Multi-agent systems bring a new level of intelligence to enterprise decision-making, enabling LLM agents to interact across business domains. Explore how to build systems that break silos and improve operational resilience at scale.
AI agents go beyond generative AI models by leveraging LLMs integrated with tools to iteratively improve tasks. Where a model simply predicts outputs, such as producing code, an AI agent can run the code, analyze the results, and make improvements for greater accuracy and utility.
Begin by mapping the processes, workflows and connections between the actors within your organization—whether they are individuals or software apps. This establishes the foundational structure for the agent network, identifying where the agents will operate and interact. Mapping tools or AI models can then structure the agent network as a directed acyclic graph, reducing the risk of queries falling into a tailspin.
To define the responsibilities of the agents, start by selecting an architecture that ensures modularity and precise task allocation. A distributed approach, like the AAOSA architecture, allows agents to evaluate requests, delegate subtasks to downstream agents and communicate requirements upstream as needed. This setup enables agents to treat other agents and tools as functional resources, avoiding centralized bottlenecks.
For example, if a user informs the main search box agent of a life-changing event, such as “I’m getting married,” downstream agents like legal advice, payroll, HR and benefits collaborate to provide a unified response.
To build reliable and scalable multi-agent systems, it’s essential to implement safeguards that manage agent autonomy. These safeguards ensure agents act within defined boundaries, mitigate risks, and maintain operational integrity through oversight, error handling, and gradual integration.
