What happened, what was proven, and what to do next: Avorelo Work Intelligence
Most AI coding sessions end with a fuzzy handoff. Something changed, something was tested, something is still uncertain, and the next session has to reconstruct the truth. Work Intelligence is Avorelo's local-first answer to that gap.
The problem is not only output
When an AI session ends, the hardest question is usually not "did it write code." The harder question is "what state is this work actually in." Was it proved, blocked, partially prepared, or left open? Which references mattered? Which ones were stale? What should the next session know so it does not start from zero?
Without that layer, teams fall back to rediscovery. They re-open files, re-read old threads, repeat setup, and overclaim progress because the proof and the memory are scattered.
What the layer actually answers
Avorelo's Work Intelligence layer is a local summary built after the session from existing receipts, proof, continuity, and routing artifacts. It answers the questions that matter after the run:
- What was the work?
- What changed or was attempted?
- What evidence exists?
- What remains open?
- What should the next session know?
- What context looked stale, noisy, or wasteful?
- What should not be claimed?
Why this is not another dashboard
This is not a surveillance layer and not generic AI analytics. It does not try to turn every session into management telemetry. It is closer to memory and proof around the work itself: a compact local explanation for the person who just ran the session and the person or agent who picks it up next.
The important shift is from "what did the AI do" to "what state is this work in." That is the information future sessions need, and it is the information most AI workflows fail to preserve cleanly.
What stays local
Work Intelligence stores safe metadata only. No raw prompts, source dumps, diffs, terminal output, env values, or secrets. If aggregate telemetry is enabled, the events are counts and statuses, not raw session content.
What it helps reduce
The most practical value is not hype. It is fewer repeated setup steps, fewer unsupported "done" claims, less stale context carried forward, and clearer next action after a blocked or unfinished session.
What it does not claim
It does not claim exact ROI, exact token savings, or that every session ends in proof. Open and blocked states are part of the product, not edge cases hidden to make the story cleaner.