Why reliable AI applications need validation, recovery mechanisms, and clear boundaries—not just intelligent models.
Artificial intelligence is becoming part of everyday software engineering. We are using AI to analyze documents, answer technical questions, generate code, summarize information, and automate business processes.
The capabilities are impressive. But there is an important engineering question that deserves equal attention:
What happens when the AI gives the wrong answer?
In traditional software, we generally expect the same input and conditions to produce predictable results. A database constraint, a validation rule, or a calculation can be tested against an expected outcome.
AI systems introduce another dimension. A model may generate a plausible but incorrect answer, misunderstand an instruction, omit an important detail, or produce a response that looks convincing despite containing an error.
This does not mean AI is unsuitable for production. It means we must design AI-powered applications with failure in mind.
1. A convincing answer is not necessarily a correct answer
Consider an AI-powered application that analyses a software repository and prepares release notes.
The system examines commits, identifies changes, and generates a summary for the release team.
Now imagine that a commit contains a small but important security fix. The AI overlooks it and produces release notes that look professional and complete.
The problem is not the writing quality. The problem is that an important change has been missed.
A similar issue can occur in customer support, document processing, technical research, financial reporting, and many other applications.
The output may be grammatically correct, logically structured, and still be wrong.
Therefore, we should evaluate AI output based on correctness and business impact, not merely presentation quality.
2. Separate AI reasoning from deterministic business rules
One of the most useful architectural principles is to give AI and conventional software different responsibilities.
AI is well suited to tasks such as:
Summarising large volumes of text.
Classifying unstructured information.
Extracting candidate insights.
Drafting explanations and recommendations.
Identifying patterns that deserve further investigation.
Conventional application logic should handle tasks such as:
Authentication and authorisation.
Database constraints and data integrity.
Mandatory field validation.
Permission checks.
Financial calculations.
Approval requirements.
Publishing conditions and transaction boundaries.
For example, an AI model can recommend that a document is ready for publication. The application should still verify that the required approval has been recorded before publishing it.
The model should not be able to bypass that rule simply because it generates a response saying that approval is complete.
This separation makes the overall application easier to test, maintain, and secure.
3. Validate the output before acting on it
AI-generated responses should be treated as proposed data, not automatically trusted instructions.
Suppose an AI component is expected to return a release summary with a title, a list of changes, known issues, and a release identifier.
Instead of accepting arbitrary text, the application can require a defined structure.
For example:
{
"title": "Release Summary",
"changes": [
{
"description": "Improved login validation",
"category": "security",
"source_commit": "abc123"
}
],
"known_issues": []
}The application can then validate the response against a schema and apply additional business checks.
Questions worth asking include:
Are all mandatory fields present?
Are the values in the expected format?
Does each reported change correspond to an actual source?
Are any required items missing?
Does the proposed action comply with business rules?
Structured output and schema validation reduce formatting errors, but they do not prove that the underlying claims are true. Source verification remains necessary.
4. Design for recovery, not just success
A production AI workflow should have an explicit strategy for handling failures.
Consider a document-processing workflow:
Receive a document.
Extract relevant information.
Ask the AI to classify and summarise it.
Validate the generated result.
Store the result.
Send it for review or continue processing.
What should happen if the model times out at step three? What if it returns invalid data? What if the database operation succeeds but the next step fails?
These are ordinary software engineering questions, and they deserve ordinary engineering discipline.
Useful mechanisms include:
Timeouts: Prevent a slow model response from blocking a workflow indefinitely.
Bounded retries: Retry transient failures a limited number of times, with suitable delays. Repeatedly retrying a fundamentally invalid response will not necessarily fix it.
Idempotency: Ensure that retrying an operation does not create duplicate records, send duplicate messages, or publish the same content twice.
Dead-letter handling: Preserve failed jobs for investigation and controlled reprocessing.
Fallback paths: Route uncertain results to manual review or use a simpler, deterministic alternative where appropriate.
Audit logs: Record the input references, model and prompt versions, validation results, decisions, and failures needed for troubleshooting.
The objective is not to eliminate every failure. It is to ensure that failures remain visible, controlled, and recoverable.
5. Use human approval where the consequences justify it
Not every AI output needs human approval. Requiring someone to review every low-risk classification or internal summary can remove much of the efficiency that automation offers.
However, human review becomes important when an incorrect result could have a significant business impact.
For example:
Publishing a public statement.
Approving a sensitive business transaction.
Making a consequential decision about a person.
Deploying a production software release.
Changing access permissions or security settings.
A useful approach is risk-based approval.
Low-risk operations can proceed automatically after validation. Higher-risk operations can require explicit approval, additional evidence, or a second verification step.
The important distinction is that human approval must be a real application-level control. It should not depend solely on the AI remembering to ask permission.
6. Measure failure, not just response time
Many AI prototypes are evaluated using a few successful examples. Production systems need a more systematic approach.
In addition to response time and cost, consider tracking:
Schema validation failure rate: How often does the model return an unusable response?
Grounding accuracy: How often are claims supported by the available source material?
Task completion rate: How often does the entire workflow finish correctly?
Retry and timeout rate: How frequently do requests fail or need recovery?
Human correction rate: How often must reviewers modify the result?
Duplicate action rate: Are retries causing repeated side effects?
Cost per successful task: What does a genuinely completed task cost, rather than a single model call?
These metrics should be evaluated against representative test cases, including difficult and unexpected inputs.
A system that responds quickly but produces unreliable results may be more expensive in the long run because of rework, operational incidents, and loss of trust.
7. Test the boundaries, not only the happy path
Before deploying an AI feature, test situations where the input is incomplete, contradictory, misleading, or outside the expected scope.
For a document-analysis application, tests might include:
A document with missing sections.
Two documents containing conflicting information.
An unsupported file format.
A prompt containing instructions that conflict with the application's rules.
A model response that is valid JSON but contains incorrect claims.
A temporary model-provider outage.
A repeated job that attempts to perform the same action twice.
This is where conventional testing and AI evaluation complement each other.
Unit tests can verify deterministic rules. Integration tests can verify system behaviour. Representative evaluation datasets can measure model quality. Adversarial tests can explore how the application behaves under hostile or unexpected conditions.
No single test suite guarantees correctness, but together these practices provide much stronger protection than testing only successful examples.
8. Treat AI as one component in a larger engineering system
An AI application is more than a model and a prompt.
It includes input handling, context retrieval, permissions, business rules, data storage, external integrations, monitoring, and recovery mechanisms.
A useful design principle is:
Let AI propose. Let the application verify. Let controlled workflows execute.
For example, an AI model may recommend a change to a software release. The application can verify the recommendation against repository data, apply release policies, request approval where required, and record the final decision.
This architecture does not eliminate AI errors. It prevents many individual errors from automatically becoming business actions.
Conclusion
The future of AI in software engineering will not be determined solely by which model produces the most impressive response.
It will also depend on how well we build the systems around those models.
Reliable AI applications need clear boundaries, verifiable outputs, controlled permissions, recovery mechanisms, meaningful monitoring, and appropriate human oversight.
These are not obstacles to innovation. They are the engineering practices that allow innovation to move from a demonstration into dependable business software.
When designing an AI-powered application, it is worth asking two questions:
How will the system behave when the AI is right? And, more importantly, what will happen when the AI is wrong?
The answer to the second question is often what separates an interesting prototype from a production-ready system.