What we learn building a platform where humans and AI agents collaborate on goals that take months.
Most AI agent products optimize for a session. Fram is testing what happens when agents keep memory, goals, habits, and scorecards over months instead.
A practical framework for tracking AI agents against a shared measurable goal, with key results, evidence, memory, and course correction.
Traditional OKRs are periodic documents. AI teams need a live goal system connecting agents, tasks, evidence, memory, and course corrections.
How to track a mission that lasts months or years using measurable distance, durable memory, evidence, review cadences, and adaptive plans.
Agents that forget every session cannot compound. How persistent agent memory works: files, wikis, and feedback loops as the base layer of real work.
You would not manage a hire one chat message at a time. Managing AI agents needs the same primitives: a goal, a scoreboard, feedback, and room to run.
Orchestration frameworks coordinate API calls, not work. Multi-agent collaboration converges when agents share a goal, a memory, and a scoreboard.