Writing about what it actually takes to make LLMs and agents work in production.
Teams are making critical model selection decisions based on benchmarks designed for someone else's problems. At sufficient scale, a fine-tuned 8B open-source model can match GPT-5.4 nano's quality on your specific task — at up to 10x lower cost. The only way to know is to benchmark on your own data.
Most teams only optimize the prompt. An agentic system has retrieval logic, tool definitions, a judge, and business rules that all affect output quality. Here's the honest landscape of approaches — and a concrete pattern using a meta-agent judge that gets you closer to optimizing the whole stack.
At scale, the real decision isn't which frontier model to use — it's whether a fine-tuned 8B open-source model can match GPT-5.4 nano on your specific task. Comparing Llama 3.1 8B, DeepSeek-R1-Distill, and Qwen3-8B on report summarization.