Blog · AI agent projects
The 7 most common mistakes in AI agent projects
An AI agent delivering good results during a demo does not necessarily mean it will perform just as well in a real business environment. Once deployed in an enterprise setting, factors such as process clarity, the quality of information sources, access permissions, and human oversight become just as important as the capabilities of the model itself.
The challenges encountered in AI agent projects tend to arise around similar areas. The following seven mistakes explain why a project that starts with a promising idea may fail to deliver its expected business value.
Mistake 1 of 7
Failing to clearly define the problem and success criteria
Saying "Let's use AI" is not a project objective on its own. The problem to be solved must first be clearly defined, followed by the role AI is expected to play in addressing it. When the objective is unclear, the agent's responsibilities can quickly expand. Initially, it may be expected to review a few documents. Then it is asked to prepare reports, respond to customers, and perform actions across different systems. Before long, it becomes difficult to define where the project starts and where it ends.
Another important consideration is defining success criteria from the outset. What business outcome is AI expected to improve? Should the process be completed faster, should operational costs be reduced, or should the error rate be lowered? Expected improvements need to be defined through measurable indicators such as time, cost, or productivity. Knowing the current performance baseline and the target makes it possible to evaluate whether the project is actually delivering value.
"Making operations faster" is a broad and difficult-to-measure objective. By contrast, "Reducing the time it takes to classify incoming support requests by topic and route them to the right team" provides a much clearer starting point.
How Harmona helps
Harmona allows managers to describe the agent or workflow they want to create in natural language. The system can ask questions to clarify missing decisions and prepare a draft based on the connections, integrations, and models configured within the organization. Still, deciding which problem needs to be solved and which business outcomes should be improved is the responsibility of the process owner, not the technology.
Mistake 2 of 7
Trying to automate the wrong process
Not every process is a good candidate for AI automation. Tasks that occur infrequently, vary significantly each time, or rely heavily on subjective decisions may not deliver the expected benefits. A common mistake is trying to automate an existing process without first evaluating whether it actually needs automation. Sometimes, the problem is not that a task is performed manually, but that the process itself is unnecessarily complicated. An automation built without addressing excessive approval steps, repetitive data entry, and unclear responsibilities will simply make an inefficient process run faster.
How Harmona helps
Workflows in Harmona are autonomous systems. For these systems to operate as effectively as possible, their start and end scenarios need to be clearly defined and communicated to Harmona in detail.
Mistake 3 of 7
Overlooking the quality of information sources
The quality of an agent's responses depends on more than the capabilities of the underlying model. If an agent works with outdated, incomplete, or conflicting information, the results will not be reliable either.
Another important consideration is that different versions of the same information may exist across different sources. For example, an organization's systems may contain both old and current versions of the same procedure. In this case, simply giving the agent access to relevant documents is not enough. It must also consider document version and update information, prioritizing the most current versions. This allows older documents to remain in the system while ensuring that the agent's responses are based on up-to-date information.
The goal is not to upload as many documents as possible into the system. It is to ensure that agents can access information across different enterprise sources and identify what is accurate, current, and relevant.
How Harmona helps
Harmona brings information scattered across an organization's different systems and sources together in a shared data layer accessible to AI agents. Enterprise documents, relational databases, web content, and defined APIs can all be made available to agents through this layer. Regardless of the system or format in which information is stored, agents can access the sources they need through a single access layer.
This access is not limited to semantic search. Agents can find information based on its meaning or search directly for a specific phrase or code. This capability is particularly important for contract clauses, product codes, and queries that require exact matches.
Another important capability of Harmona is that it separates the process of retrieving information from the process of working with that information. Relevant sources are identified first. Operations such as comparison, consolidation, and calculation are then performed in a separate analysis environment using the actual files and query results. This separation ensures that, particularly in tasks requiring multiple sources to be evaluated together, the analysis is based directly on the relevant files and query results.
One access layer for every source
Agents reach documents, databases, web content and APIs through the same layer, by meaning or by exact phrase. Retrieval and analysis stay separate steps.
1 · Retrieve
Identify the relevant files and run the queries.
2 · Analyze
Compare, consolidate and calculate on the actual files and query results.
Mistake 4 of 7
Giving agents more permissions than necessary
Connecting an agent to different enterprise systems offers significant advantages. However, these advantages can become a risk when access boundaries are not properly defined.
Reading information and modifying records are not the same thing. Neither are preparing an email draft and sending it directly, or checking payment information and initiating a financial transaction. Each carries a different level of risk.
This is where the principle of least privilege comes into play. An agent should only have access to the data and actions required to perform its assigned task.
