Capstone: Building the 'Budget-First' Researcher

Congratulations! You have navigated the technical, architectural, and financial depths of Token Efficiency. You understand pruning, caching, multi-agent systems, and model economics. Now, it is time to build.

In this capstone module, you will build a "Budget-First" Autonomous Researcher.

The Mission:

Create an agent that can take a complex topic (e.g. "The history of quantum computing in Japan"), perform multi-step research, cite sources, and write a report—for less than $0.10.

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1. Project Specifications

Your researcher must implement at least 5 core techniques from the course:

1. Semantic Caching: (Module 5) To avoid redundant searches.
2. Multi-Model Routing: (Module 14) Using Small Models for search-log analysis and Expert Models for final writing.
3. Context Pruning: (Module 6) Dynamically stripping the "Reasoning Tail" from the history.
4. Structured JSON Output: (Module 13) To ensure zero conversational waste.
5. Termination Circuit Breaker: (Module 10) Stopping the agent if it is "Circling" without fresh signal.

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2. The Architectural Blueprint

graph TD
    U[User Query] --> R{Router}
    R -->|Cache Search| SC[(Semantic Cache)]
    SC -->|Hit| Report
    SC -->|Miss| A[Agent Loop]
    
    subgraph "The Efficient Loop"
        A --> B[Search Tool]
        B --> C[Prune & Extract Content]
        C --> D{Circuit Breaker}
        D -->|Continue| A
        D -->|Stop| E[Consolidate Facts]
    end
    
    E --> F[Expert Model Report]

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3. The Efficiency Log

During the build, you are required to maintain a "Token Log" showing the cost of every turn.

• You must be able to justify why you used an "Expert" model for any specific step.
• You must show the "Pruning Log" (How many tokens you deleted from the context window).

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4. Next Steps

In the next lesson, we will set up the Multi-Model Router using FastAPI. We will prepare our environment for the "Intelligence Tiering" strategy that is the foundation of our $0.10 target.

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Exercise: The Goal Setting

1. Spend 10 minutes researching the cost of a "Standard" search agent (like AutoGPT). (Hint: They often cost $2.00 - $5.00 per task).
2. State your target: "I will build an agent that completes the task for $0.05 without dropping below 90% accuracy."
3. Commit: Which 5 techniques from the course are your "High Priority" for this build?

• Record them. These will be your Evaluation Criteria.

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Good luck on your Capstone Project! The future of sustainable AI is in your hands.