When a development team uses artificial intelligence to generate or modify code, they often face a subtle yet dangerous problem: the AI produces changes that look correct but are based on wrong assumptions. The resulting code can be clean, unit tests may pass, yet the functionality ends up being incorrect in production. Detecting that error requires reviewing not the code itself, but the premises the AI assumed before writing it. This article explains why it is crucial for AI systems, especially AI agents applied to software development, to explicitly list their assumptions before modifying any file.
The core idea is simple: before an AI tool makes a code change, it should stop and list up to five key assumptions it is making. For each assumption, it must provide evidence extracted from the current project, the risk if that assumption is false, and what needs further verification. This creates a pause between thinking and action, allowing the developer to review the foundations before code is generated. This practice fits perfectly with secure development methodologies and the implementation of cybersecurity systems in cloud environments, such as those offered by Q2BSTUDIO in its cloud AWS and Azure services.
Let us take a classic example: the AI is asked to add automatic retries to failed payment status checks. The AI might assume that every failed request is safe to retry, that a timeout means no work was done, or that two simultaneous requests cannot cause duplicates. These assumptions can be catastrophic in a financial system. Instead of reviewing the generated code, the developer should have reviewed those initial beliefs. Making the AI explicit allows opening the source file, reading existing tests, and verifying behavior in edge cases. This approach is especially relevant when developing custom software where business rules are unique and flexibility is key.
Now imagine that the AI assumes a database write operation is idempotent because the function name contains 'update'. In a custom application developed by Q2BSTUDIO for inventory management, that assumption could be false and generate duplicates. Real evidence would be found in the code handling concurrency conflicts or in integration tests. That is why it is essential that the AI not only lists assumptions but backs them with concrete project evidence. Unit tests that name the behavior, current configuration values, design notes, or existing code that already handles the same case are examples of good evidence. Phrases like 'this is usually safe' or 'most systems work this way' are not evidence, but guesses in disguise.
The practical implementation of this technique requires that the prompt sent to the AI explicitly includes the instruction: 'Before editing code, list up to five assumptions you are making. For each, include: 1) the assumption, 2) evidence from the project, 3) risk if wrong, 4) what needs verification. Do not write code yet.' The last line is crucial because many tools list assumptions and then proceed to code without waiting for confirmation. The pause between thinking and action is what allows the developer to review and validate before the change materializes.
In cloud environments like AWS or Azure, where resources are dynamically provisioned and configurations can vary, assumptions about availability, latency, or consistency are critical. An AI agent managing auto-scaling must verify that load metrics are correctly configured and that retry policies are appropriate. Q2BSTUDIO, through its cloud AWS and Azure service, helps companies design robust architectures where AI can operate transparently and securely.
For teams using Business Intelligence with Power BI, assumptions about cardinality of relationships or the behavior of DAX functions can lead to incorrect reports. Listing assumptions before modifying measures or tables avoids interpretation errors that affect strategic decisions. Integrating AI in these processes must include verification of underlying business rules, something Q2BSTUDIO addresses in its BI projects.
Cybersecurity is another area where unverified assumptions can open vulnerabilities. A change in authentication token handling might assume the token is always valid after being verified once, or that the session expires based on a fixed time. Without evidence of the current expiration and renewal policy, a security flaw could be introduced. Therefore, Q2BSTUDIO integrates good cybersecurity practices in all its developments, including controlled use of AI tools.
Not all edits require this level of scrutiny. For trivial changes like fixing a typo or renaming a variable, the cost of listing assumptions outweighs the benefit. The technique is especially valuable when the change affects data writes, access or authentication rules, scheduled jobs that could run twice, public APIs, error handling, or legacy code with few tests. In those cases, a wrong assumption can have serious consequences: corrupted data, security breaches, or service unavailability.
The goal is not perfect certainty, but to make unknowns visible before they become code. Once assumptions seem solid, the AI can be authorized to continue. If evidence is missing, it is better to stop and verify first. This small change transforms the review: we no longer ask only 'Does this code look right?' but also 'Did it start from correct facts?'.
At Q2BSTUDIO, as a software and technology development company, we apply this principle in every project involving AI, whether in creating intelligent agents, process automation, or integration with cloud platforms. We believe transparency in algorithmic decisions is a pillar of software quality. By requiring the AI to list its assumptions, we align the power of automatic generation with the rigor of human review, obtaining more reliable and secure code.
