflows, and what developers need to stay in control.
Resources
- Anisha Malde on LinkedIn, GitHub, and X
- Łukasz Chludziński on LinkedIn and X
- Amazon Devices Builder Tools for AI: getting started
- Vega OS developer documentation
- Amazon Devices community agent skills
- Callstack’s React Native best practices agent skill
- OpenIAP: getting started
- Model Context Protocol documentation (further reading)
- Agent Skills overview (further reading)
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