---
title: Parsimony
emoji: 🔥
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 5.13.0
app_file: app.py
pinned: false
license: cc-by-sa-4.0
short_description: an experiment in parsimony
---

## **Building Towards a Smarter Agentic AI**  
*The balance between simplicity and evolution in a rapidly advancing field.*

Developing agentic AI systems is a fascinating challenge, particularly when focusing on the delicate balance between **lean design** and **scalable evolution**. My recent experimentation with a prototype—powered by **Smolagents** and instrumented via **Phoenix/OpenTelemetry** — has reinforced some valuable principles about starting small and building incrementally.

This isn't a finished product; it’s a **work in progress**. But that’s where the real insights come from—learning to make purposeful decisions at each step while keeping future growth in mind.

---

### **The Current State: Minimalist by Design**  

The initial implementation was intentionally lean:
- **Interface**: A clean, Gradio-powered UI with domain-specific examples.
- **Instrumentation**: Basic monitoring using Phoenix/OpenTelemetry for telemetry insights.  
- **Framework**: Smolagents provided a lightweight, extensible base to explore agentic capabilities.  

This minimalist foundation allowed for:

✅ Establishing a clear performance baseline.  
✅ Reducing dependency complexity to focus on core functionality.  
❌ Acknowledging gaps in domain-specific biomedical context.  
❌ Recognizing the absence of specialized data connectors (e.g., BioGRID or PubMed integration).  

---

### **Strategic Evolution: From Foundation to Functionality**

With the baseline established, the next phase focuses on layering **biomedical context** and **domain-specific capabilities** into the system, guided by a phased and deliberate approach:

**Key Milestones in the Evolution Pathway**:  

```mermaid  
graph TD  
    A[Baseline] --> B[Add Biomedical NLP Layer]  
    B --> C[Integrate API Gateways]  
    C --> D[Build Validation Pipelines]  
    D --> E[Develop Custom Tools]  
```  

1. **Domain-Specific Models**: Switch to specialized models like `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract` for improved contextual understanding.  
   - *Impact*: Enhanced language processing tailored to biomedical QA tasks.  
2. **Preprocessing Pipelines**: Add **scispacy** and **en_core_sci_lg** for named entity recognition (NER) and text preprocessing.  
   - *Impact*: Improved ability to identify biomedical entities and relationships in unstructured text.  
3. **Critical Libraries**: Introduce **bioservices**, **PyBioMed**, and **NetworkX** for API access, molecular analysis, and interaction networks.  
   - *Impact*: Enable integration with BioGRID, STRING, and other key data sources.  
4. **Caching for Efficiency**: Implement tools like `diskcache` to optimize API calls and ensure faster response times.  
   - *Impact*: Reduced latency and cost.  

---

### **Key Drivers for Lean Evolution**

This approach embodies the principles of lean design:
- **Start with What’s Necessary**: Focus on baseline performance before scaling complexity.
- **Iterate Responsibly**: Introduce new capabilities (e.g., biomedical NLP or validation pipelines) only when they add measurable value.  
- **Optimize for Flexibility**: Leverage OpenSource tools like **Smolagents** and **Phoenix** to experiment and adapt quickly.  

---

### **Insights from the Journey**

Here’s what this process has taught me:
1. **Simplicity is a Strength**: A lean start lets you identify what works without the noise of unnecessary features.  
2. **Feedback Is Essential**: Tools like Phoenix help monitor system performance, guiding refinements with real-world data.  
3. **Build for Impact, Not Features**: Every addition should serve the end user, whether it’s a researcher validating hypotheses or a clinician seeking actionable insights.  

---

### **Acknowledging OpenSource Inspiration**  

None of this would be possible without the incredible efforts of the **OpenSource community**. Platforms like **Hugging Face** and telemetry tools like **Arize Phoenix** empower developers to build impactful, scalable systems without reinventing the wheel. Their contributions serve as a reminder that innovation grows through collaboration.