Stop Guessing: How to Build an LLM Recommender for Agentic AI into Any Web App
When building agentic AI features into a web app, one of the most common pitfalls is hardcoding a single heavyweight LLM for every single operation. Using a frontier reasoning model just to classify user intent or forma
When building agentic AI features into a web app, one of the most common pitfalls is hardcoding a single heavyweight LLM for every single operation.
Using a frontier reasoning model just to classify user intent or format JSON wastes both latency and budgetβwhile using a lightweight model for complex multi-step planning leads to brittle agent loops.
What This Guide Covers:
- Task Classification: Scoring incoming agent steps by reasoning depth, context size, and tool-use complexity
- Scoring Matrices: Balancing latency budgets, per-token costs, and benchmark accuracy
- Recommender Architecture: Building a clean model-selection layer that plugs into any frontend or backend stack
- Fallback & Telemetry Loops: Handling rate limits gracefully and adjusting recommendations using real-world execution metrics
Read the complete implementation guide on SunnyWriteUps.
Originally published by Dev.to WebDev. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.