Technical Deep Dive: The "Hybrid Grounding" Architecture Unlike standard RAG apps, EcoCompute AI uses a Hybrid Grounding approach to ensure physics-compliant energy auditing.
- The "See-Search-Solve" Loop
See (Deterministic): We built a custom client-side Heuristic Scanner (TypeScript) that instantly maps code topology (ResNet blocks, Attention layers) using regex patterns. This gives Gemini "ground truth" context before it even starts thinking. Search (Dynamic): The agent uses Google Search to fetch real-time 2026 hardware specs (e.g., NVIDIA B200 TDP, Cloud Pricing). It doesn't hallucinate specs; it looks them up. Solve (Verifiable): We force the model to use a Python Sandbox for all FLOPs/Byte math.
- Taming Gemini 3 Pro We heavily customized the google/genai config to behave like a Senior Engineer:
Thinking Budget (1024 Tokens): Allocated specifically for the agent to plan its audit strategy and self-correct physics errors. Self-Correction Protocol: If the Python sandbox throws a ValueError (e.g., precision mismatch), the agent catches it, rewrites the code, and retries automatically.
- Infrastructure-First Design
CI/CD Simulator: We built a visual simulator to demonstrate how this acts as a "Blocking Gate" in GitHub Actions. Calibration Engine: The system is designed to ingest .nsys profiling logs to calibrate its energy model against ground truth.
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