AI & ML / README.md

Agents and orchestration

Updated 2 min read index source
On this page4
  1. Core
  2. Tools and structured output
  3. Running them in production
  4. The three answers worth having

Agents and orchestration

Rewritten for LangChain/LangGraph 1.0 (both GA October 2025).

Core

# File Covers
01 What is an agent the definition that matters, and the ladder from single call to multi-agent
02 The agent loop ReAct, termination, loop detection, context growth
03 LangChain and LangGraph 1.0 middleware and durable execution
LangGraph, in depth the nine-note deep dive: state, reducers, routing, persistence, interrupts, streaming, composition, production
04 Multi-agent patterns topologies, handoffs, why it’s usually the wrong first answer
05 Durable execution and human-in-the-loop checkpointing, approval gates, LangGraph vs a workflow engine
06 Agent protocols: MCP, A2A and the interop layer MCP vs A2A, and the governance gap
07 Agent failure modes what actually breaks, and how to test a non-deterministic system

Tools and structured output

# File Covers
08 Function Calling, Tool Use, and Structured Output schemas, tool definitions
09 LLM JSON Validation — Common Interview Questions and Answers validating model output
10 Agent Patterns — Plan-and-Execute, Reflection, Cost, Evaluation advanced patterns
11 Chatbot & Pydantic AI worked chatbot design
12 Pydantic AI the type-safe alternative framework
13 LlamaIndex, and choosing between frameworks picking between the frameworks

Running them in production

The distributed-systems view. An agent in production is a component in a system, not a loop in a process.

# File Covers
14 Long-running agent jobs the job shape, callbacks, resumption, cancellation
15 Event-driven agents agents as consumers, at-least-once, poison messages
16 Concurrency and backpressure provider limits, per-tenant fairness, cost as a rate limit
17 Computer-use and code agents UI-driving and code agents, and why code agents work
18 The agent framework landscape CrewAI, MS Agent Framework, Agents SDK, ADK, Strands
19 The Claude Agent SDK Claude Code as a library: built-ins, subagents, skills, hooks

The three answers worth having

An agent is an LLM that decides its own control flow. If your code fixes the sequence, it’s a workflow — and determinism is a feature. Walk the ladder (single call, chain, router, bounded tool loop) before reaching for autonomy.

Durable execution is what makes LangGraph more than a nicer loop. Every node transition is checkpointed, so a run survives restarts, pauses for hours awaiting approval, and can be rewound for debugging. An agent becomes a resumable process rather than a function call.

Multi-agent compounds unreliability. Five agents at 90% each is roughly 59% end-to-end. Use it for context isolation, genuine parallelism or privilege separation — not because a task has several parts.

Contents 30

Notes