# The AI Preflight Check > Source: > Published: 2026-07-08 00:00:00+00:00 I still remember when my agent would forget what I said mid-sentence. Context size is not the ceiling. Memory architecture is. I have been experimenting with a memory architecture that runs preflight instructions. A pilot plans the route before takeoff. My agent does the same. A query lands. “Summarize the Q3 board deck.” 200,000 raw tokens of emails, PDFs, & chats sit behind that sentence. Preflight is retrieval. The agent inspects its skills library 1, picks the ones relevant to the task, & loads only those into the context window. Skills are consolidated memory ; the preflight step is how the agent picks the right one. The local Ornith 35B model 2 then executes on that loaded context. Hard tasks route out to the frontier ; routine tasks remain on the local model, which happens about 80% of the time. The watchdog monitors which skills are loaded, which decisions are made, & the success rate. Every preflight decision is logged. Every skill invocation is a named, versioned artifact. Overnight, asynchronous inference 3 processes the day’s trail. It decides which new skills should be developed, & which parts of existing skills should become deterministic code. Calendar scheduling is a good example : an LLM should not be comparing free & busy slots ; Rust is much better at that. The system rewrites its skills library & restarts itself in a self-improving loop. Yesterday was the first day the watchdog did not suggest any improvements. I doubt it will continue. But it hints at something : at some level of improvement, the system reaches a plateau. Only genuinely new exceptions need human help. - The skills library is a set of workflow files (~90 at present) indexed on-disk & retrieved by intent match. Skills are workflows written once, versioned, & handed to the model as tool schemas. See [Skill Distillation](https://tomtunguz.com/the-pi-agent-skill-distillation/)for how the library was built.[↩︎](#fnref:1) - Ornith 35B is a locally-hosted open-weight model in the 35-billion-parameter class, run on Apple Silicon via [Ollama](https://ollama.com). It handles routine agent work — classification, drafting, tool selection, structured extraction — & routes the hard remainder to the frontier.[↩︎](#fnref:2) - See [Full Sail on Asynchronous Inference](https://tomtunguz.com/sail-inference-queue/)for the queue architecture that makes overnight, hours-long agent runs tractable.[↩︎](#fnref:3)