Runtime where AI work matures.
Agents are leaving the prototype — expensive, brittle, and unauditable. Forkable is the substrate underneath: every run traced, reinforced behavior crystallized into deterministic code, code thawed back the moment something changes. Nothing is lost. Everything is on the record.
Tracing as memory
Every run produces a structured trace — tool calls, intermediate outputs, retries, costs. Your RL pipelines, caches, replays, and evals read from it. Traces aren’t logs. They’re how your agents learn.
Workflows that crystallize
Workflows start as model paths — slow, expensive, expressive. When behavior stabilizes, Forkable surfaces candidates to crystallize: deterministic code paths, faster and cheaper. When the task changes, the code path thaws back. Every version recoverable. Every transition logged.
Why now
Two patterns are dead ends: every step as a model call forever is too expensive, rewriting as pure code is too slow. The loop is the resolution: write fast, run cheap, prove safe, change easy — with an audit trail finance, legal, and compliance can sign.
Forkable is in private design partnership with a small number of teams running agents in production today.
forkable@arhmjn.com →