Multi-agent
Multiple AI agents working together to complete tasks, like a team of specialized workers.
Multi-agent systems involve several AI programs, called agents, that communicate and cooperate to achieve a goal.
Think of it like a team of people working on a project. One agent might be good at finding information, another at summarizing it, and a third at writing a report. By working together, they can tackle complex tasks that a single AI might struggle with. For example, a multi-agent system could research a scientific topic by having different agents gather data, analyze it, and then synthesize the findings into a coherent summary.
Multi-agent systems are architectures where multiple autonomous AI agents interact within a shared environment to perform tasks or achieve objectives. These agents can possess specialized capabilities and communicate through defined protocols, enabling emergent behaviors and complex problem-solving. The core idea is to decompose a large problem into smaller, manageable sub-problems that individual agents can address, with coordination mechanisms ensuring coherence and efficiency. Tradeoffs often involve increased complexity in coordination and communication overhead versus the benefits of specialization and parallel processing.
Frontier AI labs are exploring multi-agent systems to tackle more complex problems. These systems allow specialized AI agents to collaborate, as seen in research automation and real-time information gathering. This approach promises more robust and observable AI workflows.
- Dynamic workflows in Copilot CLI and the Copilot appMicrosoft · GitHub Changelog: Copilot ·
- RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking ModelsAnthropic · arXiv cs.AI ·
- Multi AgentxAI · xAI Docs: Guides ·
- Anthropic Claude
- Anthropic's RankEvolve system uses a multi-agent approach to automate the discovery and improvement of ranking models.
- Microsoft Copilot
- Microsoft Copilot uses multi-agent concepts to enable dynamic workflows and observable automation of complex AI tasks.
- xAI Grok
- xAI Grok's Realtime Multi-agent Research beta allows specialized AI agents to collaborate on complex research tasks.
- Ensemble Methods
- Ensemble methods combine multiple models to improve predictive
- Multi-task Learning
- Multi-task learning trains a single model to perform multip
- Federated Learning
- Federated learning trains models across decentralized device
The concept of multi-agent systems has roots in artificial intelligence research, game theory, and distributed computing, with early work exploring how multiple autonomous entities can interact and coordinate.
You can explore multi-agent concepts by using tools that allow you to chain prompts or create simple workflows. For instance, try asking an AI chatbot to act as two different personas (e.g., a critic and a creator) and have them debate a topic, simulating a basic form of agent interaction.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- HeuristicAuthoring dynamic workflows with Copilot assistanceLeverage Copilot's built-in guidance to learn about or fully generate dynamic workflows. · 15 min
- Setting tipEnable encrypted content for multi-agent context preservationSet use_encrypted_content to True to include sub-agent state in the response for multi-turn conversations. · 1 min
- Code snippetAPI request for multi-agent research with tools and agent countExample API call to grok-4.20-multi-agent specifying model, tools, agent count, and content encryption. · 10 min
- Anti-patternAvoid broad, unstructured questions for multi-agent researchDon't ask vague questions; provide specific dimensions and structure for better multi-agent research outcomes. · 2 min
- ChecklistPre-computation checklist for multi-agent research tasksItems to consider before initiating a multi-agent research request. · 5 min
Questions people ask
- What is an AI agent?
- An AI agent is a program designed to perceive its environment, make decisions, and take actions to achieve specific goals, often with a degree of autonomy.
- How do agents communicate in a multi-agent system?
- Agents can communicate through various methods, including message passing, shared memory, or by observing and acting within a common environment, depending on the system's design.
First seen on Subvolts Jul 2, 2026. Updated Oct 8, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.