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:
graph TD
A[Baseline] --> B[Add Biomedical NLP Layer]
B --> C[Integrate API Gateways]
C --> D[Build Validation Pipelines]
D --> E[Develop Custom Tools]
- Domain-Specific Models: Switch to specialized models like
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstractfor improved contextual understanding.- Impact: Enhanced language processing tailored to biomedical QA tasks.
- 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.
- 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.
- Caching for Efficiency: Implement tools like
diskcacheto 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:
- Simplicity is a Strength: A lean start lets you identify what works without the noise of unnecessary features.
- Feedback Is Essential: Tools like Phoenix help monitor system performance, guiding refinements with real-world data.
- 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.