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FiMI Banking, a controlled Indian retail-banking setting, is introduced and it is shown that preference optimization and verifiable-reward reinforcement learning address complementary requirements for reliable banking agents.
This paper provides a mathematical formalization of the orchestration state, detailed algorithmic analysis of the execution loop, and controlled benchmarks comparing flat and hierarchical routing under increasing tool catalogs, multi-step workflow pressure, and visible schema-token exposure per LLM call.
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