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This talk presents a technical case study of applying agentic AI systems to automate community operations at PyCon DE & PyData, treated as an open-source testbed. The key lesson is simple: AI only works when put on a leash. Reliable results required good architecture, a clear plan, and structured data models — from YAML and Pydantic schemas to reproducible pipelines with GitHub Actions. With that foundation, LLM agents supported logistics, FAQs, video processing, and scheduling; without it, they failed. By contrasting successes and failure modes across different coding agents, the talk demonstrates that robust design, validation, and controlled context are prerequisites for making agentic AI usable in real-world workflows.

In this session, we’ll take a closer look at the security risks that come with integrating LLMs into applications. LLMs can be powerful allies in cybersecurity — helping with detection, testing, and protection — but they can just as easily be exploited for attacks. We’ll explore key threats such as prompt injection, jailbreaking, and agent-specific vulnerabilities, and discuss why they are currently seen as the most pressing risks. Finally, we’ll look at defense strategies, from prompt-level safeguards to system-wide controls, and show how best practices can make a real difference in securing AI systems.