Supervisor agent pattern in Agentic AI

Introduction

The supervisor agent pattern helps manage multiple AI agents by overseeing their tasks and making sure they work well together.

When you have several AI agents working on parts of a big problem and need to coordinate them.

When you want to check the quality of results from different agents before finalizing an answer.

When tasks are complex and require breaking down into smaller steps handled by different agents.

When you want to improve reliability by having a supervisor catch mistakes or conflicts.

When you want to combine strengths of different AI agents for better overall performance.

Syntax

Agentic AI

class SupervisorAgent:
    def __init__(self, agents):
        self.agents = agents

    def supervise(self, task):
        results = [agent.perform(task) for agent in self.agents]
        final_result = self.evaluate(results)
        return final_result

    def evaluate(self, results):
        # Combine or select best result
        pass

The supervisor agent holds references to other agents it manages.

It collects results from agents and decides the best or combined output.

Examples

A simple agent that returns a string result for a task.

Agentic AI

class Agent:
    def perform(self, task):
        return f"Result from {task}"

Supervisor collects results and picks one based on a simple rule.

Agentic AI

class SupervisorAgent:
    def __init__(self, agents):
        self.agents = agents

    def supervise(self, task):
        results = [agent.perform(task) for agent in self.agents]
        return max(results)  # picks the lexicographically largest result

Sample Model

This program shows two agents doing the same task. The supervisor collects their results and picks the longest one as the final answer.

Agentic AI

class Agent:
    def __init__(self, name):
        self.name = name

    def perform(self, task):
        return f"{self.name} completed {task}"

class SupervisorAgent:
    def __init__(self, agents):
        self.agents = agents

    def supervise(self, task):
        results = [agent.perform(task) for agent in self.agents]
        print("Agent results:")
        for r in results:
            print(r)
        final = self.evaluate(results)
        return final

    def evaluate(self, results):
        # For demo, pick the longest result string
        return max(results, key=len)

# Create agents
agent1 = Agent("AgentA")
agent2 = Agent("AgentB")

# Create supervisor
supervisor = SupervisorAgent([agent1, agent2])

# Run supervision
final_output = supervisor.supervise("task1")
print("Final chosen result:", final_output)

Important Notes

The supervisor agent pattern helps organize teamwork among AI agents.

Evaluation logic can be simple or complex depending on the problem.

This pattern improves reliability by checking multiple agent outputs.

Summary

Supervisor agent manages and coordinates multiple AI agents.

It collects and evaluates results to decide the best output.

Useful for complex tasks needing teamwork and quality control.