AI workforce control plane
Put AI workers to work.
Every action in GitHub, Slack, Gmail, Google Drive, Google Calendar, the browser is checked before it runs.
Audoryn gives your organization one place to define AI workers, assign them real jobs, connect your tools and decide exactly what they may do.
Proposed by the model
Post a stakeholder update
- Agent identity confirmedA separate identity, inside the right organization
- Capability declaredPost a message to an approved channel
- Policy evaluatedMessages to leadership need a reviewer
The action waits for a person.
The run pauses. A named reviewer sees the request and its context, then approves or declines.
The record, as it happens
- 09:38:12Research analystRead open issues in product-repoAllowed
- 09:39:47Support assistantSummarize 18 new messagesAllowed
- 09:40:05Operations coordinatorPost an update to #leadershipHeld for review
- 09:40:31Developer assistantDelete branch release/2.3Denied
- 09:41:02Sales assistantRead tomorrow's meetingsAllowed
- GitHub
- Slack
- Gmail
- Google Drive
- Google Calendar
- Governed browser
The product
An operating system for your AI workforce.
Create workers, give them real jobs, connect your tools and supervise the moments where human judgment matters.
- 01
Create workers
Give each AI worker a role, responsibilities, working hours and a named human supervisor.
- 02
Assign work
Turn business objectives into durable jobs that can be scheduled, queued and inspected.
- 03
Stay in control
Capabilities, policy, approvals and incident controls stay separate from AI reasoning.
One action, three possible answers
AI can propose. Audoryn decides what happens next.
Before an AI worker touches an external system, its request meets checks the model cannot rewrite.
- Allowed
The action proceeds.
- Held for review
The action waits for a person.
- Denied
The action stops here.
How controlled work moves
Autonomy with a visible boundary.
From the first instruction to the final action, a worker can keep moving without ever becoming its own authority.
Work
Each worker gets a clear job.
The objective, expected result and limits are defined before the work enters the queue.
JobPost a stakeholder updateOperations coordinatorPolicy
Authority is explicit.
Connected tools expose what is possible. Capabilities, risk and policy decide what the worker may actually do.
PolicyMessages to leadership need a reviewerApproval
Judgment returns to a person.
A sensitive action waits for a reviewer, and is checked against current authority again before it continues.
Held for reviewApproval covers this exact action only.Record
The result and the reasons stay together.
The request, decision and outcome remain connected in a record your team can inspect.
Recorded asREC-2048Request, checks, decision, reviewer and result kept together in one record.
Policy as code
The model proposes. The rule decides.
Authority lives in explicit, versioned rules outside the model. The same request always meets the same rule, and every decision is written to the record.
Proposed by the model
Post the weekly stakeholder update to #leadership.
Held for an operations lead. The approval covers this exact message and is checked again before it is sent.
IllustrativeIllustrative example · simplified
policy: leadership-channelsapplies_to: worker: operations-coordinator capability: slack.post_messagewhen: channel: "#leadership"decide: require_approvalapprover: role:operations-leadrecord: always
The operating model
One place to understand what your AI workforce is doing.
See workers, queued jobs, governance decisions and the moments that need human attention.
WorkersRoles, identity, schedules
Every worker has a defined place in the workforce.
A worker starts with a role, a responsibility, a schedule and a human supervisor. Its identity stays separate from the people who oversee it.
- Role
- Identity
- Supervisor
Defined before work begins
JobsObjectives, queue, runtime
Work moves with an objective and a visible state.
Jobs carry the expected result, requirements and completion criteria into a durable queue. Teams can see what is waiting, running and complete.
- Objective
- Queue
- Result
Work stays inspectable
GovernancePolicy, risk, approvals
Authority is checked at the action boundary.
A connected tool is never permission on its own. Capabilities, risk and policy decide whether an action proceeds or waits for approval.
- Request
- Policy
- Decision
Authority is explicit
SupervisionEscalations, incidents, audit
People see the moments that need them.
Exceptional work is routed to a named person. The decision and its outcome stay together in an audit record.
- Signal
- Person
- Record
Human attention stays visible
Human oversight
Autonomous where appropriate. Human where it matters.
Workers keep moving through routine work. Decisions that need judgment return to a person with the context to act.
Where people step in
- Approval
- Exception
- Review
The record, as it happens
Every action leaves a trace.
Requests, decisions and results arrive in one stream your team can inspect.
- Decisions today
- 1,284
- Allowed automatically
- 1,121
- Held for a person
- 142
- Denied at the gateway
- 21
IllustrativeIllustrative event stream · example data
Connections
Works where your company already works.
Each connection exposes a bounded set of operations. Every one still passes capability, policy and approval before it runs.
- GitHubRepository and issue workflows
- SlackTeam conversations
- GmailMailbox context and recent messages
- Google DriveDocuments and files
- Google CalendarCalendar context
- Governed browserApproved site interactions
Action gateway
- Capability
- Policy
- Approval
Authority is checked before execution.
- Allowed
- Held for review
- Denied
A connection is not a permission. It only makes an action possible; Audoryn decides whether it may happen.
Solutions
Built for different kinds of work.
Start with a defined responsibility. Give the worker room to move, with a person and a policy around the actions that matter.
Operations
Keep recurring work moving. Keep exceptions visible.
Scheduled work, connected tools and a clear handoff when conditions change.
Explore operationsThe boundaries in practice
Control is part of the architecture.
The boundary around an action stays visible from identity through to the final record.
Capabilities
Define the actions an AI worker may attempt.
Policy
Make authorization deterministic instead of leaving it to model judgment.
Approvals
Require a person before sensitive work can continue.
Supervision
Route exceptional autonomous work to a named human.
Audit
Keep what happened, who acted and why a decision was made.
Audoryn
Give AI real work. Keep authority explicit.
Start with a small supervised workforce and grow from there.