The AWS Developers Podcast

It's a milestone — episode 200! And to mark the occasion, we're doing something we've never done before: hosting two guests at the same time. James Ward (Principal Developer Advocate at AWS) and Josh Long (Spring Developer Advocate at Broadcom, Java Champion, and host of 'A Bootiful Podcast') join Romain for a wide-ranging conversation about why Java and Spring AI are becoming the go-to stack for enterprise AI development. We kick off with Spring AI's rapid evolution — from its 1.0 GA release to the just-released 2.0.0-M3 milestone — and why it's far more than an LLM wrapper. James and Josh break down how Spring AI provides clean abstractions across 20+ models and vector stores, with type-safe, compile-time validation that prevents the kind of string-typo failures that plague dynamically typed AI code in production. The numbers back it up: an Azul study found that 62% of surveyed companies are building AI solutions on Java and the JVM. James and Josh explain why — enterprise teams need security, observability, and scalability baked in, not bolted on. We dive into the Agent Skills open standard from Anthropic and James's SkillsJars project for packaging and distributing agent skills via Maven Central. We also cover Spring AI's official Java MCP SDK (now at 1.0) and how MCP and Agent Skills complement each other for building capable, composable agents. The performance story is striking: Java MCP SDK benchmarks show 0.835ms latency versus Python's 26.45ms, 1.5M+ requests per second versus 280K, and 28% CPU utilization versus 94% — with even better numbers using GraalVM native images. Josh and James also walk us through Embabel, the new JVM-based agentic framework from Spring creator Rod Johnson, featuring goal-oriented and utility-based planners with type-safe workflow definitions built on Spring AI foundations. We close with a look at running Spring AI agents on AWS Bedrock AgentCore — memory, browser support, code interpreter, and serverless containers for agentic workloads.

Some guests make you want to close your laptop and go build something. Linda Mohamed is one of them. In this episode, Romain sits down with Linda — AWS Community Hero, User Group Leader, Chairwoman of the AWS Community DACH Association, and independent cloud consultant based in Vienna. Linda started as a Java developer in on-premises enterprise environments. Her first AWS touch point? Building an Alexa skill for a smart home product — discovering Lambda almost by accident, and never looking back. Today she's building multi-agent AI systems, running an AI-powered video pipeline with five media customers, and doing it all while being one of the most energetic and generous contributors in the AWS community. Discover Linda's journey from Java developer in telecom to cloud and AI consultant, conference-driven development as a forcing function to ship, building Otto — a multi-agent Slack bot using Crew AI, LoRA fine-tuning, and Amazon Bedrock Agent Core Runtime. Learn about the AI-powered video analysis pipeline she built to solve her own problem and ended up selling to five media customers, vibe coding vs spec-driven development and when each makes sense, and why Clean Code principles still apply when designing agent architectures.

In this episode, Romain sits down with Michael Gasch, Product Manager at AWS for Lambda Durable Functions, to explore one of the most exciting launches in the Serverless space in recent years. Michael shares the full story: from the early days of Lambda and the evolution of the serverless developer experience, to the challenges developers face when building multi-step, stateful workflows — and how Durable Functions addresses them natively within Lambda. Discover the evolution of AWS Serverless and why last year was 'the year of Lambda', key launches including IDE integrations, Lambda Managed Instances, and Lambda Tenant Isolation. Learn what Lambda Durable Functions are and what they are not, the checkpoint-replay model and how it enables resilient, long-running executions, and wait patterns including simple wait, wait for callback, and wait for condition. Explore real-world use cases: distributed transactions, LLM inference orchestration, ECS task coordination, and human-in-the-loop workflows. Michael shares unexpected feedback from customers about architectural simplification, how coding agents like Kiro dramatically accelerate writing Durable Functions, and when to choose Durable Functions vs. Step Functions vs. SQS/SNS. Plus, what's coming next: more regions, and the Java SDK (now available!).

Mike Chambers is back — calling in from the other side of the globe — and he brought a lot to unpack. We pick up threads from our first conversation and follow them into genuinely exciting (and occasionally mind-bending) territory. We start with OpenClaw, the open-source agentic framework that took the developer world by storm. Mike shares his take on why it happened now — not just what it is — and why the timing was almost inevitable given how developers had been quietly experimenting with local agents for the past year. Then we go deep on asynchronous tool calling — a project Mike has been working on since mid-2024 that finally works reliably, thanks to more capable models. The idea: let your agent kick off a long-running task, keep the conversation going naturally, and have the result arrive without interrupting the flow. Mike walks through how he built this on top of Strands Agents SDK and why he's planning to propose it as a contribution to the open-source project. We also explore Strands Labs and its freshly released AI Functions — a genuinely new way to think about embedding generative capability directly into application code. Is this Software 3.1? Mike makes the case, and Romain pushes back in the best way. The episode closes with a look ahead: agent trust, observability with OpenTelemetry, and a thought experiment about what software might look like in five years if the execution environment itself becomes a model.

Join us for an engaging conversation with Chris Miller, an AWS Hero since 2021 and AI Software Engineer at Workato. Chris shares his journey from accidentally winning a DeepRacer competition to becoming a community leader in the San Francisco Bay Area. We dive deep into the realities of AI-assisted development, exploring multi-agent architectures, the Road to re:Invent hackathon experience, and what it's really like to be building in Silicon Valley's AI boom. Discover how Chris moved from DeepRacer champion to AWS Hero and community leader, his experience building a multi-agent imposter architecture featuring Jeff Barr, Swami, and Werner Vogels for the Road to re:Invent Hackathon, and the reality of moving beyond 'vibe coding' to responsible AI development. Learn about multi-agent orchestration patterns, token management, recursion limits, and the current state of AI development in San Francisco. Chris shares insights on developer tools like Kiro, the Strands framework, autonomous agents, and best practices for code review, testing, and transparency in AI-generated code. Whether you're exploring AI-assisted development, building multi-agent systems, or curious about the Silicon Valley AI scene, this conversation offers practical insights from the trenches.