The AWS Developers Podcast

Amazon Web Services

Stay updated on the latest AWS news and insights for developers, wherever you are, whenever you want.

  1. Cutting Through the AI Developer Hype

    6月10日

    Cutting Through the AI Developer Hype

    An honest, no-filter conversation about where developers actually stand with AI today. Warren Parad — CTO at Authress, AWS Community Builder, and host of Adventures in DevOps — brings a contrarian 'LLM realist' perspective grounded in daily use, while Romain nuances with enterprise customer observations and the data behind the hype. Together they explore why 93% of devs feel productive but only 4% of enterprises see results — and what separates those who benefit from those who don't. Key takeaways: • AI is a multiplier, not a magic wand — The DORA 2025 report confirms AI amplifies your existing processes. If those processes are broken, AI makes them worse faster. • Spec-driven development beats instant responses — Long-form spec-based workflows let you disengage and return, avoiding the 'TikTok-ification' of software engineering where you're always context-switching. • Sub-agent opacity is a real problem — When agents delegate to sub-agents, you lose visibility into why decisions were made. Custom agents with explicit permissions and tool access help contain the blast radius. • Greenfield work is where LLMs struggle most — LLMs excel at refactoring and targeted feature changes where engineers already know the implementation. Open-ended new projects lead to scope creep and unfinished work. • Critical thinking erosion is measurable — Microsoft/Carnegie Mellon research shows knowledge workers self-report reduced cognitive effort when using AI. The long-term implications for engineering judgment are concerning. • Governance first, tools second — Enterprises that succeed with AI spend the first month on governance, AI registries, and codifying best practices before enabling tools across teams. • Software development was never the bottleneck — Unless AI solves handoffs, knowledge management, and organizational alignment, faster coding alone won't compress your roadmap.

    1時間20分
  2. Why Your Agent Evaluations Will Fail You (and How to Fix Them Before Production)

    6月3日

    Why Your Agent Evaluations Will Fail You (and How to Fix Them Before Production)

    Anthropic deprecated Sonnet 3.5. Some of Xelix's pipelines migrated smoothly. Others broke — and customers noticed within hours. What separated the two? Evaluation. Paul Solomon and James Price Farr have spent 5+ years building AI systems that process millions of invoices for enterprise customers. In this episode, they share the evaluation-first framework that now saves them every time a model changes, an orchestration layer fails, or an agent picks the wrong tool. Key takeaways: • Evaluation-first, not evaluation-after — Retrofitting evaluation on an agent already in production is painful. Build your eval pipeline before you build the agent. • Monitor tool calls, not just outputs — If the agent isn't selecting the right tools, nothing downstream will be correct. Tool-call monitoring is your leading indicator. • 3 tiers of automation — Not everything needs an agent. Rules-based → single LLM call → agentic system. Pick the simplest tier that solves the problem. • Extended thinking tames token explosion — After migrating to newer, more verbose models, enabling extended thinking (with a budget) moved reasoning out of expensive output tokens and brought costs back under control. • Human-in-the-loop by default — Start with human review on every output, then earn trust toward touchless automation as customers gain confidence. • Pragmatism wins — Use whatever technology works best for the problem. Not every feature needs an LLM. Recorded live at AWS Summit London.

    44分
  3. 5 Quality Gates That Let You Ship 250% Faster with AI Coding Agents

    5月27日

    5 Quality Gates That Let You Ship 250% Faster with AI Coding Agents

    How 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.

    1時間2分
  4. Dark Factories: Why Your AI Coding Setup Is Already Outdated

    5月20日

    Dark Factories: Why Your AI Coding Setup Is Already Outdated

    You'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.

    49分
  5. LLM-as-a-Judge, Quotation Fidelity, and A/B Testing Models: AI Publishing at Scale

    5月13日

    LLM-as-a-Judge, Quotation Fidelity, and A/B Testing Models: AI Publishing at Scale

    What 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.

    50分
  6. AI Agents, Friction, and the Future of Developer Experience

    5月5日

    AI Agents, Friction, and the Future of Developer Experience

    AI 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.

    51分
  7. The Evolution of Microservices: Agents, Monoliths, and the Patterns That Never Die

    4月29日

    The Evolution of Microservices: Agents, Monoliths, and the Patterns That Never Die

    Recorded 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.

    47分
  8. How Can AI Agents Cut Support Resolution Time by 95%?

    4月22日

    How Can AI Agents Cut Support Resolution Time by 95%?

    CyberArk'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.

    52分
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番組について

Stay updated on the latest AWS news and insights for developers, wherever you are, whenever you want.

情報

  • クリエイター
    Amazon Web Services
  • 配信期間
    21年 - 26年
  • エピソード
    212
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © All rights reserved
  • 番組のWebサイト
    The AWS Developers Podcast

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