How an Agentic AI Assistant Enables AI Powered Requirements Extraction from Source Code
Introduction
There is a particular kind of frustration that most development teams know well. Someone joins the project and asks where the requirements documentation is. Someone else points to a folder of half-finished user stories, a few outdated wiki pages, and a suggestion to just read the code.
Reading the code is, in a way, the honest answer. The code is the most accurate record of what the system actually does. But it takes time, experience, and a lot of context to read it that way. And even when someone does, they are doing it manually, section by section, building up a picture that someone else will have to rebuild again next time.
This is the documentation gap. It is not caused by negligence. It is caused by the pace at which software actually moves. Requirements get written before development starts. Code evolves through dozens of sprints. The gap between the two grows quietly, and by the time anyone needs to close it, the work involved feels enormous.
Sanciti RGEN approaches this problem from a different direction. Rather than asking teams to manually trace what the code does and write it down, it reads the code itself and produces the documentation. Requirements, use cases, test cases — pulled directly from what is already there.
Why Requirements are Always Running Behind
Ask any product manager or business analyst on a fast-moving team how current their requirements documentation is, and you will get one of two answers. Either they will say it is reasonably up to date — meaning the last major sprint captured most of what changed — or they will pause and say something like it depends which part of the system you mean.
That pause is the real answer. Requirements documentation in most teams is current for the areas that have been actively worked on recently. Everything else exists somewhere on a spectrum between slightly outdated and completely undocumented.
The reason is straightforward. Writing requirements takes time. Keeping them current takes even more time, because every change to the code requires a corresponding change to the documentation, and that work competes with the work of actually building things. In most sprint cycles, when something has to give, it is documentation.
The downstream effects accumulate. A new engineer spends their first two weeks piecing together how the system works rather than contributing to it. An audit requires documentation that does not exist in the form it needs to. A feature request lands in an area of the codebase that nobody has touched in eighteen months and whose behavior is largely undocumented. Each of these situations is time-consuming, and all of them have the same underlying cause.
What It Actually Means to Read Code for a Living
There is a specific type of engineer on most teams — often a senior developer or a tech lead — who becomes the unofficial keeper of system knowledge. They are the person people go to when they need to understand how something works. They can trace a flow through the codebase, explain why a particular decision was made three years ago, and predict where a change is likely to cause a problem.
This knowledge is genuinely valuable. It is also fragile, because it exists in a person rather than in a document. When that person moves to a different team, takes a holiday, or leaves the company, the knowledge goes with them.
The problem is not that teams fail to document things. The problem is that documenting things from code is slow, painstaking work. You have to read function by function, trace dependencies, map out how different parts of the system interact, and then translate all of that into language that a product manager or a QA engineer or an auditor can actually work with. It takes the kind of deep familiarity that only comes from spending a lot of time with the codebase.
Most teams do not have the capacity to do this regularly. They do it when they absolutely have to, which means they do it reactively — when someone has already asked a question that the documentation cannot answer.
How Sanciti RGEN Approaches the Problem
Sanciti RGEN starts where the most reliable source of truth already is: the code. Rather than asking engineers to describe what the system does, it reads what the system does and builds documentation from that.
The way it works is through what Sanciti describes as an Agentic AI Assistant — a system that does not simply scan code for keywords or patterns, but follows the logic of the codebase the way an experienced engineer would. It maps dependencies. It understands how functions relate to each other. It identifies what the system is actually doing in terms that can be translated into requirements language.
The output is not a raw code summary. It is structured documentation — requirements artifacts, use cases, and test cases that are connected to the code they describe. For teams dealing with legacy systems where documentation has drifted significantly from reality, this is particularly useful. RGEN can reverse-engineer what the system actually does and produce documentation from that, even when the original requirements are long gone or never existed in a useful form.
For teams building new systems, it keeps documentation in sync with development automatically. As the code changes, RGEN updates its understanding. The documentation does not fall behind the way it does when it is maintained manually.
Use Cases That Come from the Code Itself
Use cases are supposed to describe how a user interacts with a system to achieve something. In theory, they bridge the gap between what the business needs and what the code delivers. In practice, they are often written before development starts, edited once or twice during the project, and then quietly abandoned as the system evolves past them.
The result is that by the time a system is mature, its use cases describe the version that was planned, not the version that was built. Teams who need to understand what the system actually supports have to go back to the code anyway.
RGEN's AI Use Case Generation takes a different approach. It derives use cases from the behavior of the code itself — mapping the logic of what the system does into real-world scenarios that describe how a user would actually move through it. These are not hypothetical flows written before development. They are descriptions of what the system does right now, in language that a product manager or a QA engineer or a business stakeholder can read and understand.
This matters particularly for onboarding. When a new team member needs to understand what the system supports, they can read the use cases RGEN has generated rather than spending days tracing the code. The documentation reflects reality because it was built from reality.
Test Cases Without the Manual Translation Work
Writing test cases from requirements is one of those tasks that sounds straightforward but takes considerably more time than anyone estimates. You have to understand the requirement well enough to know what needs to be tested, then translate that understanding into specific scenarios, then write those scenarios in a format that the testing framework can use.
When requirements are out of date, the situation is worse. QA engineers have to figure out what the system actually does before they can test it, which means they end up doing a significant amount of the analysis work that the requirements were supposed to have already captured.
