Discussion of AI and transfer pricing has largely centred on whether a single autonomous agent could take a set of intercompany agreements, run a functional analysis, select comparables and produce a defensible benchmarking range with minimal human involvement. Vendors market toward that capability, and practitioner panels debate whether such an agent could meet the reliability standards implicit in Section 92C or Section 482. A hackathon held in Vienna earlier this year, organised with the WU Tax Law Technology Center, Microsoft and TPA Global and reported only this week, points to a different pattern taking shape in practice. Rather than building one model to perform the entire task, participating teams chained together several narrower agents, each handling a bounded function.
The case studies covered intra-group financing and intercompany services: arm's length interest rates, creditworthiness assessment, the benefit test, cost allocation, method selection and documentation. Teams built separate agents for data extraction, service classification, benefit testing, cost allocation, compliance monitoring, documentation and audit readiness, and linked them into a workflow. The organisers were explicit that the intent is not to replace professional judgment: outputs are meant to remain traceable to source, reviewed before use, and subject to human oversight at each step. This combination of decomposition and human-in-the-loop review appears to be the practical model emerging from the exercise, even as vendor marketing continues to emphasise single-agent capability.
This distinction matters for Indian TP practice because the architecture of contemporaneous documentation, under Rule 10D, the erstwhile Form 3CEB and now Form 48 under the 2025 Act, assumes a single preparer's judgment trail. A TPO can ask why a particular comparable was included and expect an answer from one analyst or one firm. A pipeline of five narrow agents does not fit that assumption. If a data-extraction agent misclassifies a transaction, a downstream benefit-test agent may inherit that error and proceed regardless, since it is not designed to question upstream inputs, only to execute its own task. The more likely failure mode in a multi-agent TP workflow is not a single agent producing a wrong answer, but an error propagating silently across a handoff that no one is specifically assigned to audit. India's TP documentation requirements, safe harbour disclosures and APA application forms do not currently address this scenario; they assume one preparer whose competence and good faith can be tested under cross-examination or TPO scrutiny.
Practitioners, and possibly CBDT, will need to consider what a chain-of-custody requirement for multi-agent TP work product should look like before a dispute forces the issue. Knowing that a benchmarking output traces back to a database source, as most current vendor claims are framed, is not sufficient. It would also require knowing which agent touched the data at each stage, what it changed or flagged, and whether a human actually reviewed the boundary between two agents' work rather than only the final output. India is already working through related questions, such as how DEMPE functions performed by AI systems fit within the intangibles framework, and whether GCC functional segmentation holds up against agentic AI restructuring inside captive centres. The handoff-audit issue sits a level below those debates: it concerns not whether AI can perform a TP function defensibly, but whether anyone can reconstruct, after the fact, which of several AI agents was responsible when something went wrong. Given how current documentation standards are framed, that question is more likely to surface first in a TPO's show-cause notice than in a policy paper, and practitioners relying on multi-agent tools would do well to build their own audit trail across agent handoffs before that happens.
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