Source-Grounded Requirements Review Agent

Editorial disclosure: This is a representative engagement scenario based on common engineering constraints. It does not describe a named client, claim completed work for a specific organization, or present invented performance figures. Replace intended outcomes with approved, measured results only when they are available.

Context

An engineering organization reviews requirements across documents, tickets, standards, and product variants. The work is repetitive but high impact: reviewers must find unclear language, missing acceptance criteria, conflicts, and absent source links.

The engagement would create a review assistant that gathers approved context, proposes structured findings, cites its sources, and leaves approval with accountable engineers.

The problem

A generic language model can produce plausible criticism without proving which source supports it. It may also treat conventions as rules, miss access boundaries, or rewrite requirements in ways that change intent.

The useful unit is therefore not a chat answer. It is a review packet containing the original requirement, applicable evidence, categorized findings, proposed wording, uncertainty, and a human decision.

Engineering constraints

• Sources have different authority and access controls.
• A reviewer must see exactly what evidence informed a finding.
• The agent must not approve its own output.
• Requirement versions and decisions need an audit trail.

The approach

We would define a bounded review schema: ambiguity, testability, missing condition, conflicting source, undefined term, traceability gap, and no finding. Retrieval is limited to approved repositories and filtered by project and user permissions.

The agent returns structured findings with source passages, version identifiers, and a confidence or insufficiency state. It may draft alternatives, but it cannot overwrite the authoritative requirement or advance approval without a human action.

The pilot uses an approved set of historical requirements and reviewer decisions. Prompt, retrieval, model, and policy versions are stored so changes can be evaluated rather than guessed.

Proposed system architecture

• Permission-aware source connectors and versioned index.
• Retrieval layer that preserves source authority and identity.
• Structured review agent with bounded finding categories.
• Human review queue with accept, edit, reject, and comment actions.
• Audit record linking input, evidence, output, and final decision.

Validation strategy

Evaluation compares agent findings with an expert-reviewed set and measures both useful findings and unsupported claims. Adversarial cases include conflicting documents, insufficient context, prompt-like text inside sources, and inaccessible material.

The workflow is ready for a limited pilot only when it can abstain, cite correctly, and keep approval outside the agent.

Intended operational outcome

The intended outcome is faster preparation for human review, more consistent traceability, and clearer identification of missing information. The agent remains an assistant, not an authority.

Any published case study should report measured reviewer effort and quality on an approved dataset, with limitations and disagreement visible.

What a real engagement would require

• Approved requirement sources and authority hierarchy.
• Historical reviewer examples and finding taxonomy.
• Access-control model and retention policy.
• Named approvers responsible for final requirement decisions.

Source notes

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — The original RAG paper behind the retrieval-and-generation architecture.
• NIST AI Risk Management Framework — Risk, governance, measurement, and oversight principles used for the agentic scenarios.
• OWASP Top 10 for Large Language Model Applications — Security risks considered when agents handle untrusted content or use external tools.