prioritization

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.

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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