Learning track 04
Agent Systems
Study pipeline patterns, tool loops, protocols, orchestration frameworks, and operational agent design.
- Guides
- 6
- Sections
- 46
- Cards
- 210
Suggested sequence
Follow the track or enter where the problem begins.
Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.
- 01Vol. 01
LLM Pipeline Patterns
A comprehensive catalog of steps and tricks for building production systems based on language models. Each pattern is atomically parsed.
- Sections
- 10
- Cards
- 47
- 02Vol. 06
LLM Scaffolding: Tool Calling, Structured Output & Agent Loops
Full analysis of the orchestration layer over LLM: function calling, JSON schema, constrained decoding, ReAct/CoT patterns, agent cycle, context memory management, error handling and comparison of API providers.
- Sections
- 8
- Cards
- 27
- 03Vol. 11
MCP, A2A & Agent Protocols
Model Context Protocol (Anthropic, 2024) - architecture, creation of servers in TypeScript and Python, all transports, primitives, security. Agent-to-Agent (Google, 2025) - task model, AgentCard, streaming. Comparison of ecosystem protocols.
- Sections
- 7
- Cards
- 34
- 04Vol. 21
LangChain & LangGraph Under the Hood
This volume is not about “which framework is more fashionable,” but about the modern architecture of the LangChain Inc. stack. In the current version, `LangChain` is a high level with models, messages, tools, middleware and fast `create_agent`, and `LangGraph` is a low-level runtime for stateful agents: `State`, `Nodes`, `Edges`, `Command`, `thread_id`, `checkpoints`, `interrupts`, replay and long-running execution. Below is how this is really connected and what exactly happens during startup.
- Sections
- 7
- Cards
- 32
- 05Vol. 30
Reasoning Models & Tool Loops
This volume is about a new layer of LLM engineering: models that know how to spend more inference-time compute on reasoning, and applications that make them alternate with tools. It is important to understand two things at once: what is the reasoning runtime itself and how is the loop model -> tool -> model, where state, budgets, stop conditions and engineering invariants live.
- Sections
- 7
- Cards
- 35
- 06Vol. 31
The OpenClaw Phenomenon and What to Learn from It
The project started as Clawdbot, was renamed Moltbot on January 27, 2026, and then OpenClaw on January 30, 2026. But the essence of the phenomenon was not in the title. This was a rare case of an open-source LLM product hitting the right intersection of UX, architecture, distribution, and memetics. This volume looks at what decisions it really made, why it went viral, and what lessons to learn from it without copying its mistakes.
- Sections
- 7
- Cards
- 35