TDD as the Backbone of AI-Assisted Software Development

What Is a Mental Model in Software Development?

Software development is not just the act of producing code — it is fundamentally a form of knowledge work. Every meaningful action in development requires bridging a gap between what is known and what is needed to be known. This includes understanding the problem space, choosing appropriate abstractions, adapting to changing user requirements, and reasoning about the behavior of complex systems. In each case, progress depends not on output volume, but on acquiring, refining, and applying knowledge.

This is where mental models play a central role.

Developers rely on mental models constantly: of how the system behaves, how users think, how the CI pipeline operates, or how teammates approach their work.

For software engineers, it means holding a simplified but functional understanding of:

• Requirements – what the system is supposed to do.
• Code behavior – what the system actually does.
• System interactions – how components, dependencies, and external factors fit together.

These models allow engineers to predict outcomes, reason about trade-offs, and anticipate side effects before they happen. When a developer looks at a bug, sketches a design, or reviews a pull request, they are constantly updating and reconciling these mental models.

In short: mental models are the invisible frameworks that make software engineering possible. Without them, code is just text on a screen.

The distinguishing skill of great engineers is not just building these mental models, but continually refining them through a disciplined loop of coding and reflection.

The Hidden Skill of Great Engineers – Looping Through Mental Models

The real strength of effective engineers is not raw speed or encyclopedic knowledge of syntax—it is their ability to form and refine clear mental models through constant iteration.

When solving a problem, engineers naturally loop through a cycle:

1. Build a mental model of the requirements
2. Write code that (hopefully) does that
3. Build a mental model of what the code actually does
4. Identify the differences, and update the code (or the requirements)

This loop repeats constantly, allowing engineers to reason about trade-offs, anticipate side effects, and ensure code remains aligned with both immediate needs and long-term goals.

The distinguishing factor of effective engineers is not just their ability to type fast or know syntax. It is their ability to build and maintain clear mental models. These models let them reason about trade-offs, predict side effects, and ensure the code aligns with both current requirements and future needs. Without them, code may “work” locally but collapse under real-world conditions.

In short: mental models are the invisible frameworks that make software engineering possible. Without them, code is just text on a screen.

Why AI Falls Short

AI coding agents are remarkably capable at surface-level tasks. They can generate entire functions, scaffold APIs, suggest bug fixes, and even run tests or add logging when asked. On the surface, they mimic many of the activities human engineers perform.

But what they cannot do is maintain clear mental models.

They cannot carry forward a coherent understanding of intent. Each interaction resets them; they process the immediate tokens without retaining a persistent mental model of requirements, design goals, or trade-offs. Where engineers weave a continuous thread of reasoning across decisions, AI agents start fresh every time.

AI can write code, but it cannot remember why the code is being written.

Giving AI a Mind to Work With – Externalizing the Mental Model

If AI cannot maintain a mental model, then engineers must provide one. The question is: in what form?

Documentation describes intent but is often incomplete or outdated. Diagrams capture structure but not behavior. Comments are helpful but not executable. Only tests uniquely combine two properties: they describe expected behavior and they can be executed to verify it.

That is why tests are the most effective artifact to externalize our mental models.

For human engineers, tests are a natural checkpoint against their own mental models. For AI, tests become a stand-in for the understanding it lacks—a concrete, externalized model of what the system should do.

Tests are how we give AI a working approximation of the mental models engineers carry in their heads.

Test-Driven Development as the Answer

Test-Driven Development (TDD) has long been seen as a craft practice — a way for disciplined engineers to improve code quality and design clarity. In the age of AI coding agents, its role becomes even more critical.

• The tests provide the model of what needs to be built.
• The design, architecture, and standards provide the model of how it should be built.
• integration tests, property-based tests, and non-functional checks (like performance or security) add further detail, enriching the externalized model.

Together, they provide a layered, executable map of what the system should do.

Now AI coding agents can operate against both of these externalized models, spot mismatches, and decide whether to update the code or the tests.

But humans remain in the loop. Only the engineer can decide when requirements are wrong, when trade-offs need to shift, or when the models themselves must be rethought. AI supports the process—it does not own it.

Humans vs. AI – Managing Context at Different Scales

Tests cannot give AI true understanding. They only constrain behavior. A human still holds the ultimate integrated model that balances requirements, trade-offs, and long-term evolution of the system.

When a human developer runs into a problem, they can temporarily stash their full context, focus on resolving the issue, and then “pop the stack” to resume the bigger task. They can zoom out to the big picture, then zoom in to details, shifting perspectives fluidly.

AI agents cannot do this. They do not truly manage context—they just accumulate tokens until their context window overflows.

That is why externalized mental models matter. Tests and designs are not just guardrails; they are how we give AI a working approximation of what human engineers carry in their heads.

Practical Implication for Teams

AI assistance changes the expectations for how teams work. In a world where engineers collaborate with machines, casual or ad-hoc testing is no longer enough. Without a strong test suite, AI has no reliable model to work against — and its output cannot be trusted.

In short:

• TDD is no longer just a craft practice—it is a prerequisite for effective AI-assisted engineering.
• Tests are not overhead; they are the externalized mental models AI needs in order to be trustworthy.
• The human engineer remains accountable, orchestrating the loop between requirements, design, code, and tests.

A comprehensive, executable test suite becomes the backbone of efficient and effective use of AI coding agents. It enables AI to propose changes with confidence and allows engineers to validate those changes instantly. The tests act as a shared language, bridging the gap between human intent and machine execution.

To make the most out of AI, disciplined testing moves from “best practice” to “prerequisite.”

Conclusion

AI coding agents are powerful accelerators, but they cannot carry intent, context, or accountability. Those remain the domain of the human engineer. What engineers hold in their heads must be made explicit if AI is to be a trustworthy collaborator.

Tests externalize mental models, embody requirements in executable form, and create a shared reference point between humans and machines. With disciplined testing—especially through Test-Driven Development—AI can become a reliable partner rather than a risky shortcut.

In the end, tests are more than guardrails: they are the bridge that allows human judgment and machine assistance to work together productively.