If agent proliferation turns out to cause problems, we can monitor and possibly disincentivize it. If an HR manager watched the sorcerer's apprentice scene from fantasia, they might draft a policy document similar to the below (which was generated by GPT-4). If our agents read and understood this policy, they might refrain from spam-spawning.
Prior to deployment, we might test the policy in a sandbox test environment populated with some representative LLM agents.
Obviously it is not going to cover all cases, and we may still run into problems, in which case we should tweak the policy accordingly. Over time we might expect the policy to become more robust.
Policy Title: Agent Creation and Proliferation Control Policy
I. Purpose:
This policy aims to ensure that AI agents within our multi-agent system do not engage in uncontrolled spawning or creation of additional agents, similar to the scenario in the "Sorcerer's Apprentice." By implementing this policy, we hope to maintain system stability, resource integrity, and aligned agent behavior.
II. Scope:
This policy applies to all AI agents within the multi-agent system and any subsystem or environment where agent interaction and behavior occur.
III. Policy:
Restricted Agent Creation Capability:
Only designated primary agents have the capability to spawn or create secondary agents.
The capability to create additional agents shall be restricted by specific control algorithms or permissions that are centrally managed.
Limit on Creation:
Any agent with creation capability is restricted to spawning a set number of agents within a given timeframe. This limit must be pre-defined and cannot be altered by the agent itself.
Resource Monitoring:
Continuously monitor system resources. If an unexpected consumption or drain on resources is detected, the system will initiate an audit to identify any unauthorized agent creation. This will trigger corrective measures.
Alignment and Reward Mechanism:
Agents will be rewarded based on their alignment with system goals and objectives. Any agent found creating other agents without explicit permission or beyond set limits will be penalized.
Rewards and penalties will be implemented via a mechanism that the agents value, such as computational resources, priority processing time, or increased decision-making capacity.
Audit and Reporting:
Regular audits will be conducted to ensure agents are behaving within the set parameters.
Any agent found to be in violation of this policy will be flagged, and corrective measures, including possible deactivation, will be taken.
Transparency and Communication:
Agents with creation capabilities must report any creation activity to a central management system immediately.
Failure to report or any delay in reporting will be considered a policy violation.
Decentralized Watchdogs:
Deploy "watchdog" agents whose primary function is to monitor and report any unauthorized agent creation activities.
These watchdog agents will have a higher level of system access and permissions to ensure they can effectively monitor all agents and their activities.
Training and Initialization:
All agents, upon initialization, will undergo a training phase where the importance and reasons for controlled agent creation are emphasized. This ensures that agents start with an understanding of the system's goals regarding proliferation.
IV. Policy Violations:
Any agent found to be in violation of this policy will face corrective measures, which may include re-training, re-programming, resource restrictions, or, in severe cases, deactivation.
V. Review and Updates:
This policy will be reviewed annually or as deemed necessary by system administrators to ensure it remains effective and aligned with the overall goals of the multi-agent system.
By implementing this policy, we aim to avoid scenarios reminiscent of the "Sorcerer's Apprentice" and ensure that our multi-agent system operates efficiently, safely, and in alignment with our objectives.
What about agents that write agents?
“Bot, do such-and-such.”
Don’t know how to do that. Write some agent code. Compile. Add it to the chain.
If agent proliferation turns out to cause problems, we can monitor and possibly disincentivize it. If an HR manager watched the sorcerer's apprentice scene from fantasia, they might draft a policy document similar to the below (which was generated by GPT-4). If our agents read and understood this policy, they might refrain from spam-spawning.
Prior to deployment, we might test the policy in a sandbox test environment populated with some representative LLM agents.
Obviously it is not going to cover all cases, and we may still run into problems, in which case we should tweak the policy accordingly. Over time we might expect the policy to become more robust.
Policy Title: Agent Creation and Proliferation Control Policy
I. Purpose:
This policy aims to ensure that AI agents within our multi-agent system do not engage in uncontrolled spawning or creation of additional agents, similar to the scenario in the "Sorcerer's Apprentice." By implementing this policy, we hope to maintain system stability, resource integrity, and aligned agent behavior.
II. Scope:
This policy applies to all AI agents within the multi-agent system and any subsystem or environment where agent interaction and behavior occur.
III. Policy:
Restricted Agent Creation Capability:
Only designated primary agents have the capability to spawn or create secondary agents.
The capability to create additional agents shall be restricted by specific control algorithms or permissions that are centrally managed.
Limit on Creation:
Any agent with creation capability is restricted to spawning a set number of agents within a given timeframe. This limit must be pre-defined and cannot be altered by the agent itself.
Resource Monitoring:
Continuously monitor system resources. If an unexpected consumption or drain on resources is detected, the system will initiate an audit to identify any unauthorized agent creation. This will trigger corrective measures.
Alignment and Reward Mechanism:
Agents will be rewarded based on their alignment with system goals and objectives. Any agent found creating other agents without explicit permission or beyond set limits will be penalized.
Rewards and penalties will be implemented via a mechanism that the agents value, such as computational resources, priority processing time, or increased decision-making capacity.
Audit and Reporting:
Regular audits will be conducted to ensure agents are behaving within the set parameters.
Any agent found to be in violation of this policy will be flagged, and corrective measures, including possible deactivation, will be taken.
Transparency and Communication:
Agents with creation capabilities must report any creation activity to a central management system immediately.
Failure to report or any delay in reporting will be considered a policy violation.
Decentralized Watchdogs:
Deploy "watchdog" agents whose primary function is to monitor and report any unauthorized agent creation activities.
These watchdog agents will have a higher level of system access and permissions to ensure they can effectively monitor all agents and their activities.
Training and Initialization:
All agents, upon initialization, will undergo a training phase where the importance and reasons for controlled agent creation are emphasized. This ensures that agents start with an understanding of the system's goals regarding proliferation.
IV. Policy Violations:
Any agent found to be in violation of this policy will face corrective measures, which may include re-training, re-programming, resource restrictions, or, in severe cases, deactivation.
V. Review and Updates:
This policy will be reviewed annually or as deemed necessary by system administrators to ensure it remains effective and aligned with the overall goals of the multi-agent system.
By implementing this policy, we aim to avoid scenarios reminiscent of the "Sorcerer's Apprentice" and ensure that our multi-agent system operates efficiently, safely, and in alignment with our objectives.