A running notebook

AI agent orchestration is the process of coordinating multiple autonomous AI agents to work together toward shared goals, providing a control layer for managing execution, context, and collaboration across agents. Rather than relying on one general-purpose model, it coordinates multiple specialized AI agents within a unified system to efficiently achieve shared objectives.

In practice, orchestration works by breaking larger objectives into defined actions, dividing a workflow into smaller tasks that can be assigned more intentionally rather than asking one agent to handle everything. An orchestration layer determines which agent acts, when, on what data, and with what authority, managing hand-offs, shared memory, and error recovery so agents can coordinate on a shared goal, break it into sub-tasks, and recover from failures without human intervention.

Several open-source frameworks have emerged to implement these patterns. For example, LangGraph is a low-level, graph-based orchestration runtime with durable state and fine-grained control, and is strong for long-running, stateful systems. Common orchestration patterns include sequential pipelines, hierarchical "manager-worker" setups, and decentralized swarms, with differences between frameworks lying in orchestration model (graph-based vs. role-based vs. swarm), state management, and communication pattern. Good orchestration design requires attention to workflow structure, shared context, governance, and observability before connecting agents in production.

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that pairs a large language model (LLM) with an external retrieval system, so the model can pull in relevant, up-to-date information before generating a response rather than relying solely on what it memorized during training. Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries, using these documents to supplement information from its internal training data.

For AI product managers, RAG matters because it closes the gap between a general-purpose model and a product that needs to reason over proprietary, current, or fast-changing data — like a knowledge base, support tickets, or product catalog — without the cost and delay of retraining the model. Product managers can reference customer feedback and user behaviors when considering future development choices, and more broadly, RAG systems can power internal knowledge tools, customer support, onboarding, and recommendation features by grounding LLM outputs in trusted, retrievable source data.

From a PM perspective, adopting RAG typically shifts scoping conversations toward decisions like which knowledge sources to index, how to keep them fresh, retrieval quality/relevance tuning, latency and cost tradeoffs, and how to handle citation and trust in the generated answer — making RAG as much a data and workflow design problem as a model choice.