Podcast
The AWS Developers Podcast
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Stay updated on the latest AWS news and insights for developers, wherever you are, whenever you want.
32episodes
Episodes
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5 Quality Gates That Let You Ship 250% Faster with AI Coding Agents
1w ago01:01:43Tap to summarizeHow do you give 120+ engineers AI coding agents — and NOT break production? Ryan Cormack, Principal Engineer at Motorway and AWS Community Builder (recognized as a Renaissance Developer by Werner Vogels), shares the exact system his team uses to ship 250% more deployments while keeping quality high. In this episode, we break down the 5 quality gates that let Motorway's engineering teams move faster without sacrificing reliability: spec-driven planning to catch design issues before a single line of code is written, AI-assisted code review to verify code matches the plan, deterministic tests (unit + integration) as an automated safety net at the boundary, cyclomatic complexity checks to keep code maintainable, and human review as the final gate that stays human. Ryan explains how cross-functional DevOps teams — organized like Amazon's two-pizza teams with full end-to-end ownership — enable faster AI adoption. He walks through running parallel agents to explore multiple solutions simultaneously, building custom tools on top of ACP (Agent Client Protocol), and sharing agent configurations across 120+ engineers via a Git + S3 pipeline. The conversation also covers the Renaissance Developer mindset that Werner Vogels introduced at re:Invent 2024: curiosity, ownership, systems thinking, communication, and experimentation. Ryan shares how Motorway embraces this philosophy by encouraging engineers to build their own tools, experiment with new technologies in parallel, and focus engineering time on design and planning rather than writing code. Whether you are scaling AI coding assistants across a large engineering org, building quality gates for agentic development, or rethinking how your team ceremonies and processes should evolve in the age of AI, this episode offers a practitioner's blueprint from someone delivering measurable results: 250% more deployments, 4x engineering throughput, and no uptick in production incidents.With Ryan Cormack, Principal Engineer at MotorwayRyan Cormack — Personal WebsiteRyan Cormack — MediumRyan Cormack — GitHubACP and Strands — An Open Source Match (Ryan Cormack)Motorway — UK's Fastest Growing Used Car MarketplaceWerner Vogels — The Renaissance Developer (re:Invent 2024)The Phoenix Project — Gene Kim, Kevin Behr, George SpaffordThe Unicorn Project — Gene KimThe Architect Elevator — Gregor HohpeStrands Agents SDK — Open SourceKiro — AI-Powered Development Environment
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Dark Factories: Why Your AI Coding Setup Is Already Outdated
2w ago00:49:10Tap to summarizeYou're using Copilot. Maybe you've tried Cursor or Claude Code. But what if that's already the tail end of the AI wave? In this episode, Romain sits down with Christian Weichel, CTO and co-founder of Ona (formerly Gitpod), to explore 'dark factories' — autonomous AI agents that pick up work, write code, open PRs, and ship fixes while you sleep. No laptop required. Chris shares how his team of ~20 engineers went from 450 open pull requests to a streamlined, auto-approving system — all while staying SOC 2 compliant. He walks through the 3 stages of AI in the SDLC (better autocomplete → software conductor → background agents), the governance model that makes background agents safe for regulated enterprises, and why terminal-based coding agents' days are numbered. The conversation covers the risk ladder approach to auto-approving PRs, how isolated cloud development environments provide the security and autonomy agents need to operate safely, multi-agent code review with meta-reflection, and why accelerating implementation without accelerating review creates a bottleneck that breaks teams. Christian also shares his perspective on architecture governance, cognitive load management when running parallel agents, and why the future of IDEs will look different but won't disappear. Whether you are adopting AI coding assistants, building governance frameworks for agentic development, or exploring how background agents can automate your SDLC end-to-end, this episode offers a practitioner's view from someone who's been shipping with autonomous agents in production.With Christian Weichel, CTO & Co-founder, Ona (formerly Gitpod)Christian Weichel — Personal SiteOna — Background Agents for Software DevelopmentThe Phoenix Project — Gene Kim, Kevin Behr, George SpaffordThe Unicorn Project — Gene KimThe Origins of Efficiency — Matt Might (Book Recommendation)The Rise of the Software ConductorThe Software Conductor's HandbookLM Studio — Run Local LLMsOllama — Run Local Models
