Product · AI Coworkers
When one person builds an AI workflow that works, it usually lives — and dies — in their personal account. Leapforce turns proven workflows into coworkers: named, owned, access-scoped agents that any approved team can use, improve, and keep when people move on.
Why coworkers, not chat histories
From personal benefit to company asset
Across most companies, AI skill is unevenly distributed and privately held: a handful of people in each department have built prompts, contexts, and small automations that genuinely work — inside personal accounts the company can't see, reuse, or keep. When those people change roles or leave, the capability leaves with them. The same work gets rebuilt, slightly worse, by the next person.
Analysts expect task-specific agents in a large share of enterprise software within the next year, and agent activity in workplace suites has grown by an order of magnitude — but almost all of it is being created bottom-up, without ownership or review. That's not a reason to stop it; it's a reason to give it a place to live.
A coworker is that place: a workflow promoted into a company asset, with an owner, a scope, a version history, and an audit trail — running through the same gateway, access rules, and routing as everything else on this platform.
How it works
A loop, not a funnel — every improvement feeds the next build
Begin with a ready-made coworker template, or capture a workflow someone already runs by hand. Instructions, context, connectors, and model preferences live together as one definition.
The coworker gets its own non-human identity: a named owner, the minimum connector scopes it needs, a model policy, and a budget. It can never quietly do more than it was given.
Before publishing, the responsible owner reviews what the coworker does, touches, and costs. Approval is recorded — the question “who allowed this agent?” always has an answer.
The coworker appears for the teams whose roles include it — Support sees the triage coworker, Finance doesn't. Each user works with it under their own identity, on top of its scope.
Changes are versioned; usage, cost, and outcomes are visible per coworker. Good ones get invested in, stale ones get retired — like any other asset.
In practice
Not generic assistants — each one is built from a real workflow, scoped to a team, and pinned to a policy. Hover to see how each is wired.
Support
Reads incoming tickets, drafts replies, and escalates by policy. Scoped to the ticketing system only; sends stay human-approved.
Legal
Flags clauses against your playbook and drafts redlines for counsel to review. Premium model; document access only.
Finance
Assembles monthly summaries from ledger data. Sensitive fields stay on a local model inside your network, by policy.
Every coworker has a named owner. When the owner leaves or changes roles, ownership transfers — the coworker, its history, and its scope stay. The company keeps what it paid to learn.
Instructions, reference context, and behavioral guardrails live at the company level, versioned. Improvements by one team compound for every team — instead of resetting with each new hire.
Ready-made coworker templates for common departmental work give teams a working starting point — adapted to your systems through your connector registry, not built from a blank prompt.
Coworkers run through the gateway like everyone else: access checked per call, sensitive data masked or kept local, spend metered against their own budget, every action logged with the user who asked.
A coworker built in Sales can be published to Operations with different scopes — same behavior, different reach. Reuse is a publishing decision, not a copy-paste of prompts over chat.
Customer-visible or irreversible actions can require a named person's approval. The coworker prepares; the human decides; the trail shows both. Autonomy is granted per action, never assumed.
Per-coworker dashboards show usage, cost per task, and escalation rates, so team leads manage adoption with evidence. A coworker that isn't earning its keep is visible — and retirable.
On the enterprise plan, we stand up your first coworkers with you — configuration, scoping, and tuning — so value doesn't wait for an internal AI team to be hired.
Adoption, honestly
The research on unsanctioned AI is consistent: people use unapproved tools because the approved ones are worse or missing, and bans push usage underground rather than ending it. Companies that offer capable, sanctioned alternatives see unauthorized use drop sharply — in one documented healthcare rollout, by nearly ninety percent, while saving clinicians real time.
Coworkers are the "better path" strategy in product form: employees get agents that are already connected to real systems and already allowed — faster than anything they could wire up themselves. And because human enablement matters as much as tooling, free role-based training through our partner Leapskill.ai is included, aligned with your policies and workflows.
Questions we hear
Three ways: it has an identity (owner, scope, budget) instead of living in someone's account; it reaches real systems through governed connectors instead of pasted context; and it is versioned, measured, and auditable. A saved prompt is a habit. A coworker is an asset.
That's a policy you set. Common pattern: anyone can build for themselves within their own scopes; publishing to a team requires the team owner's approval; publishing company-wide requires IT or a designated review group.
Only if published with a trigger through Workflows — and even then within its scope, budget, and approval gates. Autonomy is an explicit grant with an owner's name on it, never a default.
Ownership transfers as part of offboarding; the coworker, its versions, and its audit history remain. This is the point: institutional AI knowledge stops walking out the door.
Whatever their model policy says: a cost tier for volume work, premium for reasoning, local for sensitive data — decided by routing rules, not hardcoded. See Model Routing.
Its scope is its ceiling: connector actions, data classes, model tiers, and budget are fixed at publish time and enforced per call at the gateway. Users' own permissions apply on top — a coworker can't show someone data their role couldn't see.
Coworkers improve when people improve them — edits are versioned and reviewed. Your prompts and data are not used to train underlying models; provider-level training controls are part of the gateway configuration, not left to defaults.
Usually, yes — existing prompts, instructions, and workflows are re-homed as coworker definitions, their integrations re-issued through the registry with proper scopes. The managed service does this with you as part of onboarding.
Watch its numbers: volume handled, escalation and correction rates, cost per task, and the qualitative feedback of the team using it. Publishing is reversible — retiring a mediocre coworker is one click, and the audit trail keeps its history.
Yes — free AI training through Leapskill.ai comes with the platform: role-based, department-level, and custom-agent training so teams get the most out of the coworkers built for them.