RGEN's AI Test Case Generation generates test cases directly from the code and from the requirements and use cases it has already produced. Because everything is connected — the code, the requirements, the use cases, and the test cases — there is full traceability. If a part of the system changes, the test cases connected to it can be identified and updated, rather than relying on a QA engineer to manually work out what has changed and what needs to be retested.
The test cases RGEN produces can include unit, regression, and performance scenarios depending on the project configuration. Teams still review and adjust them — the judgment of an experienced QA engineer is not replaced. But the baseline work of translating code behavior into testable scenarios is done automatically, which means QA time goes toward the parts of testing that actually need human attention.
What This Changes for the Teams Doing the Work
The way documentation work typically gets distributed on a development team is not particularly efficient. Senior engineers spend time explaining context that should be written down. Business analysts spend time chasing requirements updates after sprints. QA engineers spend time figuring out what to test because the requirements do not reflect what was actually built. Project managers spend time on documentation during audits or compliance reviews that could have been prepared automatically.
When AI powered requirements extraction handles the work of reading code and producing documentation, several of those tasks change character. Senior engineers spend less time being the human documentation system and more time on the technical decisions that actually need their experience. Business analysts get requirements that reflect the current state of the system rather than its intended state from two sprints ago. QA engineers start from a documented baseline rather than having to construct one.
The agentic AI requirements assistant that sits inside RGEN also gets more accurate over time. It learns the patterns of a specific codebase — the way functions are named, the conventions used, the architecture that shapes how everything fits together. The longer it runs against a codebase, the more precisely it understands it, and the more useful the documentation it produces becomes.
Where Sanciti RGEN Fits in the Development Lifecycle
One of the practical considerations when thinking about a tool like this is where it actually fits into existing workflows. RGEN is built to integrate with the tools teams already use — IDEs, CI/CD pipelines, version control systems, and DevOps tools. It does not require teams to change how they work in order to benefit from it.
For teams in the middle of a modernisation effort, it is particularly useful. Legacy codebases often contain years of behavior that was never properly documented, and the work of documenting it manually before migration is one of the reasons modernisation projects take so long. RGEN can produce that documentation from the existing code, which means teams go into a migration with a clearer picture of what they are moving and what it needs to do on the other side.
For teams running audit-heavy processes — regulated industries, enterprise compliance requirements, security reviews — the ability to produce structured, traceable documentation from code on demand is genuinely useful. The documentation is connected to the code it describes, which means it can be regenerated whenever the code changes, rather than being a static document that gradually falls out of sync.
The agentic requirement generator aspect of RGEN also means it is not a one-time tool. It runs continuously against the codebase, keeping documentation current as development progresses. Teams do not have to decide when to update documentation — it updates as they build.
Conclusion
The documentation gap is not a new problem. It has existed as long as software development has, and it has never been solved by asking teams to write better documentation. The pace of development is too high and the maintenance burden is too continuous for manual documentation to keep up.
Sanciti RGEN takes a different approach. It reads what the code does and produces documentation from that — requirements, use cases, test cases, all connected to the code they describe. For legacy systems, it closes gaps that have been building for years. For active development, it keeps documentation current without anyone having to maintain it manually.
The teams that benefit most are the ones where the gap between what the code does and what the documentation says has become a real cost — in onboarding time, in audit preparation, in testing overhead, in the engineering hours spent explaining things that should have been written down. Those costs do not disappear on their own. But they can be addressed from the code side, where the most accurate information already lives.
Frequently Asked Questions
Q1. What does Sanciti RGEN actually do with my code?
RGEN reads your codebase and produces structured documentation from it — requirements artifacts, use cases, and test cases. It follows the logic of your code rather than just scanning it, which means the output describes what your system actually does rather than summarising its structure. For codebases that have grown without documentation, it can reverse-engineer that documentation from the existing code.
Q2. Does it work on legacy codebases with no existing documentation?
Yes, that is actually one of the situations where RGEN is most useful. Older systems often contain years of behavior that was never written down, and RGEN can produce documentation from the code itself without needing existing requirements to work from. Teams going into a migration or modernisation effort find this particularly helpful because it gives them a clear picture of what the system does before they begin.
Q3. Can it connect to the tools our team already uses?
RGEN integrates with the tools most development teams already have in place — IDEs, CI/CD pipelines, version control systems, and DevOps tooling. You do not need to change your existing setup to use it. It fits into the workflow rather than replacing it.
Q4. Do engineers still need to review what RGEN produces?
Yes, and that is by design. RGEN handles the mechanical work of reading code and translating it into documentation. Your team still reviews, adjusts, and makes the calls that need judgment and context. The value is that the baseline work is done — engineers are reviewing documentation rather than creating it from scratch.
Q5. How does the test case generation connect to our QA process?
The test cases RGEN generates are linked to the code and the requirements they relate to, which means there is full traceability. When something in the codebase changes, the connected test cases can be identified and updated. QA engineers still control the process and make final decisions, but they start from a documented baseline rather than having to build one manually before testing can begin.
Q6. What kinds of test cases does RGEN produce?
Depending on how the project is configured, RGEN can generate unit, regression, and performance test scenarios. These are derived from the actual code behavior and the use cases it has mapped, which means they reflect what the system does rather than what it was originally planned to do.
Q7. Does RGEN stay current as we continue developing?
Yes. RGEN runs continuously against the codebase and updates its understanding as the code changes. Documentation does not fall behind the way it does when it relies on manual updates. Over time, it also becomes more accurate for your specific codebase as it learns the patterns and conventions your team uses.