AI/ML
Multi-Agent System
How multiple AI agents coordinate complex workflows
Definition
A multi-agent system coordinates multiple specialized AI agents that communicate, delegate subtasks, and share context to solve problems too complex for a single agent. 1Raft designs multi-agent architectures with typed schemas and observability across agent boundaries for production reliability.
How it works
A single AI agent can handle a focused task, but real-world workflows often require different types of expertise. A multi-agent system splits work across specialized agents - one researches, another analyzes, a third writes, and a fourth reviews. Each agent focuses on what it does best, and the system coordinates handoffs between them. This mirrors how human teams operate, but at machine speed.
Multi-agent systems follow common coordination patterns. In a hierarchical pattern, a manager agent delegates tasks to worker agents and aggregates results. In a pipeline pattern, agents process work sequentially - each one refining the output of the previous. In peer-to-peer collaboration, agents negotiate and share findings directly. The right pattern depends on the workflow. Sequential document processing fits a pipeline. Research synthesis fits hierarchical delegation.
The complexity cost is real. Each additional agent multiplies potential failure points - network calls, context misalignment, conflicting outputs, and compounding latency. Production multi-agent systems need typed communication schemas between agents, shared state management, clear fallback behavior when one agent fails, and end-to-end tracing to debug issues across agent boundaries.
How 1Raft uses Multi-Agent System
1Raft designs multi-agent architectures for clients whose workflows demand specialized reasoning at each stage. For a logistics company, we built a system where a planning agent optimizes routes, a compliance agent checks regulatory requirements per region, and a communication agent generates carrier-specific instructions - all coordinated through a shared state store with typed schemas and full observability across every agent boundary.
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
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
Model Inference
Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. When you send a prompt to an LLM and get a response, that is inference. It is where compute costs, latency, and user experience are determined.
Related services
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