What are Multi-Agent Systems? - Definition & Meaning
Learn what multi-agent systems are, how multiple AI agents collaborate on complex tasks, and which applications and frameworks exist.
Definition
Multi-agent systems are AI architectures where multiple specialized agents collaborate to solve complex tasks. Each agent has a role, access to tools, and communicates with other agents via an orchestrator or directly.
Technical explanation
Patterns: hierarchy (manager delegates to workers), sequential (agent A → B → C), collaborative (agents discuss/negotiate). Frameworks: CrewAI (role-based teams), AutoGen (conversational agents), LangGraph (state machines), MetaGPT. Challenges: coordination overhead, cost (many LLM calls), inconsistency. Benefits: specialization, parallel work, more complex tasks than one agent can handle.
How AVARC Solutions applies this
AVARC Solutions builds multi-agent systems for complex workflows: research (researcher + writer + reviewer), document processing (extractor + validator + summarizer), support (triage + specialist + escalator). We use LangGraph or CrewAI depending on the use case.
Practical examples
- A research pipeline with a researcher agent finding sources, a writer creating a draft, and a critic giving feedback.
- A support system where a triage agent classifies, a specialist answers, and an escalator handles complex cases.
- A code review workflow with reviewer, fixer, and validator agents iterating until all issues are resolved.
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