Generative artificial intelligence (GenAI) and agentic systems are shifting software engineering from code-centric production toward intent-centric human-agent work, in which natural language, repository context, tools, tests, and governance shape delivery. Prior studies examine code generation, AI pair programming, and software engineering agents, but less is known about how public technical discourse and peer-reviewed evidence jointly frame the profession’s near-term transition. This study addresses that gap through a reflexive thematic analysis (RTA) of public-discourse and document analysis. The corpus combines peer-reviewed software engineering and AI literature, technical benchmarks, public talks and interviews, essays, product-facing technical announcements, and X-originated discourse. The corpus contains 49 unique artifacts across three evidentiary layers: 28 peer-reviewed sources, 6 technical preprints/benchmarks, and 15 public discourse artifacts. Sources were organized using a corpus register, codebook, coding matrix, theme-to-source traceability table, DOI/reference audit, and reproducibility protocol. The analysis indicates that GenAI lowers the cost of producing plausible code while increasing the importance of intent specification, context curation, architecture knowledge, verification, security, provenance, governance, and accountable human judgment. Software engineering is consequently becoming less about isolated code authorship and more about supervising, validating, and governing socio-technical systems of humans, agents, tools, and evidence gates. Bounded autonomy can preserve quality, security, maintainability, and trust while limiting hidden technical debt and accountability gaps.