AI Coding Agents: Efficacy and Usage - Coding agents are substantively useful, though opinions vary on their best practices and applications [1] - The number one mistake people make with coding agents is using them the same way they used AI coding tools six months ago [1] - The evolution of frontier model capabilities drives distinct eras in generative AI, influencing application architecture [1] Design Decisions for Agentic LLMs - Agents should make edits to files without constant human approval [2] - The necessity of a thick client (e.g., forked VS Code) for manipulating LLMs is questionable [2] - The industry is moving beyond the "choose your own model" phase due to deeper coupling in agentic chains [2] - Fixed pricing models for agents introduce perverse incentives to use dumber models [2] - The Unix philosophy of composable tools will be more powerful than vertical integration [2] Best Practices and User Patterns - Power users write very long prompts to program LLMs effectively [4] - Directing agents to relevant context and feedback mechanisms is crucial [5] - Constructing front-end feedback loops (e.g., using Playwright and Storybook) accelerates development [6] - Agents can be used to better understand code, serving as an onboarding tool and enhancing code reviews [9][11] - Sub-agents are useful for longer, more complex tasks by preserving the context window [12][13]

Agent & MCP Server Development - Cloudflare and Work OS are collaborating to promote the idea that agents acting on behalf of users need the same credentials and authorization as user-facing projects [1] - The industry is moving towards more fine-grained authorization for AI agents, potentially authorizing per-line changes, per-tool changes, or even network connections [20] - Cloudflare offers a free tier for Durable Objects, which can be used for persistent storage in agents [3] Cloudflare's Offerings - Cloudflare provides compute cloud workers, AI model hosting, vectorized inference, vector database, SQL database, durable objects, video streaming, and image optimization [2] - Cloudflare workers have bindings that allow interaction with other Cloudflare products and other companies' products [3] - Cloudflare's agents framework includes an OAuth framework for setting up authorization, enabling easy identification of the worker or agent acting on behalf of a user [5] MCP Server Demo & Use Case - A basic MCP server was built using Cloudflare and Work OS, which is available for users to check out and run [6] - The demo showcases ordering a shirt via an agent, demonstrating how agents can act on behalf of users with proper authorization [9][10][11] - The demo uses Cloudflare's key-value storage to save order data, accessible through the interface [12] - Durable Objects can store data directly on the context associated with a worker object, unique for each user [14][16] Security & Authorization - The industry emphasizes the importance of audit trails with OAuth tools to track agent interactions, including reasons for interaction, the user on whose behalf it acted, and the outcome [21] - The industry needs to consider users as deputies who have access to tools and can potentially misuse them [21]

AI agents promise breakthroughs but often deliver operational chaos. Building reliable, deployable systems with unpredictable LLMs feels like wrestling fog – testing outputs alone is insufficient when the underlying workflow is opaque and flaky. How do we move beyond fragile prototypes? This talk, from the creator of Docker, argues the solution lies outside the model: engineering reproducible execution workflows built on rigorous architectural discipline. Learn how containerization, applied not just to depl ...

Product leaders see AI possibilities. Operations teams see implementation chaos. That disconnect can kill promising AI features before they ever reach users. In this session, Chris Hernandez (Chime) and Jeremy Silva (Freeplay) share an integrated framework that bridges product strategy and operational reality. You'll learn how they transformed fragmented AI workflows into a unified approach—from prototyping and prompt testing to human review loops and model benchmarking. We’ll explore how to build evaluatio ...

Key Optimization Goal - Aims to achieve Time To First Byte (TTFB) below 150 milliseconds for voice models [1] Technology and Tools - Leverages open-source TTS models like Orpheus, which have an LLM backbone [1] - Employs tools and optimizations such as TensorRT-LLM and FP8 quantization [1] Production Challenges - Client code, network infrastructure, and other outside-the-GPU factors can introduce latency [1] - Common pitfalls exist when integrating TTS models into production systems [1] Scalability and Customization - Focuses on scaling TTS models in production [1] - Extends the system to serve customized models with voice cloning and fine-tuning [1]

Agent deployments can be dicey, especially at first. This session goes over all the things that cause headache with deployments from serverless issues to networking issues - and how we fix them. About Rick Blalock Seasoned founder with exit. Developer at night and during the day if I can fit it in meetings... Scaled a mobile developer platform from hundreds to 800,000 developers. Successfully started and sold a fisheries platform & app to the world's largest fishing app with 15m+ users, and then led that co ...

An introduction to the evals track About Manu Goyal Manu Goyal is the founding engineer at Braintrust. Previously, he developed autonomous systems at Nuro. He has an 8 year old Pomeranian named Hendrix. Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our newsletter here: https://www.ai.engineer/newsletter ...

[Music] Welcome everyone. Thank you so much for coming. My name is Forest.This is Matt. Uh and we're going to be talking to you today about augment agent and specifically legacy code. how we get the most out of gnarly legacy code bases using an AI agent.So I do not work for Augment Code. Um I am a friend and partner of Augment Code. So I helped to put this talk together.Matt is from Augment Code. So he's going to be your best person to come to with your most detailed technical questions after the session. M ...

Generative AI and Agent Technology - Amazon Web Services (AWS) specializes in generative AI, evolving from machine learning [1] - The presentation focuses on deploying generative AI agents to cloud scale, targeting both developers and leaders [1] - The core components of an agent include a model for natural language understanding, a prompt defining the agent's role, an agentic loop for processing input and using tools, history for maintaining context, and tools for external interaction [1][2] - AWS Bedrock offers a suite of capabilities for building generative AI components, including models from Anthropic, Meta, and Mistral [2] - Amazon Bedrock Agents is a fully managed service for deploying agents without infrastructure management [2] Practical Implementation and Tools - The demonstration uses a simple Python agent with a dice rolling tool, initially running locally on a laptop with the Llama 3 8 billion parameter model [1] - The agent is configured with instructions (similar to a prompt) and action groups, which connect to tools [2] - Lambda functions are used to host the tools, enabling them to perform various actions, including interacting with other AWS services [2] - The AWS console provides a user interface for creating and configuring agents, including defining parameters and descriptions for tools [3][4][5][6][7][8][9][10][11][12][13][14][15] - Amazon Q developer is integrated into the console's code editor, offering code suggestions [17][18][19][20][21] Deployment and Scalability - The presentation emphasizes deploying agents to a production-ready, cloud-scale environment [1] - Infrastructure as code frameworks like Terraform, Palumi, and CloudFormation can be used for deployment [3] - AWS offers free courses on deeplearning.ai with AWS environments for experimenting with Amazon Bedrock Agents [25]

As organizations seek to harness their proprietary data while maintaining security and compliance, Amazon Bedrock provides a comprehensive framework for building tailored AI applications. Using Amazon Bedrock Knowledge Bases and Amazon Bedrock Data Automation, organizations can create AI solutions that truly understand their unique business context, terminology, and requirements. Combined with Amazon Bedrock Guardrails, these capabilities enhance the accuracy and relevance of AI-generated responses, while e ...