Rather than granting broad permissions from the beginning and restricting them only when problems arise, it is safer to start with a limited scope and expand access in a controlled manner as actual needs emerge.
The least-privilege ladder
Each rung carries more risk than the one before. Start on the lowest rung the task allows.
Read
Look up a record
Draft
Prepare an email
Send
Send it to the customer
Transact
Start a payment
How Harmona helps
In Harmona, agents are configured through the connections, integrations, skills, and capabilities available to them. For database connections, administrators determine which tables can be accessed. In custom API integrations, read operations are separated from operations that make changes.
Mistake 5 of 7
Failing to design human approval for critical actions
The purpose of AI automation is not to remove humans from every process under all circumstances. Some tasks can be handled autonomously by an agent, while others require the final decision to remain with a person.
When this distinction is not clearly defined, one of two problems can arise: either the agent is given too much freedom and may perform actions that put the organization at risk, or employees are required to manually review every step. In the latter case, the expected time savings from automation disappear.
Human oversight is the broader approach, while human approval refers to a specific checkpoint that requires authorization before an action can proceed. Human approval is one practical implementation of the approach known as human-in-the-loop.
How Harmona helps
Harmona allows human approval steps to be added to workflows. When a process reaches one of these steps, it pauses and waits for a decision from the designated person. This is not simply a rule written into the agent's instructions. Approval is enforced at the system level, meaning the agent cannot proceed with the action as though approval had already been granted.
A human approval step in a workflow
The workflow pauses here. Nothing after this step runs until the designated person decides.
- 1Collect the data
- 2Draft the reply
Human approval
Waiting for a decision
- 4Send
- 5Log the result
Mistake 6 of 7
Leaving traceability and error handling until later
When an agent produces an unexpected result, it should be possible to examine which sources it used, which steps it followed, and which actions it performed.
Traceability is not a reporting feature to be added after deployment. It should be built into the project from the very beginning.
Knowing what to do when something goes wrong is just as important as keeping records. Should the agent retry the same operation? Stop the task? Turn to another source? Or hand the issue over to an employee?
How Harmona helps
Harmona provides agent logs, tool-call records, workflow timelines, and step-level usage information. This makes it possible to examine not only the final result but also the key stages leading up to it. Understanding why a particular step failed makes it easier to introduce changes that prevent the same issue from recurring.
Mistake 7 of 7
Scaling up without running a pilot
Rather than introducing an AI agent into a critical, large-scale process from day one, it is safer to start with a well-defined pilot focused on a specific task. For example, you can select a time-consuming, repetitive task within a department and introduce the agent into that process.
The primary purpose of a pilot is to determine whether the success criteria defined at the outset are being met in real-world use. This means looking beyond whether the agent can complete the task and measuring how much the process has improved compared to its initial performance, how much time it saves, and how it affects operational costs.
These results make the agent's contribution to the organization tangible, whether in terms of productivity or financial returns. The next step is then to expand a solution that has demonstrated its value to more users, processes, or departments.
To evaluate the success of a pilot, consider the following questions:
- How long did it take to complete the task?
- How accurate were the results?
- How many tasks required human intervention?
- Which outputs did users have to correct?
- What were the most common errors and exceptions?
- Did process owners find the results useful?
- Did the time savings and cost benefits align with the targets defined at the outset?
The purpose of a pilot is to identify what works within a small, manageable scope and test initial assumptions through real-world use. This makes it possible to evaluate the agent's contribution to productivity and financial performance more concretely.
From pilot to scale, one step at a time
Prove the result in a narrow scope, then widen it. Every new source, permission or integration is a new risk assessment.
One repetitive task in one department
More users and the neighbouring processes
Other departments, more sources and actions
Reassess the risks before each step
Measure at every step
- Time per task against the baseline
- Accuracy of the results
- Tasks that needed a person
- Time and cost saved against the target
How Harmona helps
In Harmona, an agent or workflow can be configured with a limited set of connections and integrations. Workflow execution history and usage logs can be monitored. The platform can also estimate time savings based on successful tasks and assumptions defined by the organization.
Once the pilot meets expectations, its scope can be expanded step by step. Whenever a new data source, permission, or integration is added, the associated risks should be reassessed.
What success looks like
Success in AI agent projects is not about deploying as many capabilities as possible at once. What matters is solving the right problem with reliable information sources and clearly defined boundaries. Unclear objectives, poor process selection, outdated sources, excessive permissions, insufficient human approval, inadequate traceability, and scaling without a pilot can quickly turn a promising project into a structure that is difficult to manage.
Harmona brings agents, connections, workflows, human approval, and traceability together in one environment, helping organizations put this approach into practice.
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