Orchestration Patterns for Multi-Agent Systems

We have moved beyond "Chain of Thought" to "Graph of Thoughts". In 2024, everyone was building simple RAG wrappers. In 2026, the cutting edge is multi-agent orchestration. Using libraries like LangGraph (Python) and AutoGen, we are building cyclic agentic flows where specialized agents collaborate to solve opaque problems.

The "Supervisor-Worker" Pattern

In our recent deployment for a FinTech client, we implemented a Supervisor pattern. A central "Manager" agent (powered by GPT-4o) breaks down a user request ("Analyze the risk of this portfolio") into subtasks:

• Researcher Agent: Scrapes the web for recent news on the holdings.

• Quant Agent: Runs Python code to calculate volatility and Sharpe ratios.

• Writer Agent: Synthesizes the findings into a PDF report.

The Supervisor reviews the output of each worker. If the Writer's draft contradicts the Quant's data, the Supervisor rejects the task and loops it back for correction. This Self-Correction loop is what separates a toy demo from an enterprise tool.

State Management is Key

The hardest part isn't the prompting; it's the state. Managing the shared context window across 5 agents without blowing up your token budget requires aggressive summarization strategies and a robust persistence layer (like Redis or Postgres) to store the "Memory" of the conversation graph.