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