An AI agent can pass every test you give it and still create serious problems after deployment.
It might select the wrong customer record, trigger an unnecessary workflow, call an API with incorrect parameters, or continue taking actions when it should have stopped and asked for human approval.
The frustrating part is that the agent may not actually be broken.
The problem is often the environment in which it operates.
During testing, developers usually provide controlled inputs, predictable data, limited permissions, and clearly defined tasks. Production environments are different. They contain incomplete information, unexpected user requests, changing data, API failures, and situations that were never included in the original test cases.
This creates an important engineering challenge:
Building an AI agent that works is not the same as building an AI agent that can be trusted in production.
Let's look at why these failures happen and what developers can do to prevent them.
Consider an AI agent designed to process customer support requests.
In development, it might receive requests like:
The agent identifies the intent, retrieves the relevant information, and returns an appropriate response.
Everything works.
But real users rarely follow predictable patterns.
A customer might send an incomplete message, combine multiple requests, provide an incorrect order number, or ask for something outside the agent's permissions.
For example:
"Cancel my order, but only if it hasn't been shipped. Also, update my delivery address if cancellation isn't possible."
This request requires the agent to understand conditions, retrieve accurate information, and choose the correct action.
A simple happy-path test might never reveal whether the agent can handle this safely.
What developers should do:
Test realistic and difficult scenarios, not just successful ones.
Include:
The goal is not simply to prove that the agent can complete a task. It is to understand what happens when it cannot.
One common mistake is providing an agent with more access than it actually needs.
Imagine an agent responsible for managing customer records.
It needs permission to:
But it also receives permission to delete customer records, modify billing information, and change account ownership.
Even if the model is generally reliable, these additional permissions increase the consequences of a wrong decision.
A better approach is the principle of least privilege.
Give each agent only the permissions required for its assigned responsibilities.
| Action | Suggested control |
|---|---|
| Read customer record | Allow |
| Add internal note | Allow |
| Update contact details | Validate before execution |
| Delete customer record | Human approval |
| Change account ownership | Human approval |
The important distinction is that an AI agent should not automatically receive every permission available to the user or system account.
Its access should be designed around its actual role.
AI agents often use tools to complete multi-step tasks.
A typical workflow might look like this:
The risk is that one incorrect decision early in the process can affect every step that follows.
For example, an agent might identify the wrong customer record. It then retrieves that customer's information and updates the wrong account.
The API call may succeed.
The workflow may even report that the task was completed successfully.
But the outcome is still incorrect.
This is why successful tool execution should never be treated as proof of a correct business outcome.
A useful engineering principle: validate important decisions before allowing them to trigger consequential actions.
Developers can introduce validation between steps:
def execute_customer_update(customer, changes):
if not customer:
return {
"status": "rejected",
"reason": "Customer not found"
}
if not validate_customer_changes(changes):
return {
"status": "rejected",
"reason": "Invalid changes"
}
if changes_require_approval(changes):
return {
"status": "pending_approval",
"changes": changes
}
return update_customer_record(customer, changes)
This is an illustrative control pattern. The validation, approval, and update functions must be implemented according to the application's actual data model and security requirements.
There is a common assumption that automation becomes more valuable when humans are removed from every step.
That is not always true.
Some actions are low-risk and repetitive. Others can affect customer accounts, financial records, access permissions, or business operations.
A useful approach is to classify actions by their potential impact.
Low-risk actions
These may be suitable for automatic execution, depending on the application.
Medium-risk actions
These should have appropriate validation and monitoring.
High-risk actions
These may require explicit human approval before execution.
The important point is that approval requirements should be determined by the consequences of an action, not simply by how confident the AI sounds.
Many development teams focus heavily on pre-deployment testing.
But an AI agent's behavior can change when its environment changes.
An agent that worked reliably last month may behave differently after these changes.
This makes continuous monitoring essential.
At minimum, developers should track:
| Metric | Why it matters |
|---|---|
| Task success rate | Measures completed tasks |
| Tool-call failures | Identifies integration problems |
| Invalid actions | Reveals decision or validation issues |
| Human intervention rate | Shows where automation needs assistance |
| Latency | Helps identify performance problems |
| Repeated actions | Helps detect duplicate execution |
| Escalation rate | Measures when agents cannot safely proceed |
Monitoring should also include enough context to investigate failures.
A useful event record might include:
{
"agent_id": "support-agent-01",
"task_id": "task-1042",
"action": "update_customer_record",
"approval_required": true,
"approval_status": "pending",
"execution_status": "not_executed"
}
Do not log sensitive information unnecessarily. Apply access controls, retention limits, and appropriate data protection measures to agent logs.
No production system is completely immune to failure.
An external service may become unavailable. A request may time out. A tool may return incomplete data.
The important question is not whether a failure will happen, but what the system will do when it happens.
Consider an agent that sends a payment-related request to an external service.
The request times out.
Should the agent immediately send the same request again?
Not necessarily.
The original request may have succeeded even though the response never reached the application. Sending it again could create a duplicate transaction.
Developers should consider:
def process_action(action, idempotency_key):
existing = find_previous_action(idempotency_key)
if existing:
return existing
try:
result = execute_action(action)
save_action_result(idempotency_key, result)
return result
except TimeoutError:
return {
"status": "unknown",
"message": "Verify execution before retrying"
}
This is a simplified illustration. In production, the idempotency record and business operation need appropriate atomicity and concurrency protection.
An AI agent is more than a model.
It includes multiple components:
Testing only the model's responses ignores many of the places where real failures occur.
A more complete testing strategy should include:
Unit testing: Test individual functions and validation rules.
Integration testing: Verify that tools, APIs, and databases communicate correctly.
Scenario testing: Test realistic user requests, including unusual and conflicting inputs.
Security testing: Check whether agents can access or modify resources beyond their permissions.
Regression testing: Repeat important tests whenever prompts, tools, models, or business rules change.
Failure testing: Simulate timeouts, unavailable services, malformed responses, and interrupted workflows.
A system should be evaluated based on its complete behavior, not just the quality of its generated text.
Before deploying an AI agent, developers and engineering teams should be able to answer these questions:
If several answers are no, the system may need more engineering work before it is ready for production.
AI agents are becoming increasingly capable, but capability alone does not make an automated system reliable.
Production readiness depends on the engineering surrounding the model: permissions, validation, monitoring, testing, failure recovery, and human oversight.
This is also an important consideration for organizations working in AI automation, including Octacer: automation should be evaluated not only by what an agent can accomplish, but also by whether its actions remain understandable, controlled, and accountable.
The best AI agent is not necessarily the one that performs the most actions without human involvement.
It is the one that knows what it is allowed to do, recognizes when something is uncertain, and stops when an action requires additional verification.
That is the difference between an AI agent that works in a demo and one that is ready for production.
Disclosure: This article was prepared with AI assistance and should be reviewed by the author before publication. The author is associated with Octacer; the company name is included for affiliation transparency, not as a product recommendation. The technical examples are illustrative rather than claims of personal production experience.