The AWS Developers Podcast

Yan Cui, an AWS Serverless Hero and independent consultant, explores how Lambda is evolving beyond traditional functions. They compare Lambda with Fargate and ECS, unpack Managed Instances, MicroVMs, Durable Functions, and S3 Files, and examine AI agents, CI/CD runners, observability challenges, and the architecture principle that every component must earn its place.

Mike Chambers, AWS Senior Developer Advocate for Generative AI, explores Jev, TypeSafe AI’s fast decision model. They unpack its Noul, Choice, and Score primitives, gated tool calls in agents, and rapid model routing. The conversation also examines Jev’s role alongside LLMs, potential cost and latency savings, and when ordinary code or specialized models remain the smarter choice.

Most developers say AI makes them more productive. Most companies say their teams aren't shipping faster. What's going on? In this special solo episode, Romain unpacks the data behind the "acceleration whiplash," walks through a four-level AI maturity framework built from hundreds of customer conversations, and shares the practical lessons that separate teams getting incremental gains from those achieving 10x outcomes. This is a recording of his AI-DLC presentation, delivered at the AWS Summit in Zurich and iterated based on feedback from startups, enterprises, and digital-native companies across Europe. Key topics covered: • The adoption paradox: individual productivity up, team delivery flat • Why vibe coding is a mirage for production systems • Theory of constraints applied to AI-era software development • The four-level AI maturity framework: Traditional, AI-Assisted, AI-Augmented, and AI-Native • From prompt engineering to context engineering to loop engineering • Cross-functional teams and the evolution of Amazon's two-pizza teams • AI-DLC workshops: mob elaboration, construction bolts, and continuous delivery • Building AI fluency across your organization • The technology stack for AI-native development • Werner Vogels' Renaissance Developer and T-shaped engineers Chapters: 00:00 Introduction and why this episode exists 01:28 The evolution of coding assistants (2004–2026) 05:01 Systems of agents and software factories 06:27 The adoption paradox: faster developers, slower teams 09:05 Vibe coding is a mirage 10:12 Theory of constraints and The Phoenix Project 12:17 You can't bolt AI on existing workflows 13:03 Start with why: what are you optimizing for? 15:33 The four-level AI maturity framework 24:30 Level 3 and 4: loop engineering and frontier teams 26:57 People, process, and technology transformation 37:56 The AI-DLC technology stack 41:09 Mindset: the Renaissance Developer 43:24 Practical lessons learned at every levelFaros AI Engineering Report 2026 — The Acceleration WhiplashAmazon's Cost-to-Serve Software MetricAmazon Science — Measuring Software Development EffectivenessWerner Vogels' re:Invent 2025 Keynote — The Renaissance DeveloperBoris Cherny on Loop EngineeringNetflix CPTO Elizabeth Stone — Systems Thinkers in the AI EraStrands Agents — Open-Source Agentic FrameworkKiro — Spec-Driven DevelopmentAmazon Bedrock AgentCoreThe Phoenix Project — Gene Kim, Kevin Behr, and George SpaffordStart with Why — Simon SinekTeam Topologies — Matthew Skelton and Manuel Pais

The MCP specification just went stateless. Clare Liguori — Senior Principal Engineer at AWS and core MCP maintainer — explains what changed, why it matters, and what it unlocks for agent developers building with Strands Agents and Kiro. In this episode, Romain sits down with Clare Liguori, Senior Principal Engineer at AWS, to discuss the July 28 MCP spec release, new MCP extensions (Skills, Tasks, Events), Strands Agents Harness SDK, Strands Shell, Physical AI in Strands Labs, and the latest Kiro updates across Web, CLI, IDE 1.0, and iOS. Key takeaways: • MCP goes stateless — the July 28 spec release removes the need for stateful streaming in remote MCP servers, so SaaS providers can drop sticky sessions and load-balancer gymnastics and run each request anywhere. Expect a new wave of remote MCP servers over HTTP. • New MCP extensions framework — features now start as stable extensions before graduating into the official spec: Skills over MCP (bundle a workflow and its tools together), long-running Tasks (kick off builds or jobs without blocking the agent), and Events (trigger always-on agents from external signals like Slack or an earthquake feed). • Strands Agents Harness SDK & TypeScript 1.0 — a more batteries-included harness with context management, compaction, and excellent out-of-the-box file tools, plus the TypeScript SDK reaching 1.0. Upgrade the model ID and your agent gets better. • Strands Shell — a lightweight, in-process agent sandbox written in Rust (cross-platform, Python SDK today). It gives an agent a virtual file system and minimal bash/Lua scripting without a heavyweight VM — great as a safe scratch pad or for scripting tools together. • Strands Labs & Physical AI — an experimental space for bleeding-edge agent ideas, including combining low-latency local VLA models on robots with the long-range, multi-task reasoning of frontier models in the cloud. • AgentCore Managed Harness — a configuration-based way to run agents (prompt, model, Lambda tools, context and session management) that is Strands under the hood, no Python or TypeScript required. AgentCore Gateway added MCP 2026-07-28 support day one, with version negotiation for backward compatibility. • One unified Kiro harness — Kiro Web, iOS, CLI, and IDE 1.0 now share one harness, bringing spec-driven development, hooks, skills, and powers to every client and letting new features ship across clients on the same day. • Automated reasoning in specs — property-based testing plus ambiguity and conflict detection in requirements help you express intent clearly; the permission system is built on Cedar with policy presets like dev shell, trust all, and read all. • Right-sizing specs and collaborating — check specs into code as a snapshot of intent, watch design and task-list length as a signal to split into multiple specs, add per-task validation steps, and collaborate on specs with comments in Kiro Web.With Clare Liguori, Senior Principal Engineer at AWSMCP Specification (2026-07-28)Strands Agents SDK on GitHubStrands Agents Harness SDKStrands ShellStrands Agents TypeScript SDKKiro — AI-Powered Development EnvironmentAmazon Bedrock AgentCore GatewayHow AgentCore Gateway supports the MCP 2026-07-28 specLean languageCedar — Permission LanguageCustodians of Wonder — Eliot Stein (Clare's Book Recommendation)Clare Liguori on LinkedIn

