Coordination motifs when several agents divide work, debate, or review each other (PDF 243–245).
● Agent roles & specialization: Agents with unique purposes
● Orchestration frameworks: tools (like CrewAI, etc.) to build workflows.
This layer is about collaboration and coordination among agents.
4) Agentic Infrastructure
The top layer ensures these systems are robust, scalable, and safe. This includes:
● Observability & logging: tracking performance and outputs (using frameworks like DeepEval).
● Error handling & retries: resilience against failures.
● Security & access control: ensuring agents don’t overstep.
● Rate limiting & cost management: controlling resource usage.
● Workflow automation: integrating agents into broader pipelines.
● Human-in-the-loop controls: allowing human oversight and intervention.
This layer ensures trust, safety, and scalability for enterprise/production environments. Overall, Agentic AI, as a whole, involves a stacked architecture, where each outer layer adds reliability, coordination, and governance over the inner layers. 7 Patterns in Multi-Agent Systems Monolithic agents (single LLMs stuffed with system prompts) didn’t sustain for too long. We soon realized that to build effective systems, we need multiple specialized agents that can collaborate and self-organize. To achieve this, several architectural patterns have emerged for AI agents.
This visual explains the 7 core patterns of multi-agent orchestration, each suited for specific workflows:
1) Parallel
Each agent tackles a different subtask, like data extraction, web retrieval, and summarization, and their outputs merge into a single result. Perfect for reducing latency in high-throughput pipelines like document parsing or API orchestration.
2) Sequential
Each agent adds value step-by-step, like one generates code, another reviews it, and a third deploys it. You’ll see this in workflow automation, ETL chains, and multi-step reasoning pipelines.
3) Loop
Agents continuously refine their own outputs until a desired quality is reached. Great for proofreading, report generation, or creative iteration, where the system thinks again before finalizing results.
4) Router
Here, a controller agent routes tasks to the right specialist. For instance, user queries about finance go to a FinAgent, legal queries to a LawAgent. It’s the foundation of context-aware agent routing, as seen in emerging MCP/A2A-style frameworks.
5) Aggregator
Many agents produce partial results that the main agent combines into one final output. So each agent forms an opinion, and a central one aggregates them into a consensus. Common in RAG retrieval fusion, voting systems, etc.
6) Network
There’s no clear hierarchy here, and agents just talk to each other freely, sharing context dynamically. Used in simulations, multi-agent games, and collective reasoning systems where free-form behavior is desired.
7) Hierarchical
A top-level planner agent delegates subtasks to workers, tracks their progress,