How to Build an End‑to‑End Business‑Requirement Expert Agent

Problem: Context‑linking cost is the real bottleneck

Generating code with AI is no longer the main obstacle; the costly part is stitching together the entire requirement lifecycle (PRD, clarification, planning, implementation, CR, acceptance, release, and close‑out). Scattered tools and manual hand‑offs cause context loss and high human effort.

Overall Architecture: Organizing Context, Tools, and Feedback

The system is a business‑requirement handling platform that (1) organizes context, (2) orchestrates tools, (3) places human confirmation where needed, and (4) feeds back learning. It is decomposed into four layers forming a closed loop:

Context Input Layer : Retrieves long‑term business knowledge, current requirement materials, code facts, and runtime evidence. Proper context is essential; incomplete context leads to downstream errors.

Business‑Expert Orchestration Layer : Determines the next step (clarification, planning, implementation, etc.) and decides when human intervention is required.

Tool Execution Layer : Calls skills such as superai-aone, superai-clarify, superai-plan, superai-execute, and others to interact with Aone, code repositories, configuration platforms, SLS, HSF, monitoring, and DingTalk.

Feedback Learning Layer : Captures CR comments, issue dialogs, acceptance records, and logs, distilling stable knowledge into a long‑term wiki and generating improvement candidates for skills and prompts.

Vertical Flow: How a Requirement Is Processed

3.1 Requirement Intake – Organize Context : The Agent loads two sources: (a) current demand materials (Aone work item, DingTalk PRD, issue comments) and (b) long‑term business knowledge (LLM wiki, codemap). These form an initial context snapshot stored in three repositories: code repo, project‑memory repo, and long‑term wiki.

3.2 Clarification – First Quality Gate : Using the superai-clarify skill, the Agent structures the snapshot into a reviewable requirements document (goals, boundaries, acceptance criteria, risks). Humans only confirm the document, not individual answers.

3.3 Technical Planning – Second Quality Gate : The Agent produces a detailed plan via superai-plan, covering current state, target behavior, impact, risks, verification methods, and rollback strategy. Acceptance criteria are defined here to avoid later rework.

3.4 Implementation & Internal Quality Gates : Guided by TDD, the Agent modifies code, adds tests, and commits. Pre‑push quality gates enforce diff‑to‑test mapping, PMD/lint checks, and an internal code review ( superai-code-review) via a git pre‑push hook, making these checks non‑bypassable.

3.5 CR / Issue Collaboration : From requirement entry onward, CR comments are read and acted upon. The Agent resolves unresolved comments, updates documents or code, and may revert to earlier stages if scope changes.

3.6 Acceptance Verification : The Agent deploys the feature branch to an isolated environment and validates behavior using HSF calls, SLS log queries, and monitoring metrics. Acceptance covers positive cases, edge cases, and regression checks.

3.7 Release & Post‑Release Observation : After manual release confirmation, the Agent continues to monitor logs and metrics, feeding any anomalies back into the close‑out process.

3.8 Close‑Out – Knowledge Distillation : The Agent generates a raw log, classifies materials into stable knowledge (to be merged into the long‑term wiki), process‑improvement candidates (skill/prompt updates), and archival artifacts. The superai-finish skill orchestrates merging project‑memory branches and creating wiki change requests.

Current Issues and Future Plans

High onboarding cost : Initial authentication and runtime setup require manual effort; future CLI/API improvements will lower this barrier.

Lack of measurement framework : Proposes metrics for human effort, requirement quality, online stability, and collaboration efficiency to evaluate Agent improvements.

From single Agent to Agent Team : Envisions multiple specialized agents (frontend, backend, testing, PD) sharing a common long‑term wiki, coordinated by a team lead that aligns deliverables and contracts.

Conclusion

The presented system demonstrates that the repetitive tasks of context organization, tool orchestration, evidence collection, and feedback consolidation can be systematized into an AI‑driven closed loop, reducing human overhead and paving the way for fully autonomous requirement fulfillment.