AI Engineer World's Fair 2026 — unofficial import demo

Building an AI demo is easy. Knowing whether it actually works — and keeping it working in production — is the hard part. Most teams ship agents on vibes: they try a few prompts, the output looks good, and they push to production with no real way to measure quality or catch regressions. This hands-o…

Your agent can reach your data but still can't use it reliably: vector search and Text2SQL each hand it a slice, but not the view to know what's truly relevant and how to connect the right info. Without that, answers come back confident but wrong, and agent decisions cannot be trusted. The problem i…

Codex is changing how technical teams ship across the software development lifecycle, from feature implementation to code review and automation. But the real unlock comes when these practices move beyond a single workflow and become shared systems a team can trust. In this hands-on session, you'll u…

Industry research shows AI productivity gains have plateaued at 10–15% — because today's tools only optimize the 20% of a developer's day spent writing code. The real bottlenecks are left and right of code: planning, orchestration, review, and operations. We'll also explore the value of AI-powered c…

In this hands-on workshop you'll build a working autonomous agent from the harness up, in a notebook, then see it live in a full working web application and leave with one that can write and run its own automations. You'll implement every surface area yourself: a set of predefined tools, persistent…

Every production agent today is renting its intelligence. You're paying per token, sending your customer's data to someone else's servers, and hoping the provider doesn't rate-limit you during your launch. For most teams, that's fine. But for a growing number of teams in regulated industries, with h…

Agentic coding workloads demand long contexts, multi-turn conversations, and throughput at a scale that most inference engines weren't built for. TokenSpeed is a new open-source engine purpose-built for this regime, built collaboratively by NVIDIA DevTech, AMD Triton, Qwen Inference, Together AI, an…

Your AI is in production, but is it actually good? In this hands-on workshop, you'll learn how to uncover patterns in your production traces using Braintrust Topics, build custom scorers to target real issues, and systematically improve your agent. By the end, you'll have a repeatable eval workflow…

In this hands-on lab, you will build a production-ready AI application using Microsoft Foundry, with no fine-tuning or deep machine learning expertise required. You will discover and select models, provision a Foundry project, and connect to a hosted model using the OpenAI SDK. You’ll implement a co…

Building an AI demo is easy. Knowing whether it actually works — and keeping it working in production — is the hard part. Most teams ship agents on vibes: they try a few prompts, the output looks good, and they push to production with no real way to measure quality or catch regressions. This hands-o…

Running sandboxes at scale can get painful. How do you manage a thousand concurrent sandboxes? We'll cover burst traffic, fast sandbox creation under load, resource exhaustion, shared state with volumes, and per-user data isolation. Then you'll trigger each failure, implement fixes, and see the cost…

Frontier labs are releasing new models constantly, and it is hard to know when “better” is better enough to justify touching a working system. On top of that, “just swap the model” often turns into real work because providers expose different APIs and different expectations around tools and structur…

Modern agentic systems often fail because the right training data simply does not exist. Search agents are a perfect example: if you want a model to browse the web effectively, you need high-quality multi-step trajectories that teach it how to search, refine queries, inspect sources, and recover fro…

Have you heard "Buy a GPU," "Opensource AI Must Win," or "Local AI FTW" before? This workshop will be a practical window into that confusing world and a practical map for understanding what different Local AI hardware is actually capable of and which models make sense on each class of machine. Wheth…

AI coding agents can now generate code at unprecedented speed. But faster code generation creates a new engineering problem: how do we know when agent-written code is actually safe, maintainable, and ready to merge? In this hands-on workshop, attendees will build an agentic coding workflow with enfo…

To run state-of-the-art inference yourself, you must master the inference engine: vLLM, SGLang, TRT-LLM, or your own jawn. The inference engine manages the lifecycle of an inference request, from input to output. In this workshop, we'll examine the architecture of modern high performance inference e…

One agent. Fully deployed to production before the workshop ends. We'll take you from a blank file to a running production agent using Amazon Bedrock AgentCore and Strands Agents, covering the full lifecycle: ideation, coding the agent loop, deploying to serverless infrastructure, wiring up observab…

What happens when you let AI agents do the building? In this hands-on lab, you'll go from an empty terminal to a deployed app on Azure — with GitHub Copilot CLI and coding agents handling the scaffolding, coding, debugging, and deployment. You'll use the new Azure skills to provision resources and w…

“Our evals pass and our velocity is up, so it works.” It’s the most reassuring sentence in AI engineering and also the most dangerous. Teams are shipping more code than ever while incidents per PR and change-failure rates climb, and the instruments meant to catch this are quietly broken. This talk t…

"You've invested in the best models, coding agents, and AI tooling. Now comes the hard part: unlocking autonomous development without creating security headaches, governance gaps, or endless approval loops. In this 90-minute hands-on workshop, you'll learn how to run coding agents in isolated enviro…

Most engineers using LLMs can call an API. Far fewer can explain why their model is slow, why it's running out of memory, or how the inference engines powering every major LLM API actually work. This workshop walks through the full inference stack — from how a transformer generates a single token to…

There is nothing quite as demoralizing as finishing a feature and realizing you built the wrong thing. The code is clean. The tests pass. The ticket is closed. And none of it matters. This is happening more often, not less. AI makes it faster and cheaper to implement, which means teams can now waste…

Running one agent eval is easy. Running hundreds — with controlled timeouts, replicated configs, and automated collection across distributed VMs — requires infrastructure that most teams end up building from scratch. In this workshop, we shortcut that process and build a rigorous evaluation pipeline…

