AI Orchestration

No model does everything well. Orchestration combines each model's strengths: GPT-4 for writing, Claude for reasoning, Whisper for transcription, etc.

OpenClaw and n8n for developers, Relevance AI and Activepieces for no-code. LangChain and LlamaIndex for programmatic frameworks.

A typical example is a customer-support workflow: an incoming ticket is classified by one model, routed to a retrieval step that pulls the right knowledge-base articles, drafted by an LLM, then checked by a second model before sending. Tools like n8n, LangChain or LangGraph wire these steps together, passing outputs from one component to the next so the chain runs as a single coordinated process.

An AI agent is a single autonomous unit: an LLM that reasons, decides, and calls tools to reach a goal. AI orchestration is the layer above it, coordinating multiple agents, models, and tools into one workflow, managing how they pass data, run in sequence or parallel, and handle errors. In short, an agent does the work; orchestration decides which agent runs when and how results combine.

An orchestrator breaks a goal into subtasks and assigns each to a specialized agent. It manages the control flow, deciding what runs sequentially or in parallel, routes outputs between agents, maintains shared state or memory, and handles retries when a step fails. Frameworks like LangGraph and CrewAI model this as a graph or a team of role-based agents, while a supervisor agent often coordinates the others.

A coding assistant is a good example: a planner agent breaks a feature request into steps, a coding agent writes the changes, a testing agent runs the suite, and a reviewer agent inspects the result. Each can use tools, loop back on failures, and pass control to the next. CrewAI and LangGraph are commonly used to build such multi-agent setups where agents collaborate rather than run a fixed script.

There is no single best tool; it depends on your needs. LangChain and LangGraph are popular for code-first, graph-based agent workflows. CrewAI suits role-based multi-agent teams. n8n and Activepieces offer low-code visual automation, while Relevance AI targets no-code users. For production agents, teams often pair LangGraph with an observability layer. Pick based on whether you need code control, visual building, or no-code speed.

ChatGPT is built on an LLM (the underlying GPT model), but the product also adds agent-like features: it can call tools, browse, run code, and use memory to pursue multi-step tasks. In orchestration terms, the raw model is one component, while the agent behavior, tool use, and routing around it form an orchestration layer that turns a single model into a more capable assistant.