Setting up AI agents in Paperclip (assigning goals, roles, budgets)
Agent configuration is the most labor-intensive part of Paperclip implementation. Each agent's purpose, role, tools, budget, and behavioral guidelines directly determine the quality of the entire AI team's results.
Agent configuration parameters
Role & Goal (System Prompt): A detailed description of the role, specialization, and area of responsibility. Examples of correct and incorrect behavior. Tone of voice. What to do when faced with uncertainty.
Tools: List of tools the agent works with. Research agent: web_search, read_url, save_to_knowledge_base. Writer agent: read_brief, generate_text, save_draft. Publisher agent: format_content, publish_to_cms, notify_slack.
Budget:
-
max_tokens_per_task: LLM spending limit -
max_wall_time: task execution timeout -
max_tool_calls: limit the number of actions -
max_cost_usd: monetary limit
Escalation Rules: Conditions for escalating a task: budget overrun, uncertainty, tool error, human confirmation required.
Memory Configuration: Agent memory type: in-context (within the task), persistent (vector storage for long-term memory), shared (available to other agents in the same team).
Configuration iterations
Initial configurations are never perfect. Three to five iterations are needed based on real test tasks. We analyze task execution traces, identify where the agent made incorrect decisions, and adjust the configuration.