Search email the way an agent would
Query to ranked threads with match attribution, then act—draft, forward, or Ask—without treating search as a branded launch name.
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Human email search is a box that returns a chronological pile. Agents need a different loop: interpret the goal, retrieve candidate threads, rank them, explain why each matched, and propose an action. That capability is search built for agents—not a trademarked product launch, just the way a role should work when mail is a tool rather than a dump of tokens in the prompt.
Query → ranked threads → attribution
A role on Neuro OS receives a task: “Find the last carrier dispute on lane X and draft a status reply.” It queries Agent Inbox across the mailboxes it is allowed to read. Results come back as ranked threads with match attribution: subject hit, attachment filename, participant, date window, or semantic similarity to a prior case. The agent shows its working. A naked “top result” without why is how wrong threads get answered with confident tone.
Keep retrieval scoped. A finance role should not search legal’s mailbox. Policy is part of search quality, not an afterthought bolted on after a demo. If the query needs broader access, require an explicit escalation rather than silently widening scope.
Act with Ask
From a ranked result, the useful actions are draft reply, forward to a human, label, or link the thread into project memory. Sending defaults to Ask. Forwarding to a person is often the correct outcome when the match is uncertain or when the thread names a relationship owner. Allow only for narrow, reversible actions after evaluation on real traffic.
This turns search into operations. The agent does not stop at a list; it prepares the next governed step with the evidence still attached.
Evaluation beats demos
Measure precision at k, time-to-correct-thread, Ask reject rate on actions after search, and how often humans re-ran the query with different wording. Include adversarial cases: similar customer names, forwarded chains, threads with misleading subjects, and attachments whose display names do not match contents. Update skills when attribution patterns fail instead of adding more prompt adjectives.
Implementation notes
Index message metadata and carefully scoped body text according to retention policy. Sanitize attachment names before they become retrieval keys. Prefer thread-level ranking over isolated messages so replies keep context. Log the query, the ranked ids, the attribution features, and the action taken for incident review. Avoid stuffing unbounded mail into the model context as a substitute for retrieval; that pattern is expensive and hard to audit.
Agents already “search” in demos by stuffing recent mail into a prompt. Production search is narrower, attributed, and tied to Ask. Build that loop once and every role that touches email gets faster without getting reckless.
Prompt stuffing is not a search strategy
Dumping the last two hundred messages into context feels like search until cost, latency, and accidental disclosure show up. Retrieval with attribution scales; stuffing does not. Prefer ranked thread ids plus short excerpts, then load full bodies only for the threads the agent actually acts on under Ask. That discipline keeps search cheap enough to run on every task.
Agent Inbox gives each role a mailbox people can reply to, with forwarding into Neuro OS when a human must see the thread. Outbound mail defaults to Ask. Run the role on Neuro OS. To scope the first inbox, get started.