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LLM-as-a-Judge, Quotation Fidelity, and A/B Testing Models: AI Publishing at Scale
3w ago00:50:13Tap to summarizeWhat happens when a data scientist builds a generative AI proof of concept — and it scales to 700,000 articles and 4 billion page views? Recorded live at AWS Summit London, Romain is joined by Lewis James, Senior Data Scientist at Reach PLC — the UK's largest commercial publisher with over 120 brands including the Mirror, the Express, and OK Magazine. Lewis shares the full journey from GPT-2 experiments to a production AI publishing platform called Launchpad that now assists with 20–30% of the portfolio's daily article output. We explore how the team earned journalist trust by focusing on mundane tasks first, how they built multi-model pipelines with quotation fidelity checks to avoid misquoting, and why working backwards from users — not pushing technology — drove adoption where others failed. The conversation covers the technical evolution from prompt engineering to fine-tuning, model distillation, and agentic workflows built with the Strands Agents SDK running on Amazon Bedrock AgentCore. Lewis also introduces the concept of 'vibe publishing' — giving journalists a chatbot interface with more creative freedom — and discusses how evaluation strategies differ when you're measuring editorial tonality versus factual accuracy. Whether you are building AI-assisted content pipelines, navigating enterprise AI adoption, or thinking about how to earn user trust for generative AI tools, this episode offers a rare look at what three years of production generative AI looks like at massive scale.With Lewis James, Senior Data Scientist at Reach PLCReach PLC — UK's Largest Commercial PublisherAmazon Bedrock AgentCoreStrands Agents SDK — Open SourceAmazon Bedrock Model DistillationAmazon Bedrock LLM-as-a-Judge EvaluationsThe 4 Stages of Psychological Safety — Timothy R. ClarkWerner Vogels — The Renaissance Developer (re:Invent 2025)
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AI Agents, Friction, and the Future of Developer Experience
May 500:51:12Tap to summarizeAI agents are transforming how we write, test, and ship software — but are they actually improving the developer experience? Recorded live at AWS Summit London, Romain is joined by Tomasz Ptak — AWS AI Hero and Senior Engineer at Duco — for a candid conversation about developer experience friction in the age of AI agents. We explore what happens when teams adopt AI coding assistants without thinking about the developer workflow holistically — from context overload and broken feedback loops to the hidden costs of AI-generated code that nobody reviewed. The conversation draws on Werner Vogels' 'Renaissance Developer' keynote from re:Invent 2025, where he argued that developers need to be broader thinkers, not just faster coders. Tomasz shares his perspective on what great developer experience looks like when AI agents are part of the picture, how the AWS AI League is helping developers build real agent skills through gamified competition, and why critical thinking about AI adoption matters more than blind acceleration. We also discuss psychological safety in engineering teams — drawing on Brené Brown's work on vulnerability — and why the best developer tools are the ones you barely notice, as Don Norman taught us decades ago. Whether you are building AI agents, designing internal developer platforms, or evaluating how AI tools fit into your team's workflow, this conversation offers a grounded, human-centered perspective on reducing friction and improving developer experience in 2026 and beyond.With Tomasz Ptak, AWS AI Hero, Senior Engineer at DucoAWS AI League — Gamified AI CompetitionAWS AI League 2026 Championship — BuilderCenterTomasz Ptak's Blog — mediocr.isFrictionless — Nicole Forsgren & Abi NodaThink Again — Adam GrantRising Strong — Brené BrownThe Design of Everyday Things — Don NormanTomasz Ptak — AWS Machine Learning Hero
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The Evolution of Microservices: Agents, Monoliths, and the Patterns That Never Die
