Use of Sources
Sources in this project are used to support both explanation and real-world relevance. Academic research provides the technical foundation for how AI agents function and fail, while industry and policy sources show how those failures affect real systems and users.
- Academic sources: explain system structure and failure patterns
- Industry sources: connect those failures to real-world risks (security, finance, automation)
- Forward-looking sources: show how these risks are evolving
These sources are not used separately, but combined to show how system design leads to real-world consequences, which then informs how users should respond.
References
APA citation list
Consumer Bankers Association. (2026). Agentic AI payments: Navigating consumer protection. https://consumerbankers.com/wp-content/uploads/2026/01/CBA-Agentic-Symposium-White-Paper-2026-01v2.pdf
Cyber Magazine. (2026). The risk of agentic AI systems. https://cybermagazine.com/news/the-risk-of-agentic-the-story-of-metas-ai-agent-data-leak
Gartner. (2026, January 15). Gartner says worldwide AI spending will total $2.5 trillion in 2026. https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026
IBM Technology (Crume, J.). (2025). Cybersecurity trends in 2026: Shadow AI, quantum & deepfakes [YouTube video]. https://www.youtube.com/watch?v=2jU-mLMV8Vw
Kumar, P. [@pankajkumar_dev]. (2026, July 5). Claude Mythos: The model Anthropic is too scared to release… [Tweet]. X (formerly Twitter). https://x.com/pankajkumar_dev/status/2041750996196749547
Microsoft. (2025). Global AI adoption report. https://www.microsoft.com/en-us/corporate-responsibility/topics/ai-economy-institute/reports/global-ai-adoption-2025/
N2K / The CyberWire. (2025). Cybersecurity predictions for 2026. https://thecyberwire.com/stories/5a2a9536820742d9afc5be71e4002eab/looking-ahead-cybersecurity-predictions-for-2026
Shinn, N., Labash, B., & Gopinath, A. (2023). Reflexion: Language agents with verbal reinforcement learning. arXiv preprint arXiv:2303.11366. https://arxiv.org/pdf/2303.11366
Weng, L. (2023). LLM powered autonomous agents. https://lilianweng.github.io/posts/2023-06-23-agent/