Automating a task with an AI agent has become accessible: you describe what you want, connect your tools, and the agent runs. That ease has a downside. With no technical barrier left, mistakes move elsewhere: into the choice of task, the way it is described, and the monitoring after launch.
The twelve mistakes below fall into four families. For each, you will find the symptom, the cause and the fix.
Before launch: mistakes of choice#
1. Automating a task nobody can really describe#
Symptom: the agent's results miss the mark, and every correction calls for another.
Cause: the task relies on implicit judgement that nobody has ever put into words. "Answer quote requests" often hides a dozen rules: which customers to prioritise, which jobs to turn down, what lead time to announce.
Fix: before creating the agent, try explaining the task in writing to an imaginary intern. If you cannot do it in half a page, break it down into simpler sub-tasks.
2. Starting with the riskiest task#
Symptom: a mistake visible to a customer in the very first week, and a lasting loss of trust.
Cause: you pick the most painful task, which is often also the most sensitive (customer follow-ups, replies to complaints).
Fix: start with internal tasks where a mistake has no consequences: a morning briefing, a watch, a report. Then move on to visible tasks, with human approval at first.
3. Automating a process that does not work#
Symptom: the agent faithfully reproduces an already shaky process, only faster.
Cause: you automate what exists without asking whether it makes sense. A reminder sent 45 days after the due date is too late, whether a person or an agent sends it.
Fix: use the agent's description as a chance to rethink the process. What timings? What tone? What exceptions? The unpaid invoice follow-up template, for example, suggests stages at 3, 15 and 30 days after the due date, which you can adapt.
While describing: brief mistakes#
4. Staying too vague#
Symptom: uneven results, sometimes very good, sometimes unusable.
Cause: a brief like "keep an eye on my industry" leaves the agent guessing the scope, frequency, length and format.
Fix: always specify what, when, with which tools and in what form. Compare:
Too vague: "Keep an eye on my competitors."
Precise: "Every Monday at 8 am, check the websites of these four competitors (list below), spot new products, price changes and new job postings, and send me a summary in five points maximum. If nothing has changed, just write 'Nothing new'."
5. Forgetting the exceptions#
Symptom: the agent chases a customer in dispute, writes to a prospect who asked not to be contacted again, or processes a contested invoice.
Cause: the exceptions are obvious to you, so you do not write them down.
Fix: add a "never" section to every brief. For example: "Never chase an invoice marked 'disputed'. Never send emails at the weekend. Never reply to a review that mentions a health problem."
6. Not specifying the tone#
Symptom: messages too casual for your institutional clients, or too formal for your regular customers.
Cause: an agent's default tone is neutral, which does not necessarily match your house style.
Fix: give two or three examples of messages you have actually sent and that you feel get it right. State the level of formality, the signature, the length. On Future Agent, you can upload these examples as context files: the agent refers to them on every run.
After launch: monitoring mistakes#
7. Not reviewing the first results#
Symptom: you discover a problem a month later, when a customer complains.
Cause: the agent seems to work, so you stop looking.
Fix: review everything during the first week, then a sample every week for a month. Each agent's run log shows what it did, when and with what result.
8. Not appointing an owner#
Symptom: when an agent gets something wrong, nobody knows who should fix it. When an employee leaves, nobody knows what a given agent is for.
Cause: the agent was created by someone, but belongs to no one.
Fix: each agent has a named owner, who receives the alerts, reviews the results and updates the description. Write it into the agent's description itself.
9. Piling up agents without an overview#
Symptom: two agents each send an email to the same customer on the same day, or one agent overwrites another's data.
Cause: each agent was designed in isolation.
Fix: keep a simple list of your agents (what they do, with which tools, how often). When agents need to work together, link them explicitly rather than letting them step on each other's toes: on Future Agent, one agent can fill a shared table and trigger the next, which only receives the new rows. The workflow map shows who does what.
Security and compliance: costly mistakes#
10. Granting more access than necessary#
Symptom: an agent that was only supposed to read your calendar also has access to your emails and files.
Cause: you connect everything "to be on the safe side".
Fix: apply the principle of least privilege. Only connect the tools the agent needs for its task. If an agent does not need to send emails, do not give it that ability.
11. Neglecting personal data#
Symptom: customer or employee data flows through tools without you knowing exactly which data or why.
Cause: automation multiplies data processing without anyone thinking to document it.
Fix: for each agent that processes personal data, note the purpose, the data involved and the retention period. Update your record of processing activities, which a dedicated agent can help with using the up-to-date GDPR register template. For health data or other sensitive data, be especially careful and seek advice if needed.
12. Letting an agent make irreversible decisions#
Symptom: a payment made, a contract sent, a purchase approved without anyone checking.
Cause: human approval was removed too early, or never planned.
Fix: keep human approval for anything that commits the business irreversibly or financially. An agent can prepare a transfer, a purchase order or a proposal; you are the one who presses the button. On Future Agent, for example, buying a domain name never happens without your explicit authorisation.
What to do when a mistake happens anyway#
Even with all these precautions, an agent will get something wrong one day. That is not a failure of the approach: it is a chance to improve its rules. Here is a simple procedure.
- Pause the agent if the mistake affects customers or money. Better a day without follow-ups than ten wrong ones.
- Read the run log to understand what the agent received as input and what it did with it. In the vast majority of cases, the mistake comes from a situation the brief did not anticipate.
- Put things right with the people affected: a short, sincere apology email is usually enough.
- Add a rule to the agent's description so the situation is handled correctly next time, then restart it.
- Record the incident in a shared document. After a few months, that list becomes valuable documentation of your special cases.
This discipline turns every mistake into one more rule, and your agents become more reliable over time.
Summary table#
| Mistake | Family | Fix in one sentence |
|---|---|---|
| Task that cannot be described | Choice | Write the task in half a page before creating the agent |
| Task that is too risky | Choice | Start internally, then move to visible tasks with approval |
| Shaky process | Choice | Rethink the process while describing it |
| Vague brief | Brief | What, when, with which tools, in what form |
| Forgotten exceptions | Brief | Add a "never" section |
| Tone not specified | Brief | Provide real example messages |
| No review | Monitoring | Review everything in week one, a sample afterwards |
| No owner | Monitoring | One named owner per agent |
| Piled-up agents | Monitoring | A list of agents, explicit links between them |
| Access too broad | Security | Only connect what is needed |
| Personal data | Security | Document purpose, data and retention period |
| Irreversible decisions | Security | Mandatory human approval |
A simple method to avoid these pitfalls#
Before each new agent, take five minutes to answer these questions:
- Can I describe the task in half a page, exceptions included?
- What happens if the agent gets it wrong? Who will notice?
- Which tools does it need, at a minimum?
- Does it process personal data?
- Is there an irreversible action? If so, where is the human approval?
- Who is responsible for it?
If you are just starting, the 30-day plan to get started with AI builds these precautions in step by step. And to monitor your agents over time, the article measuring how well your AI agents perform suggests concrete indicators.