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 modelWith Mike Chambers, Senior Developer Advocate for Generative AI at AWSAI Engineers World's Fair 2026Strands Agents SDKStrands Agents on GitHubStrands Agents Harness SDKAmazon Bedrock AgentCore HarnessKiro for iOSLatent Space PodcastMike Chambers on Coursera

Lilia Abaibourova, Principal Product Manager leading AI-native transformation at Prime Video, drives agentic developer workflows and org-wide AI adoption. She traces Prime Video’s journey from early coding assistants to agent platforms and MCP-powered end-to-end collaboration. The conversation spotlights team structure experiments, where managers orchestrate fleets of agents, and the shift from code generation to verification and validation.

Laura Skylaki, VP of AI Engineering at Thomson Reuters who builds enterprise AI platforms, explains how a true platform supports thousands with persona-driven services. She highlights an evaluation-first approach, when not to use agents, AWS-based architecture with AgentCore and Strands, and how AI coding tools shift engineers toward system design.

They unpack why most AI-generated code never reaches production and where that waste hides. They explain the verification triangle that makes audits the real pace-setter for delivery. They introduce cost per accepted change as a metric to expose wasted token and human effort. They argue for specs as alignment tools and for tiny, focused agents to cut cost and failure surface.

Most MCP tools today return text. But what if your agent could render a chart, a form, or a full dashboard right inside the chat? That's what MCP apps do — and they're already live on ChatGPT, Claude, and Amazon Quick. Romain sits down with Luigi Pederzani, co-founder of Manufact (the company behind mcp-use, 10K+ GitHub stars), to explore MCP apps — the first official extension of the MCP protocol that lets servers return interactive UI inside AI chat interfaces. Key takeaways: • What MCP apps are — Standard MCP returns text and actions; MCP apps let a server send back a piece of interface (a form, a chart, a dashboard) that renders right inside the chat. It became the first official extension of the protocol this year, growing out of MCP-UI. • UI drives retention — Amplitude saw 2x retention for users exposed to a chart-rendering MCP app versus text-only responses. UI lets software products stay experiences, not just systems of record. • AI apps are the new browsers — Extending Paul Graham's thesis, every software product will be rendered inside AI chats, pulling data and structure from different sources the way we switch tabs today. • Building with mcp-use — Reuse existing React components with minimal changes; the useMcp hook bridges tool arguments (filled by the agent) into component props. The server stays a normal MCP server, the client is the host, and the view runs in a sandboxed iframe. • Interactivity and safety — Iframes are battle-tested and the model-to-server communication is standardized. UI can send events back to the model, so clicking a chart element can trigger another tool call in the chat. OAuth is now standard for production MCP servers. • Tool design best practices — Don't wrap OpenAPI specs directly as MCP. APIs are granular and atomic; MCP tools should serve a task end-to-end so agents don't get confused on ordering. Limit the number of tools exposed; progressive disclosure is now handled by the major clients. • MCP as the A2A protocol — A2A never really landed, and MCP is becoming the agent-to-agent protocol, with companies embedding an agent as a single tool whose main argument is a prompt. • Getting to production — Start with a plain MCP server, then add UI. Skills are now part of the product, and Manufact focuses on agent-readiness of the SDK, testing across clients, deployment, auth, observability (OTEL), and per-tool analytics. • AWS integration — mcp-use can sit on the server side while AgentCore Gateway sits in front to handle enterprise concerns like auth policies and routing. • What's next — Exposing skills directly from MCP servers (rather than decoupled files), and the next stateless-by-default release of the protocol.With Luigi Pederzani, Co-founder of Manufactmcp-use SDK (GitHub)Manufact CloudOra.ai — Agent Readiness ScannerAmazon Bedrock AgentCore Gateway — MCP Server TargetsAgentMail — Email Infrastructure for AgentsThinking, Fast and Slow — Daniel Kahneman (Luigi's Book Pick)Luigi Pederzani on LinkedIn

Aaron Tummon, a software engineer building agentic AI and Cloud Copilot at Genesys. John Sexton, an engineering manager who moved Cloud Copilot into production. They talk scaling frameworks and why Strands worked better. They discuss agents-as-tools orchestration, tight context management, prompt caching for cost savings, and relentless testing to catch invisible prompt drift.