From the guy who said "Buy a GPU," "Opensource AI Must Win," and "Local AI FTW": this session shows what you build around the models running locally so agents can actually be effective and efficient when using local models. A local chatbot gives you private text generation. A useful agent needs a sy…

In this workshop we'll explore the importance of context engines in modern engineering workflows, and we'll look at why traditional RAG techniques are no longer enough to deliver the context agents need. We'll build a structured query engine that fills the gaps left by RAG, translating natural langu…

Your AI agent is only as good as the data it can access — and static training data isn't enough anymore. In this hands-on workshop, you'll learn how to connect AI agents to the live web using Bright Data's MCP (Model Context Protocol) server and scraping APIs, turning any LLM into a real-time web-aw…

Most engineers using LLMs can call an API. Far fewer can explain why their model is slow, why it's running out of memory, or how the inference engines powering every major LLM API actually work. This workshop walks through the full inference stack — from how a transformer generates a single token to…

There is nothing quite as demoralizing as finishing a feature and realizing you built the wrong thing. The code is clean. The tests pass. The ticket is closed. And none of it matters. This is happening more often, not less. AI makes it faster and cheaper to implement, which means teams can now waste…

You get a Kubernetes cluster with an Internal Developer Platform already running: ArgoCD for GitOps, Kyverno for admission control, Falco for runtime detection, Prometheus for observability. Everything is instrumented. Everything is enforced. You also get an AI agent with cluster access. Your job is…

As AI agents take on increasingly complex development tasks, the critical challenge has shifted from generation to verification. A growing body of evidence suggests that as models grow more capable, failures become more frequent and more convincing, making cognitive surrender among human reviewers a…

Reducto powered the infrastructure behind Jmail, a fully searchable email interface with over 3.5 million scanned government pages built days after the Epstein files release. The site went viral overnight, racking up millions of views across news coverage and social media. In this workshop we'll bre…

Personal knowledge bases are messy, but engineering agents need memory: decisions, docs, TODOs, old PRs, architecture notes, incident notes. This talk shows how I made an Obsidian vault usable by an agent using local-first retrieval and small-model inference. The point is not “chat with notes”; it i…

Building an AI demo is easy. Knowing whether it actually works — and keeping it working in production — is the hard part. Most teams ship agents on vibes: they try a few prompts, the output looks good, and they push to production with no real way to measure quality or catch regressions. This hands-o…

Modern AI agents typically understand either code or databases. Code-focused agents reason over files, dependencies, and syntax, while database agents see tables, columns, and query results. This works for software development and basic analytics—but it breaks down for data engineering. In real data…

The common guidance for designing complex CLI tooling that agents can use is to add a 'non-interactive' mode, where a normally interactive & flow-based command can be executed in a single pass by feeding it a bunch of flags. This is necessary for deterministic automation, but agents aren't scripts;…

Every long agent session eventually breaks: the assistant that swore it would "never push to main" does exactly that forty turns later. The model didn't get dumber — its context did. This workshop is about engineering the context window so that stops happening, shown with Towards AI's open-source AI…

If you build RAG, you reached for vector search first. This lab is about everything that happens after you realize embeddings alone don't cut it in production. You'll write real queries — semantic, lexical, and hybrid — feel exactly where each one fails, and walk out with a production-grade retrieva…

Human-agent collaboration is changing, becoming more visual. Models can perceive, point, and verify, but most agents still rely on us typing a paragraph to explain what we're looking at. Meet perception agents: computer use agents that see screens how you see screens. They understand, reason, and ve…

Heard about autoresearch, or tried it a few times in playground settings? This hands-on tutorial teaches you how to use autoresearch on one of the most serious challenges in ML this year: OpenAI's Parameter Golf. The challenge: train the best language model that fits in just 16MB. We entered our aut…

Modern agents fail in ways traditional monitoring can’t catch. In this hands-on lab, learn how Microsoft Foundry Observability helps you move from prototype → production with context-specific evaluation suites (auto-generated evaluators + test datasets) wired into developer workflows via skills/MCP…

Even the world's best computer-use agents cannot repeat their successes at the moment. Agents that write code — emitting structured selector-based actions instead of clicking pixels — break through that ceiling. We'll share two years of experience from Simular's production agent platform, the archit…

Most agent skills are a system prompt and a prayer. They produce safe, median output because that's what LLMs default to. After building 24 design skills across 9 AI platforms, I found the patterns that break through that ceiling, and they're rarely documented or discussed. Make your agents argue: s…

Agent Capability is now highly dependent on the markdown files read at runtime -- skills.This workshop treats skills as a first-class optimization surface. We borrow the concept of autoresearch (from Karpathy) and apply it to the skills your agents already read. You'll see how we at Browserbase did…

Better Auth has grown to 27k GitHub stars and over 1.5M weekly downloads, becoming a popular choice for developers who want to own their authentication stack. We recently introduced Agent Auth, a protocol designed to support autonomous and delegated agents operating services for an organization or a…

Most engineers are bolting AI onto a workflow that was designed for a pre-AI world. The result is a faster version of the same grind. This talk is about the other path: rebuilding the daily practice of software engineering from the ground up, around what agents are actually good at. Two senior pract…

This workshop helps agentic coding practitioners stop treating agents like pretend coworkers and start designing reliable, compounding loops. Using Pi as the concrete demo surface, Joel Hooks will show how loop state, handoffs, review, memory, and operator control become visible, while keeping the i…

Getting an agent into production takes more than a good prompt: it needs somewhere to run code, credentials it can't leak, sessions that survive interruption, and infrastructure that scales. This talk traces how Anthropic's agentic surfaces evolved from the raw API to Claude Managed Agents, and what…