daveshap / ACE_Framework

ACE (Autonomous Cognitive Entities) - 100% local and open source autonomous agents
MIT License
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Proposed ROADMAP #139

Open daveshap opened 1 year ago

daveshap commented 1 year ago

Proposed Roadmap for ACE Framework

  1. Foundations of Single Agent Autonomy: Develop and refine the core capabilities of a single autonomous agent. This includes defining its scope of tasks, the tools it can utilize, and the principles governing its self-directed behavior. Establish robust memory systems and internal communication protocols that allow the agent to process and retain information effectively. Ensure that the agent can perform tasks independently and handle basic decision-making processes.

    • Define agent capabilities and toolset
    • Develop self-direction and decision-making algorithms
    • Implement memory systems for information retention
    • Establish internal communication protocols
    • Creating and using tools on the fly
    • Self-checking and self-consistency* (stretch goal)
    • Self-modification* (stretch goal)
  2. Flat Network Collaboration: Implement and test a flat network where multiple agents can communicate and collaborate within a shared environment. Explore different communication models (e.g., round-robin, chat room, asynchronous messaging) to determine the most effective methods for agent interaction. Focus on enabling agents to share information, coordinate on tasks, and work towards common goals without hierarchical structure, all within a single operational domain or container.

    • Enable multi-agent communication within a single environment
    • Test various communication models for effectiveness
    • Coordinate shared tasks and collaborative goals
    • Ensure information sharing and task synchronization
  3. Cross-Container Team Dynamics: Expand the communication framework to support teams of agents operating across multiple containers or isolated environments. This stage involves establishing protocols for inter-container communication, ensuring that agents can collaborate effectively even when distributed across different systems or locations. Address challenges related to synchronization, data consistency, and task delegation among siloed agent teams.

    • Create protocols for communication across isolated environments
    • Manage distributed agent collaboration and data consistency
    • Facilitate task delegation and execution among siloed teams
    • Overcome challenges in synchronization and inter-container operations
  4. Hierarchical Communication Systems: Introduce hierarchical structures to the agent network, allowing for more complex organization and coordination. Develop vertical communication channels that enable agents to escalate issues, seek guidance, or report outcomes to higher-level agents. Simultaneously, maintain horizontal communication for peer-to-peer collaboration. This milestone focuses on creating a multi-layered network that can handle intricate tasks and workflows.

    • Introduce vertical communication for escalation and reporting
    • Maintain horizontal communication for peer-level collaboration
    • Develop a multi-layered network for complex task management
    • Implement control structures for information flow and task prioritization
  5. Self-Organizing Networks: Achieve the capability for agents to autonomously construct and optimize their own networks. Agents should be able to self-organize based on task requirements, environmental factors, and predefined objectives. This involves agents dynamically forming teams, assigning roles, and reconfiguring the network topology as needed. The principles established in previous milestones will guide the agents in creating efficient and effective networks that adapt to changing conditions and scale to accommodate various complexities.

    • Empower agents to autonomously form and optimize networks
    • Allow dynamic team formation and role assignment based on tasks
    • Adapt network topology to environmental changes and task demands
    • Scale networks to handle varying complexities autonomously