The eight mistakes that make an AI agent fail, and how to fix them
When an AI agent disappoints, the cause is rarely technical. It's almost always a vague instruction, a forgotten document, or a skipped step. Here are the eight most common mistakes, each with the symptom that reveals it, its cause, and the remedy to apply this week.
- An agent disappoints mainly because of a vague instruction or a missing document.
- A dry run and validation of the first submissions stop most of the damage.
- You automate one task at a time, never everything at once.
- A stable mission is better than a mission retouched every day.
- Without measurement before and after, you don't know if the agent is useful.
Failing agents look alike. Their manager says "AI doesn't work for my profession," when the problem is almost always in the setup. The eight mistakes below are grouped in pairs, according to when they occur. For each one: the symptom, the cause, the remedy. If you're discovering what an agent is, start with the article that defines it.
IBefore you start: the mission and the documents
Mistake 1: a vague mission. Symptom: the agent produces correct texts but off-target, too long, too commercial, addressed to the wrong people. Cause: the instruction fits in one line, like "manage my follow-ups". The agent fills the gaps by guessing. Remedy: write the mission as you would for a new employee. Who is involved, what to do, at what pace, in what tone, and what never to do.
Error 2: missing documents. Symptom: the agent invents a price, deadline, or payment condition. Cause: it didn't receive the price list, general terms, or quote template. A language model lacking information can still produce a plausible answer. Remedy: attach the documents a human would need to do the task. An ABYS agent asks questions and requests what it needs before working: take the time to answer fully.
IIAt startup: the dry run and the scope
Error 3: skipping the dry run. Symptom: the first customers receive a misdirected email or wrong amount, and trust takes a hit. Cause: the urge to go fast. Remedy: thedry run runs the agent as if it were real, without anything actually being sent. Read each draft as if you were the recipient. At ABYS, this step is part of the process, before any real work.
Error 4: automate everything at once. Symptom: several tasks start at the same time, errors mix together, and you don't know which comes from where. Cause: the enthusiasm of the first days. Remedy: one task, one agent, a few weeks of fine-tuning. Add the next one when the first runs without surprising you. It's even reasonable to never automate certain tasks, like responding to an angry customer.
IIIIn operation: review and stability
Error 5: never review. Symptom: everything seems fine, until the day a client replies, surprised, to a message unrelated to their file. Cause: after a few good sends, you switch to automatic validation and stop looking. Remedy: validate the first sends by hand. At ABYS, automatic validation is only possible after three validations, and its risks are spelled out in black and white. Even then, review a sample each week and check the log.
Error 6: changing your mind every day. Symptom: the agent seems inconsistent, one day informal, the next very formal. Cause: the mission is revised with every mood, sometimes with contradictory instructions. Remedy: note your remarks during the week, then make a single update, reviewed in full. A stable mission can be fine-tuned; a mission that changes every day can never be fine-tuned.
IVOver time: expectations and measurement
Error 7: waiting for a miracle. Symptom: disappointment after two weeks, because 'revenue hasn't budged'. Cause: you asked the agent to 'grow the business' rather than do a specific task. Remedy: give it a task whose result you can verify, like preparing today's quotes or following up on overdue invoices. A well-tuned agent can give you back time and consistency. It replaces neither a strategy nor a good product.
Error 8: not measuring. Symptom: impossible to tell, after a month, whether the agent is worth its price. You keep it out of habit or stop out of impression. Cause: no baseline figures. Remedy: before launching the agent, note for a week the time spent on the task and a simple result. Compare after four weeks. A full article details this method.
VThe five-minute diagnosis
Your agent already disappointing you? Before stopping everything, follow this checklist in order. It's often enough to find the cause, without technical expertise.
- 1Read the mission aloud
Would a new employee understand exactly what to do, for whom and within what limits? If you hesitate over a sentence, the agent will too. Rewrite it, and add what it must never do.
- 2Check the documents
Are the attached rates, templates and terms up to date? An outdated price list is enough to skew every quote. Replace expired versions and remove duplicates.
- 3Check the log
Look at what the agent read and produced during its last runs. An error that always comes back to the same place points to a specific instruction or document.
- 4Revalidate each send
If the agent sends on its own, go back to manual validation while adjusting. You see the gaps before your customers and you measure the effect of each correction.
- 5Compare with the baseline
Go back to your baseline figures. If nothing improves after two weeks of serious adjustment, the task may have been poorly chosen: change tasks rather than insisting.
Your agent announces to a customer a delivery deadline you've never practiced. What is the most likely cause?
?Questions we're asked
Why isn't my AI doing what I ask it to?
Most often, the instruction is too short or ambiguous, and the agent fills the gaps by guessing. Clarify the task, the recipient, the tone, the pace, and what never to do. Also check that useful documents are attached and up to date. An instruction a new employee would understand without asking questions is usually a good instruction.
Why do AI projects fail in business?
Because you're aiming too broad, too fast. You give the AI a vague objective, skip the dry runs, automate several tasks at once, and measure nothing. Projects that work start with a precise task, tested in dry run, validated by hand, then measured for a few weeks before going further.
How do you prevent AI from making up information?
Give it the documents that contain the right information: rates, terms, templates. Authorize it in its mission to say it doesn't know, and forbid it from announcing a price or date absent from these documents. Finally, review the first sends during the dry run: that's where inventions are spotted without damage.
Should you let an AI agent send emails on its own?
Not at the beginning. Validate the first sends yourself to check the tone, recipients, and amounts. When several sends in a row are good, automatic sending can be justified for low-risk tasks. Even then, review a sample each week and keep manual validation for anything involving money.
Creating an agent at ABYS follows these safeguards: questions before working, plan, dry run, first sends validated by you.
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