AI/ML
Agent Orchestration
How AI agents are coordinated at production scale
Definition
Agent orchestration is the coordination layer that manages task routing, state, error recovery, and human escalation across AI agent workflows. 1Raft builds orchestration systems with checkpointing and fault recovery for reliable multi-agent deployments in production.
How it works
Building a single AI agent is one challenge. Running multiple agents together in production is a different problem entirely. Agent orchestration is the layer that decides which agent handles which task, passes context between agents, tracks progress, and intervenes when something goes wrong. Without orchestration, multi-agent systems are just a collection of independent programs with no coordination.
An orchestration layer typically manages four concerns. Task routing determines which agent or agent chain handles an incoming request based on intent classification. State management maintains context across steps so agents can resume after interruptions. Error recovery detects failures - timeouts, malformed outputs, tool errors - and retries, reroutes, or escalates. Human escalation pauses the workflow and surfaces the decision to a person when the system encounters something outside its confidence threshold.
The orchestration layer is often the hardest part of a multi-agent system to get right. It needs to handle partial failures gracefully - what happens when agent 3 of 5 fails mid-workflow? Production systems need checkpointing so workflows can resume from the last successful step, not restart from scratch. They also need observability that traces a single request across every agent it touches, making debugging possible when things go wrong.
How 1Raft uses Agent Orchestration
1Raft builds orchestration layers for clients running multi-agent workflows in production. For an enterprise operations platform, we built an orchestration system that routes incoming requests to specialized agents based on intent, manages conversation state across multi-turn interactions, and checkpoints progress at each step - so if an agent fails, the workflow resumes from the last successful checkpoint instead of restarting. Every orchestrated workflow includes end-to-end tracing for debugging and compliance.
Related terms
AI/ML
AI Agent
An AI agent is a software system that uses a large language model to plan, reason, and take actions autonomously. Unlike chatbots that respond to single prompts, agents execute multi-step workflows - calling APIs, querying databases, and making decisions to achieve a defined goal.
AI/ML
Multi-Agent System
A multi-agent system (MAS) coordinates two or more AI agents to accomplish tasks that are too complex for a single agent. Agents in a MAS communicate, delegate subtasks, and share context - following patterns like hierarchical management, pipeline assembly lines, or peer-to-peer collaboration.
AI/ML
Agentic AI
Agentic AI refers to AI systems that can plan, make decisions, and take actions autonomously to achieve a goal. Unlike simple chatbots that respond to one prompt at a time, agentic systems break complex tasks into steps, use tools, and self-correct along the way.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
MLOps
MLOps (Machine Learning Operations) is the set of practices for deploying, monitoring, and maintaining machine learning models in production. It applies DevOps principles to ML systems, keeping models accurate, reliable, and cost-effective after launch.
Related services
Next Step
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