AI agent orchestration
What is AI agent orchestration?
AI agent orchestration is the coordination of one or more AI agents, tools and data sources into a single controlled workflow that decides what runs, in which order, and with what context. An orchestrator routes each request to the right agent or step, passes along the conversation and the user's identity, handles tool calls and retries, and assembles the final answer. It matters because production agents fail at the seams between steps, and orchestration makes those seams explicit, testable and observable.
In AgentRow: Multi-agent orchestrationMulti-agent systems (MAS)
What are multi-agent systems?
A multi-agent system is an application in which several AI agents, each with its own role, instructions, tools and knowledge, work together on a task instead of one agent doing everything. The agents are coordinated by an orchestration layer that selects, sequences or routes between them, and their combined output is returned as one result. Multi-agent systems trade the simplicity of a single prompt for specialised, smaller agents that are easier to test, permission and replace.
In AgentRow: Multi-agent orchestrationAI agent observability
What is AI agent observability?
AI agent observability is the ability to reconstruct exactly what an agent did on a request: which steps ran, what each model call received and returned, which tools were called with which inputs and outputs, what was retrieved from knowledge or memory, and what it cost in tokens, time and money. It goes beyond logging errors: every run is a complete trace that can be replayed, compared and audited. Teams rely on it to debug wrong answers, prove compliance, control spend and improve agents from real behaviour rather than guesses.
In AgentRow: Runtime observabilityRetrieval-augmented generation (RAG)
What is retrieval-augmented generation?
RAG is a technique that gives a language model relevant excerpts of your own documents at answer time so its response is grounded in that content. The documents are split into chunks and embedded; at each question the most similar chunks are retrieved and placed in the prompt, often with citations. It lets an agent answer from current, private information without retraining the model.
In AgentRow: Knowledge bases and RAGAI workflow designer
What is an AI workflow designer?
An AI workflow designer is a visual editor for defining the steps an AI application takes: input handling, memory and knowledge retrieval, model calls, tool use, validation and the final response, connected by edges and conditions. The best designers produce an executable definition rather than a picture, so what you see is what runs. It gives teams a shared, reviewable artifact for how an agent behaves.
In AgentRow: Visual agent builder