The AWS Developers Podcast

Episode 220

Aug 26, 26 • 00:50:50

Mike Chambers, Senior Developer Advocate for Generative AI at AWS

Harness engineering: are you controlling your AI agents?

What happens when you strip the model from an agent? Everything left is the harness — and engineering that harness is the new discipline every AI builder needs to master. In this episode, Romain sits down with Mike Chambers, Senior Developer Advocate for Generative AI at AWS, fresh from speaking at the AI Engineers World's Fair 2026. They dig into harness engineering — what it means, why it matters, and how it changes whether you're building or using agents. Mike introduces “slop ops” (his term for unconstrained agent deployments), explains the difference between Strands Agents SDK and AgentCore Harness, and shares why small language models are his next big bet. Key takeaways: • Harness engineering is the discipline around everything left when you remove the model from an agent — including tools, skills, memory, context, observability, evaluations, and the agentic loop • The agent you use and the agent you build require different harness thinking — personal coding agents can optimize for productivity, while production agents must manage context, cost, scale, and reliability • AgentCore Harness removes undifferentiated orchestration code — provide a model, system prompt, tools, and skills through configuration, and the service creates and runs the agentic loop • Avoid “slop ops” — agents should generate infrastructure as code rather than directly creating unconstrained cloud resources, keeping deployments repeatable, reviewable, and owned by the team • Evaluation must evolve alongside an agent — it is difficult to retrofit and cannot be treated as a one-time prerequisite or an afterthought • FOMAT, the fear of missing agent time, can push developers toward unhealthy always-on behavior — faster execution still requires space to think deeply about which problems are worth solving • Kiro for iOS enables mobile-first agentic workflows — Mike uses walks to think, dictate ideas, delegate experiments, and return to working prototypes • Small language models can make focused agentic workloads faster and more cost-effective once teams understand the task well enough to specialize the model