Complete learning path from beginner to production, 5 modules with 17 articles
Goal: Understand the minimum viable Agent architecture
Learn core Agent concepts, ReAct pattern, Tool Use and Planning, build Agent mental model
Agent Architecture Intro: ReAct / Tool Use / Planning
Over the past year, we've worked with dozens of teams building large language model (LLM) agents across industries. The most successful implementations weren't using complex frameworks, but simple, composable patterns.
2Build your first Agent with SDK
Last year, we shared lessons in building effective agents alongside our customers. Since then, we've released Claude Code, an agentic coding solution that we originally built to support developer productivity at Anthropic.
Goal: Give Agent "action capability"
Master parallel tool calls, nested calls, error handling, learn tool design principles and capability modularization
Parallel / Nested / Error Handling
The future of AI agents is one where models work seamlessly across hundreds or thousands of tools. An IDE assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines.
4Agent Tool Design Principles
The Model Context Protocol (MCP) can empower LLM agents with potentially hundreds of tools to solve real-world tasks. But how do we make those tools maximally effective?
5Explicit Reasoning Control
As we continue to enhance Claude's complex problem-solving abilities, we've discovered a particularly effective approach: a "think" tool that creates dedicated space for structured thinking during complex tasks.
6Skills Abstraction & Reuse
As model capabilities improve, we can now build general-purpose agents that interact with full-fledged computing environments. Claude Code, for example, can accomplish complex tasks across domains using local code execution and filesystems.
7Skills + MCP Server to Extend Agent
When we released the Model Context Protocol (MCP) last year, we saw developers build amazing local servers that gave Claude access to everything from file systems to databases.
Goal: Solve "memory & attention" problems in long tasks
Learn context structure design, context-aware RAG, ensure long conversation stability and retrieval serving tasks
Context Structure Design
After a few years of prompt engineering being the focus of attention in applied AI, a new term has come to prominence: context engineering. Building with language models is becoming less about finding the right words.
9Context-aware RAG
For an AI model to be useful in specific contexts, it often needs access to background knowledge. Developers typically enhance an AI model's knowledge using Retrieval-Augmented Generation (RAG).
Goal: Agent Systematization
Master long-task execution frameworks, interruption recovery, state persistence, and multi-Agent collaboration architecture
Long-task Execution Framework
As AI agents become more capable, developers are increasingly asking them to take on complex tasks requiring work that spans hours, or even days. Getting agents to make consistent progress across multiple context windows remains an open problem.
11Multi-Agent Collaboration Architecture
Claude now has Research capabilities that allow it to search across the web, Google Workspace, and any integrations to accomplish complex tasks. The journey from prototype to production taught us critical lessons.
12Agent Execution Environment
The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Connecting agents to tools and data traditionally requires a custom integration for each pairing.
Goal: Production-ready & Scalable
Learn Agent evaluation methodology, sandboxing & permission isolation, production practices, and real incident postmortems
Agent Evaluation Methodology
Good evaluations help teams ship AI agents more confidently. Without them, it's easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others.
14Sandboxing & Permission Isolation
In Claude Code, Claude writes, tests, and debugs code alongside you, navigating your codebase, editing multiple files, and running commands to verify its work. Giving Claude this much access can introduce risks.
15Coding Agent Engineering Experience
We recently released Claude Code, a command line tool for agentic coding. Developed as a research project, Claude Code gives Anthropic engineers and researchers a more native way to integrate Claude into their coding workflows.
16Real Incident Postmortem
Between August and early September, three infrastructure bugs intermittently degraded Claude's response quality. We've now resolved these issues and want to explain what happened.
17How Anthropic Teams Use Claude
Our latest model, the upgraded Claude 3.5 Sonnet, achieved 49% on SWE-bench Verified, a software engineering evaluation, beating the previous state-of-the-art model's 45%.