Orchestration Meta Agent Design

Meta Agent Design

The Meta Agent, also known as the Supervisor Agent, orchestrates a combination of worker agents — which can be ReAct agents, Multi-Agents, or hybrids of both. It manages and supervises these worker agents by deciding which agent(s) to invoke based on the user's query and coordinates their responses to deliver the final answer.

How It Works:

1. User Query Input:

   The user query is received by the Meta Agent, which acts as the central supervisor.

2. Worker Agent Selection:

   The Meta Agent analyzes the query and decides which type of worker agent (ReAct, Multi-Agent, or a combination) is best suited to handle the task based on the query’s complexity, required capabilities, and context.

3. Delegation to Worker Agent(s):

   The selected worker agent(s) are called upon to process the query. Each worker agent internally uses their bound tools — small Python functions implementing specific logic or actions — to perform their tasks.

4. Worker Agent Processing:

   • ReAct Agents proceed through their iterative reasoning and acting cycle (reasoning about tools, acting, observing results, and refining).

   • Multi-Agents may work collaboratively or independently, coordinating their tools and knowledge to resolve the query.

5. Response Aggregation:

   The Meta Agent collects and evaluates responses from the worker agent(s).

6. Decision Making & Final Answer:

   Based on aggregated responses and overall context, the Meta Agent decides:

   • To invoke additional worker agents if needed, or

   • To conclude and return the final consolidated answer to the user.

The Meta Agent (Supervisor) acts as a high-level controller that dynamically delegates tasks to the most appropriate internal worker agent(s), whether ReAct, Multi-Agent, or a hybrid. It handles query routing, response coordination, and final decision-making, ensuring a flexible and efficient multi-layered reasoning and action system that leverages specialized tools bound to each agent.