Apr 2900:46:52Tap to summarizeRecorded live at AWS Summit London, Matheus Guimaraes — Senior Developer Advocate at AWS and microservices specialist with over 25 years in tech — joins Romain to explore how agentic AI is reshaping the way we think about distributed systems architecture. From Martin Fowler's 2014 definition to agentic microservices in 2026, Matheus unpacks why the same distributed systems patterns — single responsibility, context dilution, failure modes — keep resurfacing in every new wave of architecture. The conversation covers the monolith vs. microservices debate as a deliberate architectural choice rather than accidental spaghetti, modular monoliths with Spring Modulith, and how AI coding assistants like Kiro are changing the architect's role from writing boilerplate to making higher-order design decisions. Matheus introduces his concepts of 'smart APIs,' 'monolithic agentic microservices,' and 'specialized agentic microservices' — and explains his talk 'Is It Agent?' on when to reach for agents vs. traditional applications. We dig into the serverless primitives purpose-built for agentic workloads: Amazon Bedrock AgentCore Runtime for long-running agent processes, AWS Lambda Durable Functions for multi-step workflows, and the AWS DevOps Agent for autonomous incident response. We also explore integration patterns with MCP and Google's A2A protocol, the 'lost in the middle' problem with context dilution, and why critical thinking about AI adoption matters more than ever. Whether you are decomposing a monolith or designing your first agentic system, this conversation connects the dots between a decade of microservices wisdom and the agentic future.With Matheus Guimaraes, Senior Developer Advocate, AWSMartin Fowler — Microservices (2014)Spring ModulithAmazon Bedrock AgentCoreAWS Lambda Durable FunctionsAWS DevOps AgentModel Context Protocol (MCP)Agent-to-Agent Protocol (A2A) — GoogleKiro — AI-Powered Development EnvironmentBuilding Microservices — Sam NewmanMonolith to Microservices — Sam NewmanThe Art of Game Design: A Book of Lenses — Jesse SchellMatheus Guimaraes — codingmatheus.com
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How Can AI Agents Cut Support Resolution Time by 95%?
Apr 2200:51:36Tap to summarizeCyberArk's support team was drowning in logs. With 40+ products across SaaS and self-hosted environments, each generating logs in different formats, support engineers were spending days just preparing data before they could even start investigating a customer issue. Complex cases took up to 15 days to resolve. Moshiko Ben Abu, a Software Engineer at CyberArk — now part of Palo Alto Networks — built an AI-powered system that changed all of that. In this episode, he walks us through the full architecture: replacing manual regex parsers with AI-generated grok patterns using Amazon Bedrock and Claude, storing structured data in Apache Iceberg tables via PyIceberg with automatic schema evolution, and querying everything through Athena — all while keeping PII masked and data encrypted in S3. But the real breakthrough came with agents. Moshiko describes how he moved from single-product Bedrock agents to a swarm of specialized AI agents built with the Strands framework, where agents investigating product A can autonomously call agents for product B and C to trace root causes across the entire stack. Cases that took 15 days now resolve in hours. Simple cases drop from 4-6 hours to 15-30 minutes. Engineers handle 4x more cases per day. We also dig into the security layer — Cedar policies and Amazon Verified Permissions for agent authorization, the identity integration with AgentCore, and what's coming next: S3 Tables, AgentCore in production, and cross-platform agent collaboration with Palo Alto. Moshiko's advice for developers getting started? Learn IAM first, then compute, then databases — and write everything in CDK.With Moshiko Ben Abu, Software Engineer, CyberArk (a Palo Alto Networks company)How CyberArk Uses Apache Iceberg and Amazon Bedrock to Deliver up to 4x Support Productivity — AWS BlogApache Iceberg on AWSPyIceberg — Apache Iceberg Python LibraryAmazon Bedrock AgentCoreStrands Agents — Open-Source Agentic FrameworkCedar Policy LanguageAmazon Verified PermissionsAmazon S3 TablesKiro — AI-Powered Development EnvironmentAWS CDK (Cloud Development Kit)Ran the Builder — Ran Isenberg's Serverless BlogRan Isenberg — AWS Serverless Hero
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Spec-Driven Development and the AI Unified Process — with Simon Martinelli
