LLM Agent Framework Design and Task Breakdown
Overview
A framework to coordinate multiple GPTel preset-based agents, each assigned a subproblem of a main goal. Each agent runs in its own buffer and agents coordinate by following a defined state machine until the main goal is achieved.
Goals
- Modularity: Easy to define, add, and remove agents.
- Coordination: Agents communicate results and triggers.
- Transparency: Each agent’s state and buffer visible.
- Deterministic Completion: The orchestrator knows when the main goal is achieved.
Components
- Orchestrator: Controls agents, state, and messaging.
- Agents: Encapsulated GPTel presets + buffers.
- State Machine: Defines agent states and transitions.
- Buffers: One per agent, storing context and logs.
Implementation Tasks
Define Agent Data Model
- Document required fields (preset, buffer, state, dependencies, etc).
- Decide on struct/list/object for in-memory representation.
Design Global Orchestrator
- Orchestrator holds agent list and state.
- Functions for initializing, updating, and finishing workflows.
- Plan hooks/callbacks for response arrival and step advancement.
Specify State Machine
- Enumerate agent states (Idle, Submitted, Waiting, Running, Completed, Error).
- Draft allowed transitions and state diagrams.
- Define transition triggers (incoming data, dependencies, timers).
Agent Buffer Integration
- Create buffers named after agent roles (e.g., llm-agent-research).
- Write functions to initialize and update buffers.
- Connect buffers to orchestrator state.
Agent Lifecycle and Execution
- Implement agent task initiation: send context/prompt to GPTel.
- Handle output: parse, store in buffer, notify orchestrator.
- Document interface between orchestrator and agent buffers.
Agent Coordination
- Implement message passing or result handoff between agents (via orchestrator).
- Document how dependencies are tracked and checked.
Goal/Termination Conditions
- Formalize how orchestrator knows when main goal is achieved.
- Plan for error handling and recovery.
User Interaction and Monitoring
- Optional: Provide ways to inspect or steer agent buffers and state.
Example Use Case (Documented)
- User defines a main problem and subproblems mapped to agents.
- Each agent buffer is created and initialized with its GPTel preset.
- Orchestrator triggers agents based on dependencies.
- Agent finishes a task, writes output to its buffer.
- Orchestrator advances state, possibly triggering downstream agents.
- When all requirements met, orchestrator marks goal as completed.
Future Enhancements
- Parallel agent execution (async triggers)
- Visualizations of agent progress and states
- Dynamic agent creation/removal during workflow