『The AWS Developers Podcast』のカバーアート

The AWS Developers Podcast

The AWS Developers Podcast

著者： Amazon Web Services
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  • Agent harness: the 5 pieces your AI agent is missing

    Agent harness: the 5 pieces your AI agent is missing
    2026/10/28
    An agent harness is everything around the model that turns it into a working AI agent, and most agents are missing key pieces of it. In this episode we go through five of them: context management, sandboxing, fine-grained policies, fast decision models, and a harness you can run in production. Romain sits down with Morgan Willis, Senior Developer Advocate at AWS, and Arron Bailiss, Principal Engineer and Tech Lead for Strands Agents, to look at what it takes to build a state-of-the-art agentic system today, and how Strands Agents grew from an agent loop SDK into a full toolkit for it. The 5 pieces: 1. A harness with built-in tools, memory and a CLI, open source as Strands harness or managed as AgentCore harness 2. Context management that offloads tool results and compacts history, for 28% lower token cost on the same model 3. A sandbox that limits what files and network your agent can reach (Strands Box) 4. Policies that remember what the agent already did, so it can't issue a refund before checking eligibility (Dogwood) 5. Small decision models that pick between options in milliseconds instead of a full LLM call (Strands Decider) Plus: real-time voice agents with bidirectional streaming, and why Morgan turned a Big Mouth Billy Bass into one. Chapters: 00:00 Strands one year after going open source 02:45 Why model-driven agents get better with every model 13:50 What is an agent harness? Model plus harness 18:20 Piece 1: a harness with tools, memory and a CLI 25:50 Piece 2: context management and 28% lower token cost 29:45 Piece 3: sandboxing agents with Strands Box 31:00 Piece 4: Dogwood policies that track what the agent did 42:45 Piece 5: Strands Decider and small decision models 51:15 Bonus: voice agents with bidirectional streaming 56:45 What's next: agent safety and self-improvement
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    1 時間 7 分
  • In-flight experience: a cloud inside the plane

    In-flight experience: a cloud inside the plane
    2026/10/14
    In-flight entertainment now runs on a cloud. Not the one on the ground, a cloud inside the aircraft. A 300-watt onboard data center running Kubernetes, managed databases and observability, built to survive vibration, fire and electromagnetic interference. Then AI agents on top so airlines can talk to their fleet. Romain sits down with Frédéric Faria, Chief Technology and Engineering Officer, and Yann Nicolas, Chief Data and AI Officer, at Thales In-Flight Experience, to unpack FlytEDGE, the cloud-native in-flight entertainment platform. Key topics covered: • Why most aircraft still get content by USB drive, and how connectivity changes that • Designing an aviation-grade onboard data center: about 100 TB of storage in a 300 W budget • Vanilla Kubernetes, Helm and GitOps for updates and rollbacks in flight • An open onboard SDK with REST APIs for flight data (position, speed, origin, destination) • Multi-tenancy, data minimization and forensic watermarking for studio content • Global delivery with Amazon CloudFront and caching once per aircraft • A conversational ops agent built with Amazon Bedrock AgentCore, AgentCore Gateway and Strands Agents • Rolling out Kiro to hundreds of engineers, beyond software teams Chapters: 00:00 Intro: your in-flight entertainment, now in the cloud 00:51 Welcome: Thales In-Flight Experience, 2 million passengers a day 07:24 What changed: connectivity and the end of the USB drive 12:36 A data center on board: 300 W, 100 TB, Kubernetes 18:00 Vanilla Kubernetes, Helm and GitOps at 35,000 feet 27:07 Opening the platform: onboard SDK, aircraft APIs, partners 32:52 Multi-tenancy, privacy and global delivery with CloudFront 41:19 DRM and forensic watermarking 47:35 AI agents for airline ops with Bedrock AgentCore and Strands 1:00:42 Kiro across the engineering org, books and dev tools
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    1 時間 11 分
  • Is AWS Lambda still serverless?

    Is AWS Lambda still serverless?
    2026/09/30
    Is AWS Lambda still serverless? With Managed Instances, MicroVMs, and Durable Functions, Lambda can now run workloads it was never designed for. AWS Serverless Hero Yan Cui explains what changed, what you give up, and why the core serverless promise still holds. Romain sits down with Yan Cui, AWS Serverless Hero and independent consultant, to discuss how Lambda grew into general-purpose compute and where it fits for AI agents. Key topics covered: • Why pay-per-use is no longer the whole story, and why that's fine • Lambda vs Fargate vs ECS: packaging a container image is not running a container • Lambda Managed Instances, the 90-minute timeout, and Durable Functions for jobs that run up to a year • Cold starts: why they were never as big a problem as the conversation around them • Lambda MicroVMs as a second sandbox for AI agents and CI/CD runners • AgentCore Runtime vs Lambda MicroVMs for agentic workloads • Observability for event-driven architectures, and what Lambda still gets wrong • Yan's rule: every box on your architecture diagram needs a reason to exist Chapters: 00:00 Highlights 00:55 Meet Yan Cui, AWS Serverless Hero 05:23 Is Lambda still serverless? 14:36 Lambda vs Fargate vs ECS: where to draw the line 20:46 Lambda Managed Instances explained 24:10 Are cold starts finally dead? 29:45 Lambda MicroVMs, AI agents and S3 Files 37:51 AgentCore Runtime vs Lambda MicroVMs 45:28 What serverless still gets wrong, and observability 56:07 Yan's advice, book pick and AI security worries
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    1 時間 12 分
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