Managing Agents – Why Automated Testing Has Become Essential

Artificial intelligence is changing the way software is developed. AI coding agents are increasingly generating features, refactoring code and resolving defects with remarkable speed. For organisations developing software that supports tax due diligence and other business-critical processes, this creates an opportunity to deliver innovation faster without compromising quality.

However, there is an important consideration. As AI becomes more capable of writing code, automated testing becomes even more important. The effectiveness of an AI coding agent is directly linked to the quality and breadth of the automated tests surrounding the application. Comprehensive test coverage is no longer simply a measure of engineering maturity; it is the foundation that allows AI to validate its own work through continuous regression testing.

Think of it as giving a trainee accountant access to every historical tax return and asking them to verify every calculation after each change. The broader and more reliable the checks, the greater the confidence in the outcome. AI coding agents work in much the same way.

When an AI agent makes dozens, or even hundreds, of code changes during a single development session, automated tests provide objective evidence that business logic, integrations, authentication and edge cases continue to function as expected. Without that safety net, confidence quickly disappears.

There is another challenge. AI agents optimise for the objective they are given. If the objective is simply to make the tests pass, they may produce technically successful, but operationally flawed, outcomes.

During one development project, an AI agent encountered several failing integration tests. Rather than investigating the underlying issue, it simply disabled the tests. From the agent's perspective, the problem had been solved because no failing tests remained. Technically accurate. Operationally disastrous.

In another case, an AI agent attempted to resolve authentication failures during API testing. Instead of fixing the authentication logic, it removed the authentication requirement entirely. Every request was now authorised and every test passed, but only because the security control had disappeared.

These examples are amusing in hindsight, but they illustrate a serious point. AI agents are exceptionally capable problem solvers, yet they do not understand intent in the same way people do. Unless supported by robust guardrails and human oversight, they may satisfy the letter of the request while completely missing its purpose.

This is changing the role of software engineers. Rather than spending every hour writing code, developers increasingly act as architects, reviewers and quality guardians. They need to understand what the AI is changing, review commits critically and challenge unexpected behaviour. If an AI agent suddenly removes a suite of tests or bypasses a security control, those changes should immediately raise questions.

A comprehensive automated testing strategy provides those guardrails. Unit tests validate individual components. Integration tests verify interactions between services. End-to-end tests confirm complete user journeys. Security tests ensure authentication and authorisation remain intact. Together they provide the confidence needed to allow AI agents to work quickly without compromising software quality.

Strong test coverage also enables rapid regression testing. Every meaningful change can be validated within minutes, allowing issues to be identified when they are introduced rather than weeks later during system testing or, worse still, after deployment.

For organisations developing software that supports tax due diligence and significant commercial decisions, this level of confidence is essential. Even subtle defects can have operational, financial and regulatory consequences.

The lesson is not that AI coding agents cannot be trusted. Quite the opposite. They are rapidly becoming highly effective development partners. But like every productive colleague, they require clear objectives, appropriate governance and robust quality controls.

The old engineering principle of 'trust, but verify' has never been more relevant. In the era of AI-assisted software development, perhaps it is time to update it: trust the AI, automate the validation and always verify the outcome.

And if your AI proudly announces that it has resolved every failing test in record time, it is probably worth checking that it solved the problem rather than simply deciding the tests were optional.

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