- The rise of autonomous AI agents in tax discussions often overshadows the true source of progress, which lies in effective workflow design rather than the development of smarter models. Structured multi-step workflows that separate tasks into distinct stages lead to better outcomes than relying on advanced models alone.
- Reliability, rather than intelligence, is the primary constraint for agentic systems. Achieving high reliability in multi-step processes is challenging, as each step introduces the potential for failure. A focus on reconfiguring work, rather than granting full autonomy to AI agents, is essential for effective integration within tax functions.
- The future of AI agents in indirect tax will involve using them as assistants and orchestration tools to enhance workflows, rather than fully autonomous systems. These agents will support tax professionals by reducing manual coordination, highlighting inconsistencies, and facilitating analysis while maintaining the necessary oversight and control that ensures compliance and accuracy.
Source Aleksandra Bal
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