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 |