AiVibeMulti-Agent System
A multi-agent system is a set of AI agents that work together, or sometimes compete, on a task, each with its own role, tools or knowledge, coordinated through messages, a shared plan or an orchestrating agent.
Common patterns include an orchestrator agent that breaks a task down and delegates subtasks to specialist agents, pipelines in which each agent hands its output to the next, and review set-ups in which one agent critiques another's work. Agents communicate through structured messages, shared memory or protocols such as Agent2Agent (A2A). The concept long predates LLMs in distributed AI research and robotics.
Multi-agent designs are used for research and report generation, software development with separate planning, coding and testing agents, customer service routing and analyses that span several data sources. Specialisation keeps each agent's instructions and tool set small and focused.
Coordination adds cost, latency and new failure modes: agents may duplicate work, contradict each other or pass errors along. Many tasks are handled as well by a single agent with good tools, so a multi-agent design should be justified by evaluation. Logging inter-agent messages and limiting each agent's permissions are important for debugging and security.
Key points
- Several agents with distinct roles collaborate on a task
- Patterns include orchestrator-worker, pipelines and reviewer agents
- Agents can communicate through protocols such as A2A
- Extra coordination cost should be justified by evaluation
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.