AI-native Tax Platform
Designing a multi-agent tax assistant, agentic workflows, a verifiable retrieval layer, and an Azure serverless runtime for professional tax work.
Primary outcome
Established the product, knowledge, validation, and runtime foundations for an AI-native tax work platform.
The problem
Turn fragmented tax knowledge and tacit expert judgment into a reusable AI work platform without losing traceability or expert control.
Constraints
- Tax conclusions require source traceability and professional review.
- Sensitive work context limits what can be exposed outside controlled workflows.
- Agent behavior, data preparation, validation, and operations must evolve as one lifecycle.
Approach and key decisions
- 01
Decomposed professional work into collaborating agent roles for retrieval, judgment, review, and response.
- 02
Designed a hybrid retrieval layer with normalized sources, metadata, vector search, source lineage, and citation validation.
- 03
Defined risk-tiered autonomy, human sign-off, failure handling, and evaluation criteria as part of the product workflow.
- 04
Separated authentication, APIs, functions, storage, orchestration, observability, and deployment boundaries on an Azure serverless runtime.
Outcomes
- Built a multi-agent tax assistant structure for work-context search, judgment support, review, and response.
- Shaped investment tax review, VAT reconciliation, and contract review into agentic workflows with explicit approval and exception paths.
- Established a verifiable knowledge architecture connecting source lineage, evidence tracking, citation validation, and expert sign-off.
Technology and methods
Evidence and disclosure scope
This case describes the portfolio owner’s role and public-safe architecture patterns. Client-confidential data, prompts, evaluation results, and implementation details are intentionally omitted.
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