The Advanced Supervisor: Cost-Aware Routing

In simpler systems, a "Supervisor" just routes tasks. In a production-grade fleet, a Supervisor is a Budget Manager.

The advanced supervisor doesn't just ask "Who should do this?"; it asks:

1. "How complex is this task?"
2. "Can a cheap model (GPT-4o mini) do it, or do I need the 'Expert' (Claude 3.5 Sonnet)?"
3. "How many tokens are left in this session's budget?"

In this lesson, we learn Cost-Aware Routing. We’ll build a supervisor that optimizes for both Intelligence and Economy.

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1. The "Intelligence Tiering" Strategy

Not every task requires a $20/month model.

• Tier 1 (Routine): Data formatting, simple extraction, greetings.
• Tier 2 (Analytical): RAG synthesis, summarization, simple logic.
• Tier 3 (Expert): Coding, complex legal reasoning, creative problem-solving.

The Supervisor's Job: Assign the Lowest Possible Tier that can successfully complete the task.

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2. Decision Logic: The "Cheap-First" Pattern

A token-efficient supervisor follows the "Try Cheap, Escalate if Confused" pattern.

1. Step 1: Send the task to a Tier 1 Agent (Cheap).
2. Step 2: The Tier 1 agent includes a "Confidence Score" in its output.
3. Step 3: If Score < 0.8, the Supervisor Escalates to a Tier 3 Agent.

graph TD
    U[User Query] --> S[Supervisor]
    S -->|Simple| L1[Cheap Agent $0.001]
    S -->|Complex| L2[Expert Agent $0.10]
    
    L1 -->|Confidence Low| S
    S -->|Escalate| L2

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3. Implementation: The Cost-Aware Router (Python)

Python Code: Model-Toggling Supervisor

def supervisor_router(task_complexity):
    # We maintain a mapping of Tiers to Models
    TIERS = {
        "LOW": "gpt-4o-mini",
        "MEDIUM": "gpt-4o",
        "HIGH": "claude-3-5-sonnet"
    }
    
    # Logic to evaluate complexity (often using a 1-turn cheap prompt)
    complexity = evaluate_task_complexity(task_complexity) # Returns 'LOW' or 'HIGH'
    
    return TIERS[complexity]

@app.post("/agent-task")
async def handle_task(data):
    model = supervisor_router(data['input'])
    # Call the fleet member with the SELECTED model
    pass

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4. Budget-Aware Handoffs

The supervisor should track the Cumulative Cost. If the session is nearing its limit, the supervisor should force all specialists into "Aggressive Conciseness" mode.

The "Low-Power" Instruction:

"Alert: Session budget > 80%. All agent responses must be < 20 tokens. Use high-density shorthand only."

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5. Token ROI: The Tiered Savings

In a system that processes 1,000 tasks:

• Baseline (All Tier 3): $100.00.
• Cost-Aware (Hybrid): $12.00.
• Savings: 88%.

By move the "Routine" tasks to cheaper models, you make your AI system mathematically sustainable at scale.

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6. Summary and Key Takeaways

1. Model Matching: Use your best models only when strictly necessary.
2. Confidence-Based Escalation: Use cheap models as the "Front Line" and escalate on failure.
3. Budget Throttle: Change agent behavior based on remaining token limits.
4. Cheap Oversight: The supervisor itself should always be a high-speed, cheap model.

In the next lesson, Communication Protocols for Efficiency, we look at چگونه agents should "Talk" to each other to save tokens.

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Exercise: The Intelligence Sorter

1. Take 5 tasks:
   • A: Find the date of the next meeting.
   • B: Write a full Python library for data processing.
   • C: Summarize a 1-page email.
   • D: Deduplicate a list of 100 names.
   • E: Explain the meaning of life.
2. Assign a Tier (Low, Medium, or Expert) to each.
3. Calculate the cost if you used GPT-4o for all vs. a Tiered approach.

• Most students find the "Tiered" approach is 5x cheaper on average.

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Congratulations on completing Module 12 Lesson 2! You are now a budget-aware supervisor.