Adaptive AI Requirement Gathering Agent for Faster Delivery
Challenges
AI use cases were outpacing requirements gathering.
Critical requirements were often missed.
Teams lacked a consistent discovery process.
Outcome
Faster, more consistent AI use-case onboarding.
Fewer requirement gaps and less rework.
Faster progression from idea to go-live.
Solution
AI Requirement Gathering Agent
Challenges
Solution
Technology Stack
Outcomes
One of the top UK organisations had a growing pipeline of AI use cases coming from business teams across customer-facing applications, colleague-used applications and numerous operational tools. The challenge was getting each idea defined properly before engineering started. Teams gathered requirements manually through calls and documented them across Word files and Confluence pages scattered in different locations. Important details were missed. By the time those gaps surfaced, teams had to revisit the requirements and step back in the delivery process.
Cloudaeon built a Requirement Gathering Agent that changed this process. The agent identifies the type and complexity of an AI use case, adapts its questions based on the answers it receives and produces a detailed requirements document.
Challenges
AI Use Cases Were Arriving Faster Than Teams Could Define Them
The organisation was receiving a large number of AI use cases that needed to be delivered within short periods of time. Each use case still needed enough detail for engineering teams to build it accurately. The manual requirement-gathering process was becoming a bottleneck before development even started.
Requirement Documents Regularly Missed Important Information
Teams created requirement documents and found out later that important details had not been captured. Those gaps appeared when the use case had already moved further into delivery. Requirements then had to be revisited, creating additional discussions and pushing the work backwards.
Different Teams Followed Different Requirement Patterns
There was no consistent way to gather requirements across AI use cases. Different teams used different working methods. Information could sit in calls, Word documents, and Confluence pages.
Fixed Templates Could Not Adapt to Different AI Use Cases
Existing requirement templates were too rigid. A RAG application does not require exactly the same discovery path as an agentic application or an automation use case. More complex use cases could also involve combinations of different patterns. A static template could therefore ask the wrong questions or fail to ask an important follow-up question altogether.
Root Cause Analysis
Cloudaeon’s team did not approach this as a document generation challenge. The main issue was the way requirements were being gathered. The key requirement was therefore not simply to digitise an existing questionnaire. The questioning itself had to become adaptive. That became the basis of the solution.
Solution
Cloudaeon built an AI-powered Requirement Gathering Agent that conducts an adaptive discovery process with the business user. The agent starts with a small number of predefined questions. It uses the responses to classify the use case by type, such as RAG, agentic or automation and by complexity from L1 to L5. For more complex requirements, the framework can account for combinations of use case types rather than forcing the requirement into a single category. From there, the agent changes the course of questioning based on the information it receives. The final output is a detailed use case requirement document that provides a common reference for business users, platform engineers, AI engineers and QA.
How We Delivered
Created the Use Case Classification Framework: We first defined a framework to classify AI use cases by type, including RAG, agentic and automation and by complexity from L1 to L5. The framework also supported combinations where a use case involved more than one pattern.
Used Initial Questions to Determine the Discovery Path: The agent begins with a small set of static questions. These responses help determine the use case type and complexity level. That classification then decides which questions should follow.
Built Adaptive Questioning with LangGraph: We used LangGraph to control the questioning workflow. Instead of following a fixed questionnaire, the agent evaluates each response and changes the next question based on the information provided by the business user. This allowed the discovery process to adapt as new requirements or dependencies emerged.
Added Reasoning with Anthropic Models: Reasoning enabled Anthropic models support the agent's decisions during the conversation. They help determine how the questioning path should change as the use case becomes clearer.
Generated a Shared Requirement Document: The final output is a detailed use case requirement document. It gives business users, platform engineers, AI engineers and QA teams a common definition of what needs to be built before development progresses.
Technology Stack
LangGraph
Anthropic reasoning enabled models
RAG
Outcome
The Requirement Gathering Agent gave the client a more consistent way to onboard AI use cases with more complete information.
Teams now spend less time going back and forth over missing requirements.
The organisation reported higher accuracy in implementation.
Most importantly, teams could spend more of their time developing the use case rather than repeatedly revisiting its definition. This helped them move use cases towards go live faster.
Conclusion
Cloudaeon replaced an inconsistent, manual process with an adaptive Requirement Gathering Agent that classifies each use case, changes its questions as requirements emerge and creates a shared requirement document. The result is less back and forth, better information before development starts, and more time available to actually build and launch AI use cases.
If your AI teams are still gathering requirements through repeated calls, scattered documents or rigid templates, Cloudaeon can help turn that process into an adaptive requirements workflow that gets business and engineering teams aligned before development begins. Talk to an expert now.