Online Appendix - Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners

This replication package provides how we collect, extract, code, and analyze findings from Videos (transcripts) related to the AI agent-based. In this research, the LLMs process was operated on Finnish Supercomputer using vLLM of LRM (Large Reasoning Models) and LLMs (Large Language Models). Note: All the hyperlinks referring to the shared files only work in the local version, as the browser does not find the files. (Please download the replication package here.) License All generated data is provided under DATA_LICENSE Creative Commons 4.0 Attribution License. All scripts are provided under the Script_LICENSE MIT License. Contents This repository consists of the following files: README.md: The README file describes the project and how to run the scripts to get the data and do the analysis. INSTALL.md: Installation and configurations instructions. requirements.txt: Library requirements for the Python environment. Data_Collection Analysis.xlsx: File to describe the process of the data selection. Among tabs, Tab ’Talks Selection’ describes selection of talks by application of the I/E criteria by 4 human assessors. Tab ‘I/E Criteria’ is the Inclusion and Exclusion Criteria used for the Talk Selection. Scripts 00_Data_Collection audioCutting.py: Script to cutting the audio. audioExtraction.sh: Script to extract the audio. transcriptExtraction.sh: Script to extract the transcripts. videoDownload.sh: Script to download the videos. Transcripts_Pipeline_Description: General description of the transcript extraction pipeline. 01_Extraction 01_videolinkscript_extraction.py: Script to extract evidence-grounded answers to 7 research questions from transcripts using retrieval-augmented generation and SWEBOK-aligned terminology refinement. 01_sbatch_extraction.sh: Script to submit the extraction work to the supercomputer. 02_Extraction_Validate 02_extraction_validate.py: Script to validate LLM-generated answers to research questions against transcript evidence using retrieval-augmented context and structured judgment prompts. 02_extraction_validate.sh: Script to submit the extraction validation work to the supercomputer. 03_ThematicAxial 03_thematicaxial_RQ1.py: Script to perform thematic and axial coding of RQ1-related excerpts by synthesizing timelines, motivations, and practitioner experiences into structured JSON output. 03_thematicaxial_RQ1.sh: Script to submit the corresponding RQ1 work to the supercomputer. 03_thematicaxial_RQ2.py: Script to perform thematic and axial coding of reported impacts of agent-based architectures (RQ2), labeled with supporting sources. 03_thematicaxial_RQ2.sh: Script to submit the corresponding RQ2 work to the supercomputer. 03_thematicaxial_RQ3.py: Script to perform thematic and axial coding of architectural considerations in agent-based migration or greenfield design (RQ3), labeled with supporting sources. 03_thematicaxial_RQ3.sh: Script to submit the corresponding RQ3 work to the supercomputer. 03_thematicaxial_RQ4.py: Script to perform thematic and axial coding of architectural strategies for agent selection and role assignment (RQ4), labeled with supporting sources. 03_thematicaxial_RQ4.sh: Script to submit the corresponding RQ4 work to the supercomputer. 03_thematicaxial_RQ5.py: Script to perform thematic and axial coding of architectural patterns for agent composition and coordination (RQ5), labeled with supporting sources. 03_thematicaxial_RQ5.sh: Script to submit the corresponding RQ5 work to the supercomputer. 03_thematicaxial_RQ6.py: Script to perform thematic and axial coding of domain- or task-specific differences in architectural practices (RQ6), labeled with supporting sources. 03_thematicaxial_RQ6.sh: Script to submit the corresponding RQ6 work to the supercomputer. 03_thematicaxial_RQ7.py: Script to perform thematic and axial coding of announced technologies used in LLM-based agentic systems (RQ7), labeled with supporting sources. 03_thematicaxial_RQ7.sh: Script to submit the corresponding RQ7 work to the supercomputer. 04_ThematicAxial_Validate 04_thematicaxial_RQ1Validate.py: Script to validate whether thematic and axial codes for RQ1 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq1.sh: Script to submit the corresponding RQ1 validation work to the supercomputer. 04_thematicaxial_RQ2Validate.py: Script to validate whether thematic and axial codes for RQ2 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq2.sh: Script to submit the corresponding RQ2 validation work to the supercomputer. 04_thematicaxial_RQ3Validate.py: Script to validate whether thematic and axial codes for RQ3 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq3.sh: Script to submit the corresponding RQ3 validation work to the supercomputer. 04_thematicaxial_RQ4Validate.py: Script to validate whether thematic and axial codes for RQ4 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq4.sh: Script to submit the corresponding RQ4 validation work to the supercomputer. 04_thematicaxial_RQ5Validate.py: Script to validate whether thematic and axial codes for RQ5 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq5.sh: Script to submit the corresponding RQ5 validation work to the supercomputer. 04_thematicaxial_RQ6Validate.py: Script to validate whether thematic and axial codes for RQ6 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq6.sh: Script to submit the corresponding RQ6 validation work to the supercomputer. 04_thematicaxial_RQ7Validate.py: Script to validate whether thematic and axial codes for RQ7 are correct and supported by the original source excerpts. 04_thematicaxial_validaterq7.sh: Script to submit the corresponding RQ7 validation work to the supercomputer. Results 00_citation_groups_from_data_analysis.xlsx: citation groups (map talks IDs to GXX) from data analysis to the paper. 00_Data_Analysis.xlsx: Analysis of the generated results (including the whole results, and map 7 questions to three main objectives) 01_Extraction_Marco.csv: Raw results (extract relevant info for RQ) generated from Marco-o1. 02_ExtractionValidation_Llama.csv: Raw results (extraction validation - extract relevant info for RQ) generated from Llama. 02_ExtractionValidation_Mistral.csv: Raw results (extraction validation - extract relevant info for RQ) generated from Mistral Nemo. 02_ExtractionValidation_Qwen.csv: Raw results (extraction validation - extract relevant info for RQ) generated from Qwen. 03_ThematicAxial_Marco_RQ1.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ1 generated from Marco-o1. 03_ThematicAxial_Marco_RQ2.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ2 generated from Marco-o1. 03_ThematicAxial_Marco_RQ3.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ3 generated from Marco-o1. 03_ThematicAxial_Marco_RQ4.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ4 generated from Marco-o1. 03_ThematicAxial_Marco_RQ5.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ5 generated from Marco-o1. 03_ThematicAxial_Marco_RQ6.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ6 generated from Marco-o1. 03_ThematicAxial_Marco_RQ7.csv: Raw results (thematic+axial coding - classify and summarize the extraction results) regarding RQ7 generated from Marco-o1. 04_ThematicAxial_Validation_RQ1.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ1 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ2.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ2 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ3.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ3 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ4.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ4 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ5.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ5 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ6.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ6 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). 04_ThematicAxial_Validation_RQ7.csv: Results (thematic+axial validation - classify and summarize the extraction results) regarding RQ7 generated from 3 LLMs (Mistral Nemo, Llama, and Qwen). Comanions Extended_Methodology.pdf: The extended Methodology of the paper that describe more details. Linguistics_Metrics.pdf: The quantitative linguistic analysis of AI practitioner transcripts, focusing on length, lexical diversity, syntactic complexity, and domain specificity. Linguistic_Metrics linguistic_metrics.py: Script to extract linguistic and structural metrics from text corpora and export them in structured formats. plots.py: Script to visualize and summarize the distributions of linguistic metrics through histograms, KDE curves, boxplots, and statistical summaries. plots: Folder that store the results generated by plots.py