AI Agents Are Coming to Work And They're Useful
TLDR: AI agents can follow multi-step tasks without constant prompting. They plan, act, check their work and continue until the job is done. They only perform well when instructions, data and boundaries are clear. This article explains what agents are, how they behave, and what needs fixing before trying to deploy them.
Why agents matter now
Most people have only used AI through chat.
You ask a question.
You get a response.
You drive every step.
Agents change this.
They continue working after you give them an objective.
They gather information, make choices, run steps in order and handle basic errors.
This can save a lot of time.
It also exposes every weakness inside your processes.
I see the same issue everywhere:
The organisation buys an agent, then discovers it cannot follow a workflow because no one has ever documented the workflow in the first place.
What is an AI Agent?
An agent is software that can understand a goal, break that goal into steps and carry out those steps without hand-holding. It can:
- interpret the request
- choose the right actions
- access information
- take steps in the right order
- check progress
- stop or escalate when necessary
The important part is not the technology. It is the shift from “responding” to “doing”. An assistant produces an answer. An agent completes tasks.
Where agents help today
Some workflows are already stable enough for agents.
- Agents can collect information from approved sources and prepare clean summaries.
- They can compare two sets of data and point out where they do not match.
- They can follow a repeatable process, such as preparing a document, updating a record or checking a status.
- They can perform routine quality checks against rules.
- They can assemble basic reports or checkpoints for the week.
None of this replaces jobs. It removes the constant small tasks that fill the day.
Where agents fail
Agents fail when the environment is unclear. And most environments are unclear.
Common issues include:
- instructions that change depending on who you ask
- steps that were never written down
- inconsistent naming in files and systems
- partial access to what the agent needs
- outdated rules
- unstructured documents that the tool cannot interpret
- data that is messy or incomplete
An agent cannot fix these problems. It simply exposes them.
I see organisations try an agent on a process that even experienced staff cannot describe end-to-end. The agent does not fail dramatically. It just gets stuck, loops steps or produces work that someone must clean up afterwards. This is predictable and TBH also sort of avoidable.
Why agents behave like newbie coworkers
Agents behave like junior staff members. They work fast, but they need clarity.
They need:
- clear instructions
- clear inputs
- clear outputs
- consistent data
- predictable rules
- boundaries
- supervision
Without this, they guess. Guessing is not automation. It is cost.
What needs fixing before deploying an agent
If you want agents to work, start small and stabilise the environment.
- Document how the work is actually done, not how it should be done.
- Define what a correct input looks like.
- Define what a correct output looks like.
- Clean the data the agent will touch.
- Check that the agent has the right level of access, no more and no less.
- Specify when it should stop and hand the task back to a person.
If you do this, your first agent works. If you skip it, your first agent becomes a troubleshooting exercise.
What to focus on Monday morning to explain this
Three steps make the biggest difference:
Pick one routine workflow that never changes.
Write the steps in plain language from start to finish.
Identify the files, systems, and decisions the agent would need.
This produces the first usable agent environment. It also reveals whether your organisation is ready to atttempt it.
As these tools improve, they will quietly take on more small tasks:
- first drafts
- basic analysis
- file preparation
- checking consistency
- assembling updates
- organising information
People still handle decisions, judgment, negotiation and exceptions. Agents handle the routine execution around those tasks. The value comes from removing noise, not removing people.
FAQs
Q: What is the simplest way to explain an agent to staff?
A: It is software that follows a set of steps without needing you to guide each one.
Q: How do I know if a workflow is ready for an agent?
A: If you can write the steps clearly and consistently, the workflow is ready. If you cannot, the workflow is not ready.
Q: What is the biggest reason agents fail in organisations?
A: Missing or unclear instructions. Most processes are undocumented, inconsistent or based on assumptions no one has written down.
Q: What kind of tasks should agents avoid for now?
A: Anything that depends on judgment, negotiation, exceptions or incomplete information.
Q: Should an agent access production systems?
A: Only with strict boundaries. Start with read-only access or sandbox environments.
Q: How much technical skill is required to run an agent?
A: Very little. Most of the work is process design, documentation and decision-making, not coding.
If you want a structured way to identify agent-ready processes and prepare the guardrails they depend on, the AI Business Workshop gives you the foundation to do it properly.