Learning guide
Learn to create autonomous AI agents that can perform complex tasks.
Overview
Dive deep into the world of AI agents with this advanced guide to LangChain. You'll learn how to build autonomous agents that can reason, plan, and execute multi-step tasks using external tools. From simple chain-of-thought patterns to complex agent architectures with memory and tool use, this guide provides hands-on experience building production-ready AI agent systems.
Roadmap
Each step is a segment of the path, designed to move from concepts into practical implementation.
Understand agent architectures and when to use them.
Set up LangChain and learn core concepts like chains and prompts.
Create tools that agents can use to interact with external systems.
Add short-term and long-term memory to your agents.
Build systems where multiple agents collaborate on tasks.
Deploy agents with proper error handling and monitoring.
Supporting reading
These posts cover parts of the same subject area and work well alongside the guide.
Claw Code's public docs and parity repo offer a useful blueprint for how modern AI coding agents are actually structured beyond the model layer.
Claw Code's parity workflow offers a strong model for teams rebuilding or migrating complex agent systems without drifting into vague rewrites or cargo-cult copies.
Claw Code's parity repo shows why modern coding agents often split responsibilities between Rust for runtime-critical paths and Python for orchestration and migration.
Announcements from NVIDIA and Anthropic between March 16 and March 17, 2026 show the AI race shifting toward long-running agents, policy runtimes, and physical-world execution.
Microsoft's Agent 365 points to a new enterprise AI battleground: a control plane for agent identity, policy, observability, and governed tool access.
Google's March 2026 Pixel Drop suggests Gemini is moving beyond chat and becoming a cross-app action layer for mobile, which may matter more than another model benchmark.