Getting started with AI agents: Mapping processes, roles and connections

The growing sophistication of AI agent networks is enabling organizations to automate complex workflows and enhance user interactions across various business functions, with multi-agent systems offering improved productivity and operational resilience.

Core concepts; Large Language Models (LLMs) enhanced with specific tools form the foundation of modern AI agents, which differ from simple generative AI by their ability to execute, observe, and modify their actions.

• AI agents can employ tools to run code in containers and iteratively improve their output based on results
• Unlike basic generative AI that simply produces output from input, agents can actively interact with their environment and other agents
• Multiple agents can work together in a coordinated network to handle complex tasks across different departments or functions

System architecture and design principles: A distributed architecture that respects the encapsulation of responsibilities proves more effective than centralized coordination systems.

• Agents must form a directed acyclic graph (DAG) to prevent query loops and ensure efficient processing
• Each agent can decide whether to process requests independently or delegate to other agents
• The AAOSA architecture, previously used in early versions of Siri, enables agents to gather requirements and coordinate responses through up-chain and down-chain communications

Implementation framework: Organizations can implement multi-agent systems by first mapping their existing workflows and organizational structures.

• AI tools can help identify workflows and generate agent network definitions
• Third-party specialized agents can be integrated through API wrapping
• Each agent requires clear roles, responsibilities, and tools defined in their system prompts
• Agents communicate through standardized protocols, treating down-chain agents as tools with flexible arguments

Practical applications: Multi-agent networks can enhance various business scenarios, from HR processes to technical support.

• In HR scenarios, agents can coordinate responses across legal, payroll, and benefits departments for complex employee situations
• For technical support, multiple agents can simultaneously address immediate issues while preparing upgrade quotes
• Customer-facing agents can consolidate information from multiple backend systems into unified responses

Looking ahead: The evolution of multi-agent systems brings both opportunities and challenges that require careful consideration.

• Future developments will need to focus on implementing safeguards and human intervention mechanisms
• Systems must address potential issues like tailspins and overloads through timeouts and redundancy measures
• The success of multi-agent networks will depend on carefully balanced automation and human oversight