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The Supervisor agent pattern helps manage multiple smaller agents by overseeing their tasks and combining their results. It acts like a team leader who checks progress and guides agents to work better together.
Loss
0.5 |****
0.4 |***
0.3 |**
0.2 |*
0.1 |
1 2 3 4 5 Epochs| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 0.45 | 0.6 | Initial coordination between agents is rough, moderate accuracy |
| 2 | 0.3 | 0.75 | Supervisor improves task splitting and aggregation |
| 3 | 0.2 | 0.85 | Agents better handle subtasks, supervisor refines results |
| 4 | 0.15 | 0.9 | Stable improvement, supervisor effectively manages agents |
| 5 | 0.12 | 0.92 | Converged with high accuracy and low loss |
Supervisor agent in the supervisor agent pattern?collect_results fits.class Agent:
def __init__(self, name, score):
self.name = name
self.score = score
class Supervisor:
def __init__(self, agents):
self.agents = agents
def best_agent(self):
return max(self.agents, key=lambda a: a.score).name
agents = [Agent('A1', 85), Agent('A2', 90), Agent('A3', 88)]
supervisor = Supervisor(agents)
print(supervisor.best_agent())best_agent method returns the name of the agent with the max score, which is A2 with 90.class Supervisor:
def __init__(self, agents):
self.agents = agents
def best_score(self):
return max(self.agents, key=lambda a: a.score)
agents = [{'name': 'A1', 'score': 80}, {'name': 'A2', 'score': 95}]
supervisor = Supervisor(agents)
print(supervisor.best_score())best_score method expects agents with attribute score, but agents are dictionaries, not objects.a.score on a dictionary causes an error; dictionaries need a['score'].