The setting
makes productivity software for email-heavy workers and teams. is an AI assistant that takes on tasks across the browser, desktop and Superhuman apps, working from goals written in plain language.
Cheaper routing is completing workflows without always completing the work.
The orchestration path must protect reliable follow-through while keeping frequent agent work affordable.
Who you’d be doing this for
“The candidate got the thank-you note, then my hiring manager asked why the next step never got logged.”
Hillevi Holmberg · Recruiting Coordinator · a character in this brief
Uses Go to turn interview correspondence and notes into candidate follow-ups and internal tasks.
What is at stake
Recruiting follow-ups finish with required task updates missing. You must commit to a routing policy before the next pricing review locks in its cost and reliability tradeoff.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- completion integrity vs. model cost
- human-reviewed evidence vs. rapid commitment
- broad agent capability vs. predictable follow-through
- paid-plan economics vs. enterprise trust
Why Superhuman
Superhuman Go relies on cross-application follow-through, where a missing task update can undo an otherwise useful client interaction.
Written with these in mind
Not your kind of problem? 36 more at Superhuman, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Superhuman. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Machine Learning Engineer (opens in a new tab)
- The agent also takes actions for users, and you'll widen what it can do while keeping the user in control.
- You'll keep long conversations useful, and decide what the agent should remember about a user.
- Senior Design Technologist, AI Tooling (opens in a new tab)
- You will help ensure our AI-native platform (spanning email, calendars, docs, and agents) is supported by scalable systems that maintain our high-quality standards.
- AI Engineer (opens in a new tab)
- As Superhuman Docs’ collaborative workspaces evolve so users can solve arbitrarily complex tasks with agents, rigorous measurement will be what lets us ship with confidence.
- We’re looking for a foundational Machine Learning Engineer to join our team in Porto and build the evaluation foundation behind the next generation of AI-native productivity at Superhuman, specifically our agent platform, where multiple AI agents collaborate to solve complex user problems.
- Software Engineer, Full-Stack (opens in a new tab)
- Build a Groundbreaking AI Agent Platform: Spearhead the development of a new AI platform inside the Superhuman Docs product of the Superhuman suite where multiple AI agents collaborate to solve complex user problems.
- The ideal candidate is deeply excited about what's possible with LLMs today, has experience building with agents and AI workflows, and brings strong product sense alongside their engineering skills.
Reporting read before this brief was written
The pieces closest to the situation in this brief.
In a September 2, 2026 engineering post, Superhuman described managing multiple execution contexts and threads for every task because Chrome, macOS and Windows share no common technology.
How We Built Superhuman Go to Work Across Platforms (opens in a new tab) · blog.superhuman.com · Sep 2, 2026
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
Similar problems at other organizations
Like this one, each of these turns on committing to a deal before it checks out.
Only 68% of high-acuity members reach an appropriate first Specialty Care session within 14 days.
You must weigh faster access against the clinical confidence required to route vulnerable members safely.
The same setting, from another role
Each is set around Superhuman’s Superhuman Go, written for a different role. They’re separate briefs, not the same scenario.
Go workflows complete in the Chrome extension and then stop after a user continues in the desktop app.
You must decide whether the system or the engineers decide which of streaming state, context gathering or the surface handoff gets treated as the cause when they disagree.
Enterprise users can approve prepared work without one complete view of its connected reads and writes.
You have to weigh a fast approval flow against the detail administrators and client-facing users need to trust it.
A replay showed Go preparing an update from context after an administrator removed access.
You have to weigh a fail-closed control against valid workflows that may be blocked during an enterprise rollout.
Other problems with a similar purpose
In each of these, as in this one, the aim is to build machine-learning systems that earn trust by staying explainable where their errors carry real consequences.
An audit found sensitive fields in 7% of shared artifacts that manual review missed, and the classifiers disagree on the edge cases.
You have to build evidence for a production commitment you cannot easily reverse.
One threshold sends very different numbers of drivers into coaching in each region, and reviewers there agree less often.
You have to balance fair treatment of drivers against enough coverage to improve coaching.
Confirmed takeover messages are reaching guests, while legitimate pre-check-in threads are also being blocked.
You must weigh fraud prevention against a guest losing the instructions needed to enter a booked stay.
