Self-Improving Organizations#

Will Brown (Prime Intellect, Applied Research)

• Why: A credible roadmap for the "ML for operations" direction: turning an organization's agent traces, corrections, and approval decisions into training signal — with a deliberately bounded starting point (evals and routing) rather than the usual "train on all our data" hand-waving.
• What: The argument that agents currently learn only in-context (skill files, re-reading the same instructions, cache discounts that don't eliminate reprocessing), while institutional knowledge that's hard to write as rules — preferences, friction, situational judgment — is better expressed as examples and baked into weights. The proposed sequence: collect traces from production use; curate them into evals (agents can do much of the curation); start with routing — run big and small models on the same task, and when the small model's output evaluates as good enough, that comparison becomes labeled data for routing similar work to it; expose human decisions through purpose-built interfaces (a decision card asking promote/hold/inspect on a model checkpoint, with the evidence attached) so each answer is a clean labeled target rather than something inferred from a long thread; then, only then, invest in training — with a cited example that a five-person team trained a competitive knowledge-work model for ~$300K, an "employee-scale" investment.
• How: Nightly loop prototype: serve agents on inference capacity during the day, review flagged cases and run training at night when the same capacity is idle; checkpoints scored against every evaluation suite they've seen (shown as an internal prototype). Tooling is Prime Intellect's open-source Verifiers environment/eval library; decision models like Jev are referenced as the pattern for the routing/approval layer. The contrast case is the long expert-network-to-frontier-lab pipeline, where the person who knows the job never supervises the model.
• Where applicable: The routing and eval-first starting points are broadly implementable with existing open tooling; the "earn your way into the weights" caution is sound. The $300K training figure is one cited example, not a general budget. Interfaces and nightly loops are experimental.