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Behind the Build: DeepAgent Architecture

Technical deep dive into our multi-agent orchestration framework

Suren
8 min read
Technical, Architecture, AI

Behind the Build: DeepAgent Architecture


A technical exploration of how we designed DeepAgent to coordinate multiple AI agents working together on complex tasks.


The Challenge of Coordination


When multiple AI agents work together, coordination becomes the primary challenge. Each agent has its own perspective, capabilities, and limitations. How do we ensure they work together effectively?


The Traditional Approach


Most multi-agent systems rely on:


  • Central coordination - A single controller manages all agents
  • Predefined protocols - Rigid communication patterns
  • Static role assignment - Agents have fixed responsibilities

    • This approach works for simple scenarios but breaks down as complexity increases.


    Our Approach: Hierarchical Orchestration


    DeepAgent uses a hierarchical approach to orchestration:


    Level 1: Strategic Agents (Planning)

    Level 2: Tactical Agents (Implementation)

    Level 3: Specialist Agents (Execution)


    This structure allows for both top-down direction and bottom-up feedback.


    Communication Protocol


    We've developed a standardized communication protocol that allows agents to:


  • Share context and state information
  • Request assistance from other agents
  • Provide feedback on outcomes
  • Negotiate resource allocation

    • Automation, at the heart of who we are.
    "The magic happens when agents can discover and leverage each other's capabilities dynamically."

    Results So Far


    In our initial tests, this architecture has shown promising results:


  • Teams of agents can successfully collaborate on tasks that would be impossible for any single agent to complete
  • 89% success rate on complex multi-step problems
  • 67% reduction in coordination overhead

    • We're continuing to refine this approach and will share more detailed benchmarks in future posts.


    For more technical details, check out our [research papers](https://research.sucoro.com) or explore our [open-source implementations](https://github.com/sucoro/deepagent).