Agentic AI for Autonomous Compliance in Enterprise Tax System
summary
Claims a multi-agent Autonomous Compliance Infrastructure compresses a 3–6 month regulatory-to-production cycle into hours by combining continuous regulatory monitoring, a DSL as source of truth, fine‑tuned coding LLMs for a text-to-code engine, and a four‑stage validation pipeline (static analysis, regression, differential, formal checks). It reports staged deployments with shadow/canary rollouts, confidence scoring that triggers human escalation, five deployment lessons from enterprise use, and measurable ROI: faster updates, lower manual cost, and reduced audit exposure.
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chapters (12)
- 0:00Welcome and Mission
- 1:08Compliance Drift at Scale
- 3:51Why Rule Engines Fail
- 5:24LLMs Turn Law Into Logic
- 6:44Autonomous Compliance Architecture
- 8:25Agent 1 Regulatory Monitoring
- 9:51DSL as Source of Truth
- 11:10Text to Code and Validation
- 13:42Human Governance and Auditability
- 16:10Five Deployment Lessons
- 17:52ROI and Adoption Path
- 19:21Future Outlook and Closing
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