Production tool calling for agentic systems must satisfy four operational requirements: bounded per-invocation cost, predictable latency, adaptability to evolving tool catalogs, and the ability to catch errors before execution. The ReAct paradigm keeps the large language model (LLM) in the loop, but at production scale, re-processing intermediate results inflates cost and latency and degrades answer quality. Programmatic tool calling (PTC) offers an alternative; the LLM generates a single script that resolves dependencies and extracts relevant fields in one inference pass. PTC is a harder generation task, however, because the LLM must produce code that handles complex, variable responses without observing intermediate results, and existing remedies each fail at least one of the four requirements. We describe a production deployment of PTC serving 26,528 invocations, paired with a three-layer abstract syntax tree (AST)-based validator that rejects invalid scripts before execution and requires no training data, preserving adaptability to evolving tool catalogs. This reduced average input tokens by 77%, latency by 21%, and increased the answered-query rate by 5.6 percentage points across 19 production agents.

Hierarchical Text Classification (HTC) is a subclass of multi-label classification, it is challenging because the hierarchy typically has a large number of diverse topics. Existing methods for HTC fall within two categories, local methods (a classifier for each level, node, or parent) or global methods (a single classifier for everything). Local methods are computationally expensive, whereas global methods often require complex explicit injection of the hierarchy, verbalizers, and/or prompt engineering. In this work, we propose Prompt Tuned Multi Task Taxonomic Transformer, a single classifier that uses a multi-task objective to predict one or more topics. The approach is capable of understanding the hierarchy during training without explicit injection, complex heads, verbalizers, or prompt engineering. PTMTTaxoFormer is a novel model architecture and training paradigm using differentiable prompts and labels that are learnt through backpropagation. PTMTTaxoFormer achieves state of the art results on several HTC benchmarks that span a range of topics consistently. Compared to most other HTC models, it has a simpler yet effective architecture, making it more production-friendly in terms of latency requirements (a factor of 2-5 lower latency). It is also robust and label-efficient, outperforming other models with 15%-50% less training data.