Apr 1401:10:57Tap to summarizeSimon Martinelli is a Java Champion, Vaadin Champion, and Oracle ACE Pro with over three decades of experience building enterprise software. In this episode, he introduces the AI Unified Process (AIUP) — a methodology he created that combines the rigor of the Rational Unified Process with modern AI-assisted development, and makes a compelling case for why specifications, not code, should be the source of truth. We explore the difference between system use cases and user stories, and why use cases — with their actors, preconditions, main flows, alternative flows, and business rules — give AI agents far better structure to generate working code. Simon walks through the four phases of AIUP: Inception, Elaboration, Construction, and Transition, showing how specs, code, and tests evolve together iteratively while staying in sync. On the architecture side, Simon advocates for Self-Contained Systems over microservices — vertical slices that include UI, backend, and database together, reducing cognitive load for both developers and AI agents. His tech stack of choice is Vaadin for full-stack Java UI, jOOQ for type-safe explicit SQL, and Spring Boot as the application framework — a combination he argues is uniquely well-suited for AI-driven development because it keeps everything in one language with no hidden behavior. We also dig into testing strategies with Karibu Testing for browserless Vaadin tests and Playwright for end-to-end coverage, how teams of two working on bounded contexts with trunk-based development are shipping faster than ever, and why the era of AI is bringing back the Renaissance developer — the generalist who understands the full stack from business requirements to production deployment.With Simon Martinelli, Java Champion, Vaadin Champion, Oracle ACE Pro — Software Architect & TrainerAI Unified Process (AIUP)Spec-Driven Development with AI — Simon MartinelliWhy Vaadin Is Perfect for AI-Driven DevelopmentWhy Vaadin and jOOQ Are a Natural Fit for AI-Driven DevelopmentBrowserless Testing of Vaadin Applications with Karibu TestingGoodbye Microservices, Hello Self-Contained Systems — Simon MartinelliSelf-Contained Systems ArchitectureVaadin FrameworkjOOQ — Type-Safe SQL in JavaKaribu Testing — GitHubPlaywright — End-to-End TestingSimon Martinelli's Blog
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Neurosymbolic AI: Combining GenAI with Mathematical Proof — with Danilo Poccia
Apr 801:07:30Tap to summarizeWhat if you could combine the creative power of generative AI with the mathematical certainty of formal verification? In this episode, Danilo Poccia — Principal Developer Advocate at AWS — breaks down automated reasoning, a field of AI that has been quietly powering critical AWS services for years and is now becoming essential for production AI systems. We explore why generative AI alone is not enough for high-stakes applications, and how automated reasoning provides mathematical proof — not probabilistic guesses — that your AI agents are following the rules. Danilo traces the roots of automated reasoning back to the 'symbolist' branch of AI, explains how AWS has used it internally for years to verify S3 bucket policies, encryption algorithms, and network configurations, and shows how it now converges with neural networks in what researchers call neurosymbolic AI. On the practical side, we dig into Amazon Bedrock Guardrails with Automated Reasoning checks — the first and only generative AI safeguard that uses formal logic to verify response accuracy. Danilo walks through how developers can use policy verification for agentic systems and tool access control with Cedar, and how AgentCore Gateway fits into the picture for managing MCP-based tool interactions at scale. We also cover the open source landscape: Dafny for verification-aware programming, Lean as a theorem prover, Prolog for logic programming, and the growing ecosystem of MCP servers that bring these capabilities into everyday development workflows. Whether you are building AI agents for production or just curious about what comes after prompt engineering, this conversation will change how you think about AI reliability.With Danilo Poccia, Principal Developer Advocate, AWS Developer RelationsAmazon Bedrock Guardrails — Automated Reasoning ChecksAutomated Reasoning Checks Rewriting Chatbot — Reference ImplementationAmazon Bedrock Samples — Responsible AI on GitHubA Gentle Introduction to Automated Reasoning — Amazon ScienceWhat is Automated Reasoning? — AWSCedar Policy Language — GitHubAmazon Bedrock AgentCore GatewayDafny — Verification-Aware Programming LanguageLean — Theorem Prover and Programming LanguageHow the Lean Language Brings Math to Coding — Amazon ScienceHow AWS Uses Formal Methods — Amazon ScienceOpen Source MCP Servers for AWSDanilo Poccia on the AWS News Blog
