Prioritization

Prioritization allows an agent to work smarter, not just harder. Instead of processing tasks First-In-First-Out (FIFO), a "Manager Agent" analyzes each request's urgency and business value. It assigns a priority score (P0, P1, P2) and reorders the queue effectively. This is vital for resource-constrained environments.

When to Use

• Queue Management: When the system receives more requests than it can handle instantly.

• SLA Enforcement: Ensuring premium users or critical alerts get processed first.

• Resource Allocation: Assigning the smartest (and most expensive) models to P0 tasks, and cheaper models to P2 tasks.

• Triage: Filtering out spam or low-value requests entirely.

Use Cases

• Ticket Triage: Analyzing support tickets and tagging them as "Critical" (Server Down) or "Low" (Typo).

• Inbox Management: Sorting emails by "Needs Reply", "Read Later", and "Spam".

• Agent Dispatch: A Project Manager agent assigning urgent bugs to Senior Dev Agents and documentation tasks to Junior Agents.

Implementation Pattern

def prioritization_loop(task_queue): while True: # Step 1: Ingest new_request = ingest_request()

    # Step 2: Assess Priority
    # Manager agent determines importance
    priority_score = manager_agent.evaluate(
        prompt="Rate urgency 1-10",
        input=new_request
    )
    
    # Step 3: Insert into Priority Queue
    task_queue.push(new_request, priority=priority_score)
    
    # Step 4: Process Highest Priority
    next_task = task_queue.pop()
    worker_agent.run(next_task)

Examples

Input: "I have 12 tasks to do today. Help me prioritize."

RICE scoring output:

Task Reach Impact Confidence Effort RICE Score

Fix login bug 5000 3 90% 1 13,500

Add dark mode 800 2 70% 5 224

Write docs 200 1 80% 2 80

Recommendation: Fix the login bug first — 60× higher RICE score than the next item.

Input: "We have 30 backlog items for this sprint. What goes in?"

Output: MoSCoW matrix with Must/Should/Could/Won't labels, sprint capacity check, and a risk-adjusted ordered list.

Troubleshooting

Problem Cause Fix

All items score similarly Inputs too vague Ask for specific numbers (user count, revenue impact) before scoring

Stakeholders reject prioritization No buy-in on criteria Surface the scoring rubric before running; get alignment on weights

High-priority item blocked Dependency not captured Add a dependency pre-check step before finalizing the ordered list

Scores feel arbitrary Missing confidence calibration Require a confidence percentage for each estimate