Ismael Faro and I have been working on a solution to the inefficiency of agentic LLMs. Instead of having the LLM reason through every repetitive task, we've built an architecture where the agent acts as a Just-in-Time (JIT) compiler. The "Orchestrator" (a fine-tuned Qwen2.5-Coder-1.5B) plans a task and generates a high-level concept for a new tool. A specialized LoRA (also on Qwen2.5) then translates that concept into executable Python code, which is cached and run locally at high speed. We ran a benchmark comparing this "Agent Forge" approach to a standard "Pure LLM" agent. After a one-time compilation cost, the Agent Forge was nearly 2x faster and 40% cheaper in token usage on subsequent, similar tasks. The entire experiment, from fine-tuning to the final benchmark, is reproducible in a single Google Colab notebook. We'd love to get your feedback on the architecture. Runnable Colab Notebook: https://github.com/EvolvingAgentsLabs/agent-forge/blob/main/...