An AI employee for one operations workflow, not another tool for your team to manage.
TryAgent builds, runs, and maintains AI employees that complete recurring back-office work inside your existing systems. Start with one workflow, keep humans on exceptions, and pay per completed outcome.
The useful version of an AI employee is narrower, more accountable, and easier to evaluate than the category name suggests.
One role, one workflow
The AI employee is assigned to a specific recurring workflow, not every task in the department. The boundary is narrow enough to monitor and improve.
Existing systems stay in place
The workflow runs across the inboxes, portals, ERPs, CRMs, spreadsheets, and queues your team already uses. The goal is not another workspace to manage.
Humans own judgment
Approvals, policy exceptions, unclear data, risky updates, and above-threshold decisions stay with people. The AI employee prepares and routes the work.
Price follows completed work
The commercial unit is a completed outcome, such as an invoice posted, record updated, case triaged, or approval packet resolved.
The AI employee owns the routine path. Your team owns the operating judgment.
The goal is not to remove people from the workflow. The goal is to remove repetitive coordination so people spend time on cases that deserve judgment.
- +Reading operational work from inboxes, PDFs, portals, forms, spreadsheets, APIs, and workflow queues.
- +Extracting and validating fields before a downstream system is updated.
- +Comparing records across systems and building an exception packet when data does not match.
- +Routing approvals with the supporting context a human needs to make a decision.
- +Updating the system of record after the workflow meets the agreed completion criteria.
- +Logging the action history so the team can review what happened later.
A production AI employee needs explicit boundaries before it gets useful.
Most operational risk appears at the edge of the workflow: new records, mismatches, missing data, policy conflicts, unusual customer cases, and system changes. Those boundaries are designed during the audit, not after the workflow is live.
- -New vendor, customer, or account decisions that require trust and policy review.
- -Large dollar values, unusual volume spikes, sensitive customer cases, or escalations above threshold.
- -Ambiguous records where the right next action depends on context outside the workflow.
- -Policy conflicts where the rule is unclear or the responsible owner needs to decide.
- -Workflow changes that require a new operating decision, not just a system update.
Start with a workflow where completion is easy to define.
The best first AI employee is not the most impressive demo. It is the recurring role with enough volume, clear boundaries, and a completed unit the business already understands.
AP workflow employee
Captures invoices, prepares approval packets, checks PO evidence, posts clean outcomes, and routes exceptions.
Onboarding workflow employee
Collects required inputs, checks records, updates systems, nudges owners, and escalates missing or risky items.
Document intake employee
Reads documents, extracts structured fields, checks confidence and completeness, and syncs data to the right system.
Before hiring an AI employee, define the job description.
The workflow audit turns a vague automation idea into an operating role with boundaries, systems, completion criteria, and a commercial unit.
See AI workflow automation →Compare the managed automation model →Explore back-office automation →- +The first workflow that has enough volume, repeatability, and value to become an AI employee role.
- +The exact work the AI employee owns and the work that remains human.
- +The completed outcome used for pricing and measurement.
- +The systems, permissions, and approval gates required for a controlled pilot.
- +The exception categories that should route to humans before anything goes live.
- +The smallest pilot scope that can prove value without turning into a transformation program.
Bring one workflow that feels too manual. We will help define the AI employee job.
The strongest first roles usually sit where volume, rules, documents, systems, and human exceptions meet.
Book a workflow auditGet the first-role checklist.
Leave a work email and we will follow up with the questions that help identify whether a workflow is ready to become an AI employee role.
What is an AI employee in TryAgent's model?
An AI employee is a managed automation role assigned to one recurring operations workflow. It completes routine work inside existing systems, routes exceptions to humans, and is measured by completed outcomes rather than software seats.
Is this just a chatbot?
No. A chatbot mostly exchanges messages. A workflow AI employee is scoped around operational work: reading inputs, checking records, routing approvals, updating systems, and logging completion.
What does TryAgent manage?
TryAgent maps the workflow, defines the completion criteria, builds the system connections, scopes permissions, designs the exception path, monitors the workflow, and maintains it as the process changes.
Where do humans stay involved?
Humans stay on approvals, exceptions, policy-sensitive work, high-risk updates, and cases where the AI employee does not have enough context to complete the workflow safely.
How is an AI employee priced?
The cleanest unit is a completed outcome: one invoice posted, record updated, onboarding step completed, exception packet routed, or similar workflow-specific unit. The workflow audit defines that unit before a paid pilot.
Do we need to replace our current tools?
No. The first assumption is that the AI employee should work inside the systems your team already uses. Replacement only enters the conversation if the existing workflow cannot support a controlled pilot.