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 format JSON wastes both latency and budget—while using a lightweight model for complex multi-step planning leads to brittle agent loops.

What This Guide Covers:

1. Task Classification: Scoring incoming agent steps by reasoning depth, context size, and tool-use complexity
2. Scoring Matrices: Balancing latency budgets, per-token costs, and benchmark accuracy
3. Recommender Architecture: Building a clean model-selection layer that plugs into any frontend or backend stack
4. Fallback & Telemetry Loops: Handling rate limits gracefully and adjusting recommendations using real-world execution metrics

Read the complete implementation guide on SunnyWriteUps.