Debate and consensus patterns help multiple AI agents share ideas and agree on the best answer. This makes decisions smarter and more reliable.
Debate and consensus patterns in Agentic AI
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debate_pattern(agents, question) -> final_answer consensus_pattern(agents, inputs) -> agreed_answer
debate_pattern runs agents to argue different answers and picks the best.
consensus_pattern combines agent inputs to find common agreement.
final_answer = debate_pattern([agent1, agent2, agent3], 'What is the best movie?')agreed_answer = consensus_pattern([agent1_output, agent2_output, agent3_output])
This code shows three agents giving opinions on pets. The debate pattern prints each opinion and picks the most popular. The consensus pattern finds the most common opinion from agent outputs.
class Agent: def __init__(self, name, opinion): self.name = name self.opinion = opinion def argue(self, question): return f'{self.name} says {self.opinion}' def debate_pattern(agents, question): print(f'Debate on: {question}') opinions = [agent.argue(question) for agent in agents] for op in opinions: print(op) # Simple rule: pick the opinion that appears most counts = {} for agent in agents: counts[agent.opinion] = counts.get(agent.opinion, 0) + 1 final = max(counts, key=counts.get) return final def consensus_pattern(agent_outputs): print('Consensus check on agent outputs:') for output in agent_outputs: print(output) # Find the most common output counts = {} for out in agent_outputs: counts[out] = counts.get(out, 0) + 1 agreed = max(counts, key=counts.get) return agreed # Create agents with opinions agent1 = Agent('Alice', 'Cats are best') agent2 = Agent('Bob', 'Dogs are best') agent3 = Agent('Carol', 'Cats are best') # Run debate final_answer = debate_pattern([agent1, agent2, agent3], 'Which pet is best?') print(f'Final answer from debate: {final_answer}') # Run consensus on outputs outputs = [agent1.opinion, agent2.opinion, agent3.opinion] agreed_answer = consensus_pattern(outputs) print(f'Agreed answer from consensus: {agreed_answer}')
Debate patterns help explore different views before deciding.
Consensus patterns focus on agreement, useful when agents mostly agree.
Both patterns improve AI reliability by using multiple opinions.
Debate and consensus patterns let multiple AI agents share and combine ideas.
Debate shows different opinions and picks the best one.
Consensus finds common agreement among agent answers.
Practice
Solution
Step 1: Understand debate pattern goal
Debate patterns involve agents sharing different opinions to explore ideas.Step 2: Identify the outcome of debate
The goal is to pick the best answer from these opinions, not just agree or random pick.Final Answer:
To show different opinions and select the best one -> Option AQuick Check:
Debate = select best opinion [OK]
- Confusing debate with consensus
- Thinking debate forces agreement
- Believing debate picks random answers
Solution
Step 1: Understand consensus pattern in code
Consensus means picking the most common answer among agents.Step 2: Identify code that finds most common answer
Using max with key=answers.count finds the answer with highest frequency.Final Answer:
consensus = max(set(answers), key=answers.count) -> Option DQuick Check:
Consensus = most common answer [OK]
- Using min or sum instead of frequency count
- Picking first answer without checking frequency
- Confusing consensus with random choice
agents = ['A', 'B', 'C']
opinions = {'A': 0.7, 'B': 0.9, 'C': 0.6}
best_agent = max(opinions, key=opinions.get)
print(best_agent)Solution
Step 1: Understand max with key function
max(opinions, key=opinions.get) finds key with highest value in opinions dictionary.Step 2: Identify highest opinion value
Values are 0.7 (A), 0.9 (B), 0.6 (C). Highest is 0.9 for B.Final Answer:
B -> Option BQuick Check:
Max opinion = B [OK]
- Picking agent with lowest value
- Confusing keys and values in max
- Expecting error due to dictionary usage
answers = ['yes', 'no', 'yes', 'maybe'] consensus = max(answers, key=answers.count) print(consensus)
Solution
Step 1: Analyze max with key=answers.count behavior
This finds the element with highest count, but if tie exists, it picks first max.Step 2: Check for ties in answers list
'yes' appears twice, 'no' and 'maybe' once each, so no tie here. But if tie existed, this method picks first max only.Final Answer:
It does not handle ties correctly -> Option AQuick Check:
Consensus tie handling = issue [OK]
- Thinking max can't use key argument
- Believing answers.count is invalid
- Assuming list is empty
Solution
Step 1: Understand debate pattern goal
Debate aims to compare opinions and pick the best based on confidence or quality.Step 2: Identify best approach for final rating
Choosing the rating from the agent with highest confidence aligns with debate selecting best opinion.Final Answer:
Select the rating from the agent with highest confidence -> Option CQuick Check:
Debate picks best confident opinion [OK]
- Averaging ratings (consensus, not debate)
- Picking lowest rating without reason
- Random selection ignoring confidence
