Bryce Vernon shows a more complex agent system for prioritizing feature requests - acting as the first line of defense to evaluate new requests against company strategy. The system works through this flow: customers submit feature requests via form → stored in Zapier table → prioritization agent evaluates → updates priority with justification. Here’s how Bryce built the prioritization agent: First, set up data sources. The agent needs access to two things: your feature request table (to check how frequently something’s been requested) and your strategy document (could be a Google Doc, Notion page, or other data source). These sync automatically so the agent always has current information. Second, write the agent’s instructions. Bryce tells it to: “Review the new request, check how many times it’s been requested in the feature request table, check if it aligns with our strategy document, then decide on priority level.” Third - and this is crucial - give the agent a decision framework. Bryce emphasizes: “Letting your agents make a decision is where real power comes from. There’s also risk, but giving it a framework I found has been super helpful.” His simple framework defines what “high,” “medium,” and “low” priority mean. Bryce notes: “Just like an employee on your team, if you’re going to delegate a task, giving them some decision frameworks to make a decision not only helps them make the decision, but helps you understand why they made the decision, which is critical for delegation.” Fourth, tell it to update the record with the priority ranking and brief justification. When Bryce demonstrates with a “customer portal” request, the agent searches the table for related requests, checks the strategy document, analyzes alignment, and decides: “It’s only been requested this one time, could be low priority. However, it strongly aligns with the goals, so we’re going to elevate its importance. We’re going to make it a medium priority.” The system acts as a first line of defense. From here, high-priority items could automatically flow to Jira or wherever your team works. ➡️ Give agents decision frameworks just like you would a human employee. Clear criteria for “high/medium/low” helps them make consistent decisions and helps you understand their reasoning.