Evaluation

  • Enterprise AI: An Analysis of Compound Architectures and Multi-Agent Systems

    Enterprises are moving from single model apps to coordinated systems that plan act and learn across real workflows. This article explains how to design and run compound AI and multi agent systems that ship value in production. The core pattern is modular. A planner turns goals into steps. Specialist agents and trusted tools execute against your CRM ERP data warehouse and APIs. Interoperability improves with Model Context Protocol for tool use and Agent2Agent for agent collaboration so teams can reduce lock in and evolve safely.
    The work does not end at architecture. Runtime governance observability and clear measures decide outcomes. You get a practical checklist for incident handling timeouts retries circuit breakers and human escalation. You also get metrics you can compute from traces such as Task success rate Information Diversity Score and Unnecessary Path Ratio. A simple worksheet turns messages tools tokens and review time into cost per successful task so finance and engineering can track the same numbers.
    Use this blueprint to fund the next quarter. Stand up observability. Adopt MCP and A2A where they fit. Form cross functional squads. Move from isolated use cases to full business processes with measurable gains in speed accuracy and auditability