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Realistic environments for prototyping, studying and improving analysisworkflows are a crucial element on the way towards user-friendly physicsanalysis at HL-LHC scale. The IRIS-HEP Analysis Grand Challenge (AGC) providessuch an environment. It defines a scalable and modular analysis task thatcaptures relevant workflow aspects, ranging from large-scale data processingand handling of systematic uncertainties to statistical inference and analysispreservation. By being based on publicly available Open Data, the AGC providesa point of contact for the broader community. Multiple differentimplementations of the analysis task that make use of various pipelines andsoftware stacks already exist. This contribution presents an updated AGCanalysis task. It features a machine learning component and expanded analysiscomplexity, including the handling of an extended and more realistic set ofsystematic uncertainties. These changes both align the AGC further withanalysis needs at the HL-LHC and allow for probing an increased set offunctionality. Another focus is the showcase of a reference AGC implementation,which is heavily based on the HEP Python ecosystem and uses modern analysisfacilities. The integration of various data delivery strategies is described,resulting in multiple analysis pipelines that are compared to each other.

Physics analysis for the HL-LHC: concepts and pipelines in practice with the Analysis Grand Challenge