Nico Albanese

Give Your Agent a Computer — Nico Albanese, Vercel

May 12, 2026 · 1:08:53

Nico Albanese demonstrates building an agent with Vercel's AI SDK v6, centered on the insight that giving an agent a file system transforms its behavior—it follows through on long tasks, stays on track, and builds on prior work. He walks through creating a tool loop agent from scratch, adding provider-executed web search with typed UI components, and integrating Vercel's persistent named sandboxes that snapshot state after inactivity. The agent gains a bash tool for file system access, a memories.md file for persistent memory injected into instructions each turn, and instructions to generate Python scripts for repeatable tasks so it accumulates tools across sessions. Albanese explains how these patterns scale: an internal agent called D0 reduced customer support tickets by 90% with a 95% satisfaction rate, and his personal coding agent ran for 104 minutes in one turn, using 316 tool calls and only 32% of GPT-5.4's context window with zero compaction. The session concludes with a preview of a larger sub-agent system that uses durable workflow steps and background sub-agents to keep the main thread under 7,000 tokens.

Vercel AI SDK Masterclass: From Fundamentals to Deep Research

Apr 20, 2025 · 59:52

Nico Albanese from Vercel delivers a workshop on building agents with the AI SDK, demonstrating how to use generateText, tools, and structured outputs to create a deep research clone in Node.js. He shows that switching between models like GPT-4o mini, Perplexity Sonar Pro, and Gemini Flash 1.5 requires only changing one line of code. Tools with maxSteps enable autonomous multi-step agents, as when the model fetches weather data for San Francisco and New York and sums the temperatures across three steps. The deep research workflow generates subqueries (e.g., 'requirements to become a D1 shotput athlete'), searches via Exa with live crawl, evaluates relevance with an evaluate tool that discards irrelevant results, and recursively deepens research using depth and breadth parameters. Finally, o3 mini synthesizes the accumulated research into a structured Markdown report, all in 218 lines of code.