Renjith Kathalikkattil Ravindran

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.

Multimodal misinformation detection has advanced from text-only classification toward joint processing of language, imagery, and social metadata. Yet much of the literature still treats adaptation as an internal fusion problem rather than a systems-level routing problem, leaving open the question of when multimodal escalation is warranted and how expert usage can be reduced without materially weakening predictive quality. This paper introduces MemANS, an event-driven, in-memory Agentic Name Search architecture for multimodal misinformation detection. MemANS treats inference as a resolution process over named experts: a default text–metadata resolver handles ordinary cases, while a specialist is invoked only when cross-modal disagreement and confidence conditions jointly indicate that additional reasoning is justified. The empirical study is conducted on a balanced, fully image-available Fakeddit benchmark comprising 5400 training instances, 840 validation instances, and 840 test instances across six misinformation classes. On the final test set, MemANS achieves 0.6182 macro-F1, 0.6167 accuracy, 0.5405 MCC, and 0.8726 weighted one-vs-rest AUC, clearly surpassing the always-on fusion baseline (0.5732 macro-F1) with a statistically supported gain of 0.0454 (p < 0.001) while using only 1.7202 average experts versus 2.0000. The results indicate that Agentic Name Search is a viable and practically meaningful multimedia-systems abstraction for adaptive multimodal reasoning.

This study utilizes grounded theory to investigate IT professionals’ adoption of blockchain-enabled smart contracts to maintain data integrity. Throu