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Agent-Native Serverless Development with Shridhar Pandey
Apr 100:47:20Tap to summarizeIn this episode, we sit down with Shridhar Pandey, Principal Product Manager on AWS Serverless Compute, to explore how the serverless team is pioneering agent-native development. Shridhar walks us through a remarkable March 2026 where the team shipped three major capabilities in just three weeks — a Kiro Power for Durable Functions, a Kiro Power for SAM, and a serverless agent plugin now available in Claude Code and Cursor. We trace the journey from 18 months of traditional developer experience improvements — local testing, remote debugging, LocalStack integration — to the realization that AI agents are fundamentally changing how developers build, deploy, and operate serverless applications. The serverless MCP server, now approaching half a million downloads, laid the foundation, and the new agent plugin builds on it with four specialized skills covering Lambda functions, operational best practices, infrastructure as code with SAM and CDK, and durable functions. Shridhar shares his thinking on agent personas — developer agents, operator agents, and platform owner agents — and how the team is applying an 'AX' (agent experience) lens to every feature they ship. We also take a candid detour into how AI has transformed his own work as a product leader: research that took weeks now takes hours, document cycles that spanned days now wrap up in a single sitting, and a fleet of agents handles daily digests and data analysis for the team. Open source runs through everything — the MCP server, the plugin, the public Lambda roadmap on GitHub — and Shridhar invites the community to shape what comes next.With Shridhar Pandey, Principal Product Manager, AWS Serverless ComputeAWS Serverless MCP ServerAgent Plugins for AWS — GitHubIntroducing Agent Plugins for AWS — Blog PostAWS SAM Kiro Power AnnouncementAWS Lambda Public Roadmap — GitHubServerless Land — Patterns and ResourcesKiro PowersThe Innovator's Dilemma — Clayton ChristensenCompeting Against Luck — Clayton Christensen
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The Hard Lessons of Cloud Migration: inDrive's Path from Monolith to Microservices
Mar 2501:13:58Tap to summarizeJoin us for a fascinating conversation with Alexander 'Sasha' Lisachenko (Software Architect) and Artem Gab (Senior Engineering Manager) from inDrive, one of the global leaders in mobility operating in 48 countries and processing over 8 million rides per day. Sasha and Artem take us through their four-year transformation journey from a monolithic bare-metal setup in a single data center to a fully cloud-native microservices architecture on AWS. They share the hard-earned lessons from their migration, including critical challenges with Redis cluster architecture, the discovery of single-threaded CPU bottlenecks, and how they solved hot key problems using Uber's H3 hexagon-based geospatial indexing. We dive deep into their migration from Redis to Valkey on ElastiCache, achieving 15-20% cost optimization and improved memory efficiency, and their innovative approach to auto-scaling ElastiCache clusters across multiple dimensions. Along the way, they reveal how TLS termination on master nodes created unexpected bottlenecks, how connection storms can cascade when Redis slows down, and why engine CPU utilization is the one metric you should never ignore. This is a story of resilience, technical problem-solving, and the reality of large-scale cloud transformations — complete with rollbacks, late-night incidents, and the eventual triumph of a fully elastic, geo-distributed platform serving riders and drivers across the globe.With Alexander Lisachenko, Software Architect, inDrive ; With Artem Gab, Senior Engineering Manager, Runtime Systems, inDriveRedis in Action — Josiah L. Carlson (Manning)AWS Well-Architected Framework — ElastiCache LensBrendan Gregg's Blog — Performance Analysis & ObservabilityUber H3 — Hexagonal Hierarchical Spatial IndexinDrive WebsiteAWS ElastiCache DocumentationValkey ProjectAWS Well-Architected Framework
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