Why AI agents in companies don't behave as expected
When a company deploys an AI agent and the result falls short of expectations, it's usually the consequence of a handful of predictable mistakes - in the overwhelming majority of cases, the same failure patterns repeat across very different types of projects. The most common mistakes in AI agent deployment rarely have anything to do with the technology itself; far more often they come down to how the project was briefed, what data the agent has access to, what authority it holds, and who is monitoring its output. These four areas - an unclear brief, missing data, absent boundaries, and a lack of human oversight - form the core of most problems companies run into when implementing AI agents.
Understanding these patterns in advance is the only way to avoid them. The sections below describe how each mistake arises, how it shows up in practice, and what helps eliminate it before the agent goes live.
An unclear brief: the agent doesn't know what's expected of it
The most common source of problems with an AI agent isn't the model - it's the brief. If a company defines the agent's task too broadly - "have the agent help customers" or "have it automate communication" - it ends up in a situation where nobody can say in advance exactly what the agent is supposed to do, when a task should count as complete, or what it should do when it hits an edge case.
In practice, an unclear brief typically shows up as:
- the agent handles tasks that were never meant to fall within its remit,
- different people within the company have different ideas about what the agent does and doesn't do,
- there's no clear definition of a "successful" output, so there's no way to assess whether the agent is performing well,
- when a process changes, nobody knows which part of the brief needs updating.
The solution is to define the scope precisely before development even starts - which processes the agent should cover, what types of input it processes, and where its competence ends. The same holds true for broader business process automation, where setting priorities matters just as much as it does for a single AI agent; the principles behind scoping and prioritising automation projects carry over to AI agents in many respects.
Missing or poor-quality data as a hidden cause of failure
An AI agent is only as good as the data it has access to. If a company's internal processes, product information, or customer history aren't organised and accessible in a structure the agent can actually process, the result is inaccurate answers - regardless of how capable the underlying model is.
Typical data problems in AI agent deployments include:
- fragmented information sources (multiple systems, spreadsheets, and documents with no unified structure),
- outdated or inconsistent data that contradicts itself,
- no connection to a single source of truth, so the agent fills in gaps with guesses instead of verified facts,
- no process for regularly updating the data the agent relies on.
The fix for this usually involves an approach that lets the agent answer directly from verified company documentation rather than the model's general knowledge. A concrete approach for teaching an agent to work with internal company data sources is one of the steps that directly addresses this mistake.
No boundaries of authority: when the agent does more than it should
Another recurring mistake is the absence of clearly defined boundaries around what the agent may do on its own and where it must request human approval. Companies sometimes deploy an agent with access to sensitive data or actions (such as modifying orders, sending payments, or making changes within a system) without first defining which steps are automatically permitted and which require confirmation.
This issue isn't just a matter of security but also of accountability - if an agent makes a mistake on a task it should never have carried out in the first place, it becomes difficult to trace back where the process broke down. Part of this problem can be addressed by formalising the approval step as part of the process itself - similar to the approach used in automating approval workflows within a company, where it's clearly defined which step runs automatically and which waits for sign-off from the person responsible.
Good practice is to grant the agent authority in stages - starting with purely informational tasks, moving to proposals that a human approves, and only reaching fully autonomous actions once the agent has proven itself in practice.
| Level of authority | What the agent does | When it's appropriate |
|---|---|---|
| Informational | Answers questions, changes nothing | At the start of deployment, during testing |
| Proposal | Prepares the action, a human approves it | For sensitive or costly steps |
| Autonomous | Carries out the action without approval | For proven, low-risk tasks |
Missing human oversight and feedback
Even a well-configured agent needs ongoing oversight. Companies that stop monitoring an agent's output once it goes live lose visibility into when its behaviour starts drifting from what's expected - for example when the input data changes, new types of requests appear, or a related process the agent depends on gets modified.
The absence of human oversight most often shows up as problems surfacing only once a customer or an internal team reports them - not because they couldn't have been foreseen, but because nobody was checking on an ongoing basis. The difference between an agent that operates entirely on its own and one that has a human involved at key points comes down to how quickly a deviation can be caught and corrected. The choice between these two approaches is covered in the article on autonomous agents versus human-in-the-loop agents.
It's equally important to define from the outset which metrics will be used to continuously evaluate the agent's performance - for example, answer accuracy, the rate of escalation to a human, or the number of outputs that need correcting afterwards. Only regular tracking of these indicators, rather than a one-off impression from the first few weeks of operation, will show where the brief, the data, or the boundaries of authority need adjusting.
The more of these four areas - the brief, the data, the boundaries, and oversight - a company addresses before deployment, the lower the risk that any one of them becomes the project's weak point:
| Area | What happens if it's neglected |
|---|---|
| Brief | The agent handles tasks outside its original intent and nobody can judge whether its output is correct |
| Data | The agent fills gaps with answers that sound credible but don't match reality |
| Boundaries of authority | The agent carries out a sensitive action without approval, and accountability becomes hard to trace afterwards |
| Human oversight | A deviation from expected behaviour is only discovered via the customer, not internally |
How to avoid the most common mistakes in AI agent deployment
The common denominator behind most AI agent problems isn't a failure of the technology - it's insufficient preparation before deployment. A company that clarifies the scope of the task in advance, ensures quality and accessible data, sets clear boundaries of authority, and maintains ongoing human oversight significantly reduces the likelihood of running into any of the failure patterns described above.
Choosing a partner who can assess and set up these areas in advance - not just deliver the agent itself - matters just as much. The criteria worth checking when selecting a vendor are summarised in the checklist for choosing a software vendor, which applies equally to traditional software and AI solutions.
If you're considering deploying an AI agent in your company and want to check first whether the brief, the data foundation, and the authority settings are properly prepared, take a look at our AI and automation solutions or arrange a no-obligation consultation, where we'll go through your company's specific situation together.