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- 3 days ago1h 2m
CI/CD for coding agents: when validation becomes the bottleneck
Coding agents write code faster than ever, and the bottleneck in shipping software has moved to validating it. A 15-minute build gives an agent 4 feedback cycles an hour. A 5-minute build gives it 12. Romain sits down with Dan Manges, co-founder and CEO of RWX and former CTO of Braintree and Root, to talk about what CI/CD looks like when agents are the main users, and how the rest of the software development lifecycle has to change with it. Key topics covered: - Why traditional CI/CD pipelines slow down coding agents - Graph-based execution and content-based caching, compared to Docker's lineage-based caching - A CLI-first workflow: run CI against local changes without committing or pushing, and get a green PR check right away if the agent already ran it - Sandboxes for the agent's inner loop, MCP, semantic outputs, and retrying only the failed tests - Building RWX on AWS: a distributed container runtime with no image build and push, a hot pool of EC2 M8a instances, single-tenant VMs, and self-hosted runners in customer VPCs - Rethinking the SDLC from first principles: give agents the context to get it right, throw code away and fix AGENTS.md instead of iterating - Software factories for internal tools versus human review for mission-critical code - Turning large changes into reviewable work by building the whole change first, then extracting shippable pieces - Guardrails for agent-driven changes: CloudWatch, gradual rollouts and feature flags - Results: pipelines about 2x faster on average, and about 35% lower cost - Is infrastructure as code still necessary? Chapters: 00:00 Rethinking engineering for AI agents 00:52 Meet Dan Manges and his engineering journey 02:59 Why traditional CI/CD becomes a bottleneck 05:34 Faster feedback loops for coding agents 08:02 Graph execution and content-based caching 13:19 A CLI-first, agent-native CI workflow 15:33 Unifying agent development and CI environments 17:45 Building RWX on AWS 21:33 Rethinking the SDLC for agents 24:33 When humans should review agent code 27:07 Giving agents the context to get it right 29:29 Sharing agent skills and team practices 35:50 Building software factories with agents 40:32 Turning large changes into reviewable work 46:20 Guardrails for safe agent-driven changes 48:06 Measuring CI speed, cost, and agent benefits 51:10 Signals your CI pipeline needs rethinking 54:02 Is infrastructure as code still necessary? 56:38 Closing thoughts and recommendations
- 1 weeks ago1h 7m
Agent harness: the 5 pieces your AI agent is missing
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
- 3 weeks ago1h 11m
In-flight experience: a cloud inside the plane
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
- Sep 30, 20261h 12m
Is AWS Lambda still serverless?
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
- Sep 23, 202649 min
Jev: is it just a smarter if-statement?
Is Jev just a smarter if-statement? That's the question everyone's asking about TypeSafe AI's new "System One" model. In this episode, we dig into what Jev actually is: a model that makes fast, typed decisions your code can use directly — and why it points to a bigger shift: stop defaulting to one giant LLM for everything. Romain sits down with Mike Chambers, Senior Developer Advocate for Generative AI at AWS, to discuss Jev, decision models, and how they complement the LLMs we already know. Key topics covered: • What Jev is, and why it is not just another LLM • The three primitives: Noul, Choice, and Score • Putting gated decision points inside an agent with Strands • Fast model routing, and why cost and speed are the real win • Why the right tool for the right job is back Chapters: 00:53 Welcome, and what is exciting in the Bay Area 04:13 What is Jev? 05:24 Why it is not just another model 09:53 A decision model: moving logic back into code 17:31 System One, and Thinking Fast and Slow 19:02 The three primitives: Noul, Choice, Score 22:29 Accessing Jev: API, OpenRouter, SDKs 26:22 Use cases: gated decision points in agents 35:19 Model routing, cost and speed 40:17 Can Jev drive a DeepRacer, and other models to consider
- Sep 16, 202651 min
Building Software Will Never Be The Same
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 level
- Sep 2, 20261h 2m
Kiro, Strands Agents & MCP: 10 Updates You Missed
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.
- Aug 26, 202650 min
Harness engineering: are you controlling your AI agents?
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 model
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