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New · guided experience
A curated walkthrough of the library — ordered lessons, a 15-question quiz between each, and 3 next-steps so you stay in flow. Earn XP, badges, and a streak as you go.
Library · 6440 lessons · Career+ view
Search below, or pick a shelf. Kids and teens get age-tier shelves; College+ and Career+ organize the same library around life stage and situation.
Lessons handpicked for the Career+ shelf.
The agent market matured fast. Here's the field map — frontier labs, frameworks, browsers, local stacks, benchmarks — so you can pick the right tool without shopping by hype.
Underneath every agent framework is the same primitive — the model returns a structured tool call, you execute it, you feed the result back. Master this loop and every framework looks familiar.
Model Context Protocol is the most important open standard in agents. One protocol, 1,200+ servers, and your agent can plug into almost any system. Here's how it actually works.
One smart agent is fine. Two agents checking each other's work is better. Master the canonical orchestration patterns: planner/executor, judge/worker, debate, and swarm.
Fresh Career+ lessons added to the library.
Patterns for AI agents that fail well — recovering or degrading rather than crashing.
Pick the right deployment topology for your AI agent's latency and durability needs.
When and how reflection loops genuinely improve AI agent performance.
Tool API design for AI agents differs from API design for humans — here's how.
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The agent market matured fast. Here's the field map — frontier labs, frameworks, browsers, local stacks, benchmarks — so you can pick the right tool without shopping by hype.
Underneath every agent framework is the same primitive — the model returns a structured tool call, you execute it, you feed the result back. Master this loop and every framework looks familiar.
Model Context Protocol is the most important open standard in agents. One protocol, 1,200+ servers, and your agent can plug into almost any system. Here's how it actually works.
One smart agent is fine. Two agents checking each other's work is better. Master the canonical orchestration patterns: planner/executor, judge/worker, debate, and swarm.
LangGraph became the production favorite in 2026 for good reasons — explicit state, checkpointing, first-class MCP. Build a real agent end-to-end and learn why.
Claude Code isn't just a coding assistant — it's a general agent runtime with MCP, subagents, hooks, and skills. Treat it that way and you get a free, powerful platform.
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.
Browser agents — Operator, Atlas, Browser Use, MultiOn — are the most visible agent category. The capability is genuine, the failure modes are specific. Build with eyes open.
Numbers on leaderboards are seductive and often wrong. Learn the big benchmarks, their leaderboard positions, their recently-exposed cheats, and how to run your own evals.
A prototype agent and a production agent have the same LLM. What's different is everything around it — durable state, retries, idempotency, observability. The real engineering.
An agent is a new attack surface. Prompt injection, privilege escalation, data exfiltration — these are no longer theoretical. Learn the attacks and the defenses.
Everything comes together. Design, code, test, secure, and ship a production-quality agent with open-source code you can fork today.
Skills let you package a prompt, tools, files, and configuration into a named capability Claude can invoke on demand.
ChatGPT's agent mode can browse, click, file taxes, book meetings, write code across multiple apps.
Codex cloud can work in the background and in parallel. Learn how to split tasks so multiple agents do not trample the same files.
Models get more useful when they can act through tools. Learn the difference between hosted tools, your own functions, and MCP-connected capabilities.
Build an AI study agent that tracks what you've learned, plans your week, and adapts when you fall behind. Beyond chatbot prompting, into actual agentic study.
Move past chatbots and build a workflow where AI takes multi-step actions on your behalf. Here's the safe-by-default beginner pattern.
Turn the local Hermes Agent ecosystem into a product map students can reason about before they build their own agent system.
Design a CLI that starts sessions, routes profiles, loads safe config, and gives a human a precise way to steer an agent.
Use profiles to separate personal, classroom, local, and production agent behavior without rewriting the app.
Build a small model router that can send easy, private, or expensive tasks to the right model family.
Teach students how an agent safely discovers tools, validates calls, and limits what any session may do.
Show how skill files turn repeated work into reusable agent procedures students can inspect and improve.
Build a memory layer that recalls useful facts while preventing old memories from becoming new user commands. Build the small version Draw or write a fenced prompt layout that includes system rules, user input, retrieved memory, and tool results in separate sections.
Teach students how long-running agents summarize state without losing decisions, constraints, or next actions.
Design session keys so one agent can talk through many surfaces without mixing users or channels.
Turn the Hermes platform-adapter checklist into a student build plan for adding a new chat surface.
Create a delivery router so agent outputs land in the right channel, format, and approval state.
Show how scheduled agent work can run safely with budgets, summaries, and escalation rules.
Design webhook-triggered agents that validate requests before doing any useful work.
Teach the safe architecture for a local computer-control relay: observe, propose, approve, act, audit. What the local Hermes build teaches This build lab focuses on the local relay that lets an agent help with desktop tasks without becoming an uncontrolled operator.
Map a production-friendly control plane where Vercel receives requests, Supabase stores state, Resend sends mail, and a local relay handles private machine work.
Use the local Agent Lab idea to teach how prompt queues, workers, providers, and live status make AI work manageable.
Build the observability habits agents need: event logs, tool-call trails, counters, and human-readable status.
Design quotas, budgets, and backpressure so student agents do not quietly burn money or overload providers.