AI Engineer

AI Engineer

As AI coding models like Codex Spark reach 1,200 tokens per second—20x faster than current standards—developers must abandon bad habits formed during the era of slow inference. This talk outlines a practical playbook for "slow development": orchestrating fast models for execution while using slower, smarter models for planning, and treating AI as a real-time pair programmer requiring constant verification and strict context management.

AI Engineer

Guillaume Vernade from Google DeepMind demonstrates how to build multimodal content pipelines using the new GenMedia suite (Nano Banana 2, Veo 3.1, and Lyria) via the Gemini Developer API, showcasing a live workshop that transforms text into illustrated books with AI-generated images, video, and music.

AI Engineer

Anthropic engineers Ash Prabakar and Andrew Wilson explain how to build AI agents that run for hours or days by combining model improvements with strategic 'harness' scaffolding that solves context limitations, planning failures, and unreliable self-evaluation through persistent state management, verification loops, and deterministic orchestration patterns.

AI Engineer

Marlene Mhangami presents data showing GitHub code creation accelerating to 14 billion projected commits in 2026, driven by AI agents. She argues that true productivity gains require clean codebases and advocates for behavior-driven test development using Playwright with AI agents, where developers focus on refactoring while AI handles test generation and initial code implementation.

AI Engineer

Laurie Voss presents a practical framework for evaluating AI agents, emphasizing the shift from manual 'vibe checks' to automated test suites that combine code evals, LLM judges, and human validation to catch cascading failures in production systems.

AI Engineer

Microsoft's Amy Boyd and Nitya Narasimhan present the 'Mind the Gap' framework for AI agent observability, emphasizing continuous evaluation, OpenTelemetry tracing, and integrated safety guardrails to bridge the divide between development requirements and production reality.

AI Engineer

Jonas Templestein and Misha from Iterate demonstrate a prototype event-sourced architecture for building distributed AI agent harnesses where all state changes are captured as immutable events in HTTP-accessible streams, enabling debuggability and composability across different languages and environments.

AI Engineer

Nico Albanese demonstrates building AI agents with Vercel's AI SDK 6, introducing the new tool loop agent pattern and three essential building blocks for 2026: agent runtimes, sophisticated tool ecosystems, and sandboxed computer environments for state persistence and code execution.

AI Engineer

Fryderyk Wiatrowski presents Victor, an AI employee that lives natively in Slack to automate complex cross-functional tasks by leveraging shared company context and 3,000+ tool integrations, evolving from early browser-based agents to solve the unique memory and permission challenges of multi-user enterprise environments.

AI Engineer

Mehedi Hassan explains why simply adding AI features with a single prompt ('one-shotting') fails in production, advocating instead for tight feedback loops through custom tracing infrastructure and rapid iteration frameworks to refine LLM behavior for specific use cases.

AI Engineer

Leonie Monigatti from Elastic argues that context engineering is fundamentally 80% agentic search, evolving from rigid RAG pipelines to dynamic agent-driven retrieval that must navigate diverse context sources through carefully curated, specialized search tools.

AI Engineer

Samuel Colvin demonstrates optimizing AI agent prompts in production using Jepper, a genetic algorithm library that breeds high-performing prompt variations, combined with Logfire's managed variables for structured configuration and deterministic evaluation against golden datasets.