Amazon Bedrock

AGI: The Path Forward – Jason Warner & Eiso Kant, Poolside

Dec 27, 2025 · 15:56

Jason Warner and Eiso Kant, co-founders of Poolside, present their vision and roadmap towards AGI-level capabilities for knowledge work, demonstrating their second-generation model Malibu Agent converting ADA code to Rust live on stage. They argue that next-token prediction paired with reinforcement learning is the key breakthrough, a contrarian bet they made two and a half years ago. The episode centers on their work in high-consequence code environments for defense and government, where agents must operate with tight permissions. They announce a large compute cluster of over 40,000 GB300s coming online and a public API release early next year via AWS Bedrock. Warner recounts meeting Kant through a failed GitHub acquisition, and Kant invites the audience to build with their models, emphasizing that future agents will handle tasks over days as intelligence scales.

Building Agents at Cloud Scale — Antje Barth, AWS

Aug 2, 2025 · 19:00

AWS Principal Developer Advocate Antje Barth demonstrates how to build and scale AI agents using cloud-native patterns, arguing that specialized agents will reinvent customer experiences. She showcases Alexa Plus, which orchestrates hundreds of expert systems across 600M+ devices and tens of thousands of services, and the Amazon Q Developer CLI agent, shipped in just three weeks. Barth introduces Strands Agents, an open-source Python SDK for building production-ready agents that supports multiple model providers (Claude, Llama, OpenAI) and over 20 prebuilt tools including memory, RAG, and multi-agent workflows. She demonstrates integrating MCP servers via Lambda with DynamoDB for session storage, and previews upcoming A2A protocol support and a future of personal agents connecting to agent stores.

Serving Voice AI at Scale — Arjun Desai (Cartesia) & Rohit Talluri (AWS)

Jun 27, 2025 · 17:05

Arjun Desai of Cartesia AI and AWS's Rohit Talluri discuss scaling voice AI for enterprise, arguing that latency and controllability are critical, with Cartesia's state-space model Sonic 2 achieving 40ms model latency for real-time applications. Desai explains that traditional transformer models scale quadratically, while Cartesia's SSMs maintain O(1) generation, enabling 2.5x faster inference than their earlier models. He emphasizes that edge deployment is 5x faster than cloud round-trips, making local models essential for interactive use cases. On quality, Desai notes that voice AI must handle interruptions, accents, and background noise, and that Cartesia's voice marketplace amplifies human voice actors rather than replacing them. Looking to 2030, he predicts voice AI will become the default interface across healthcare, customer support, and gaming, with interactive models extending beyond audio to full world models.

Ship it! Building Production Ready Agents — Mike Chambers, AWS

Jun 27, 2025 · 19:37

Mike Chambers, a developer advocate at AWS, demonstrates how to take a simple local agent—built with a Llama 3.1 8B model and a dice-rolling tool—and ship it to production at cloud scale using Amazon Bedrock Agents. He breaks down the essential components of an agent: model, prompt, loop, history, and tools. Then he live-deploys the agent with Bedrock Agents, configuring instructions and an action group wired to an AWS Lambda function that handles the dice roll. The fully managed service automates scaling, infrastructure, and the agentic loop. Chambers also highlights free courses on DeepLearning.AI and invites attendees to discuss MCP servers and the new open-source SDK for model-first agents.

Data is Your Differentiator: Building Secure and Tailored AI Systems — Mani Khanuja, AWS

Jun 27, 2025 · 20:10

Mani Khanuja from AWS explains how data differentiates generative AI applications, emphasizing that foundation models require careful data handling specific to each use case. She demonstrates Amazon Bedrock's capabilities: Bedrock Data Automation for custom pipelines with a single API, Knowledge Bases for RAG with out-of-box chunking and hybrid search, and Guardrails for responsible AI with PII filtering. Using a travel agent example, she details data needs—customer profiles, travel policies—and introduces the 'coconuts' framework: chunking, optimization via semantic cache and re-ranking, observability, evaluation (context relevance), test suites, and iterative updates. She argues that organizations must crack their coconuts—rigorously prepare data and evaluate applications—before scaling into production securely.

Building Agents with Amazon Nova Act and MCP - Du'An Lightfoot, Amazon (Full Workshop)

Jun 21, 2025 · 1:26:20

Du'An Lightfoot and Benjiro Byami present a workshop on building AI agents with Amazon Nova Act, Model Context Protocol (MCP), and Strands Agents, demonstrating autonomous web navigation and multi-step task execution. Lightfoot explains agentic AI as plan, action, and reasoning, with components like LLM, knowledge base, guardrails, and tools. Nova Act, a research preview, enables browser automation: one demo searches Amazon for a coffee maker and retrieves the product title using natural language commands instead of explicit HTML tags. Byami builds an MCP server for Nova Act, allowing clients like Claude Desktop to control the browser via spoken instructions, then integrates it with Strands Agents, an open-source framework that combines a prompt, LLM, and tools in minimal code. The workshop shows parallel execution (e.g., comparing three monitors) and multi-agent collaboration for tasks like generating a PowerPoint presentation for cloud migration. Limitations include inability to bypass CAPTCHAs and occasional loops in complex websites.

From Software Developer to AI Engineer: Antje Barth

Jul 24, 2024 · 19:48

Antje Barth, a Principal Developer Advocate for generative AI at AWS, outlines five practical steps from software developer to AI engineer: understanding foundation models, getting hands-on with AI developer tools like Amazon Q Developer, prototyping with models via Amazon Bedrock, integrating agents, and staying up to date with community events. She demonstrates Amazon Q Developer's ability to generate, explain, and transform code, reducing unvaluable tasks from 70% to focus on creative work. Barth introduces Amazon Bedrock's unified Converse API for standardized model invocation across providers like Anthropic's Claude 3.5 Sonnet, and shows how agents can be built to control a Minecraft bot through reasoning and tool use. The episode concludes with the announcement of the AI Engineering Hub at the AWS loft in San Francisco for community workshops and events.