Digital workers: how bots became AI agents

Digital workers are software systems that carry out tasks people would otherwise perform on a computer. The term originally described robotic process automation (RPA): bots that followed fixed instructions, clicked through screens, copied information between applications and repeated administrative processes. Today, it increasingly refers to AI agents that can read documents, interpret requests, write responses and decide which steps to take next.

The distinction matters to anyone deciding what work to delegate. A scripted bot follows a route someone has already mapped. An AI agent can interpret information and respond to situations that were not anticipated when the task was designed. That flexibility brings new risks. The agent might misunderstand a request, rely on outdated information or take an action its owner never intended. Digital workers now need supervision that accounts for judgement as well as execution.

When a digital worker meant a scripted bot

Imagine an employee processing supplier invoices. They open a document, copy the invoice number, enter the amount into an accounting system and update a spreadsheet. If the steps stay consistent, an RPA bot can repeat them.

This was the original promise of digital workers. They could click buttons, enter information, move files and transfer data between applications without someone repeating the same sequence manually. Their instructions were explicit, and their behaviour was predictable as long as the surrounding software remained unchanged.

But a small change could cause trouble. A supplier might move the invoice number to another part of a document. An application update might relocate a button. An unexpected error message could leave the bot waiting indefinitely because nobody had written a rule for that situation.

The bot did not understand the purpose of the invoice or why a particular amount needed checking. It followed instructions. Someone still had to anticipate exceptions, maintain the automation and decide what happened when the process stopped.

That arrangement worked well for repetitive tasks with stable inputs. It was less useful when a job involved reading unfamiliar material or deciding what to do with an exception. The bot could follow a procedure, but it could not make sense of the work behind it.

What changes when the worker can read and write

Modern AI agents can work with ordinary language and less structured information. They can read an email thread, examine a report, extract details from a document and prepare a response. When connected to business tools, they can also retrieve information, update records and carry out parts of a larger assignment.

Consider a supplier review. A traditional automation might retrieve a supplier's name and place it in a standard document. An AI agent can receive a broader brief: gather available information, compare it with specified requirements, identify unanswered questions and prepare an assessment for the owner.

The agent has room to interpret the assignment. It can recognise that two documents refer to the same organisation, extract relevant details from different formats and adjust its approach when a source does not contain the expected information.

That flexibility also changes how mistakes happen. A scripted bot might enter a value in the wrong field because its instructions are outdated. An AI agent might enter the wrong value because it misunderstood a document. It could treat an old email as current, confuse an estimate with a confirmed figure or write a convincing explanation based on incomplete evidence.

There is another important distinction: preparing an action does not grant permission to perform it. An agent might draft a suitable reply to a supplier. That does not mean it should send the message. It might identify an invoice that appears ready for payment. That does not give it authority to move money.

A polished deliverable is not proof that the agent understood the assignment. The person responsible still needs to check important claims against their sources and examine the proposed actions before they take effect.

Give the worker a job, not an open-ended instruction

Giving an AI agent access to business applications and telling it to help with operations leaves too much undefined. A useful delegation starts with a specific outcome, the information available to the agent, a spending limit and conditions that require it to stop.

For example, an operations lead might assign an agent to review incoming support requests, group them by subject, identify urgent cases and prepare draft replies. The brief should explain what counts as urgent, which sources the agent may consult and how it should handle a request that does not fit the usual categories.

The owner might allow the agent to organise information and draft responses while reserving customer contact, refunds and changes to existing records for human approval. The boundary needs to be clear before the work begins.

Before assigning a digital worker, settle these practical questions:

• What deliverable should the agent produce, and what does an acceptable result look like?
• Which documents, accounts and applications may it access?
• What spending limit applies to the assignment?
• Which decisions can it make without asking?
• What situations require it to stop and request human judgement?
• How will the owner inspect the result and the actions taken during the assignment?

These answers give the agent room to work without turning a broad instruction into permission to do anything that seems useful. They also give the reviewer a clear standard for assessing the finished work.

Put supervision where consequences begin

A useful way to organise supervision is to distinguish internal preparation from actions that affect the outside world. Reading a document, comparing information or drafting a message can usually happen within an agreed assignment. Sending the message changes the situation. So does paying an invoice, publishing a page or changing a customer's account.

An approval should describe the exact action awaiting permission. A proposed email should show its recipient and complete message. A payment request should identify the amount and destination. The reviewer should also be able to see the cost incurred so far and the deadline attached to the decision.

We believe eight kinds of action should always require a person:

• Making first contact with someone outside the workspace.
• Moving money or issuing refunds.
• Deleting or overwriting records the agent did not create.
• Changing roles or modifying its own policy.
• Connecting an integration or widening access scopes.
• Accepting terms or signing documents.
• Publishing material publicly.
• Taking action in another workspace.

The person reviewing a request should be able to approve, change or reject it. A general instruction to continue is not enough when the next step could contact a customer or alter a financial record.

This does not mean every internal step needs permission. Requiring someone to approve every search, draft or routine calculation would turn the agent into another source of administrative work. The better arrangement lets it complete preparation within its agreed limits and pauses when a decision carries an external consequence.

Keep a record of what happened

An approval tells you what a person authorised. An activity log tells you what happened around that decision. Both matter when an agent works across business applications.

A useful record includes tool calls, approvals and policy changes. It should be append-only, so earlier events cannot simply be rewritten or removed after an unexpected result. If an agent updates a record or produces an inaccurate deliverable, the owner needs to establish which tools it used and what permissions applied at the time.

Budgets provide another boundary. Every delegation should have a hard spending cap. An agent might need several searches or retries to complete a difficult assignment. Without a fixed limit, those extra steps can consume resources beyond what the owner intended. When the cap is reached, the agent should stop and wait for a decision.

The finished deliverable and the activity history answer different questions. The deliverable shows what the agent produced. The history shows how it worked, which actions it attempted and where a person intervened. A team needs both to review the assignment properly.

This is also why we think the term digital worker needs a more precise meaning today. An agent that interprets information and operates business tools needs a defined job, limited permissions and a record of its actions. Its ability to complete a task is only part of what makes it suitable for delegated work.

Start with one assignment you can inspect

An organisation does not need to hand an entire department to digital workers. Start with one recurring assignment that has a clear output and a manageable cost of error. Preparing meeting notes, sorting an inbox or producing an initial research report are reasonable candidates because someone can review the result before it reaches another person.

Write the brief as if you were assigning the work to a new member of staff. Explain the expected result, provide the relevant source material, set a budget and state what the agent must not do. Specify the actions that need approval, too.

Delagents is being built around this arrangement. A person writes a brief, an agent on staff carries out the assignment and checkpoints send consequential actions to an approvals inbox. Each approval card shows the exact proposed action, the cost so far and its deadline. Every delegation has a hard budget cap, and an append-only activity log records tool calls, approvals and policy changes. The Delagents product overview explains the workspace.

For your first trial, choose a task that takes someone an hour to prepare but still needs human review. Write a one-page brief, set a spending limit and identify the actions that must wait for approval. Run the assignment without expanding its permissions, then check the deliverable against its source material and review the activity log. Use what you find to decide whether the next delegation should be broader, narrower or left with a person.