AI agents are shifting the role of developers from writing code to defining intent. This conversation explores why specs are becoming more important than implementation, what breaks in real-world systems, and how engineering teams need to rethink workflows in an agent-driven world.

The blog shows how to unify scattered multimodal assets, e.g., speaker bios, talk titles, videos, and PDFs. into a single, well‑structured memory layer.  It explains the metadata and schema decisions that let an agent answer richer, cross‑asset questions such as trending topics, influential speakers, or patterns across a full conference.  By grounding these relationships in a multimodal database like ApertureDB, the approach generalizes to any domain where organizations need AI to reason over diverse, real‑world collections of content.

This panel discusses the real-world challenges of deploying AI agents at scale. The conversation explores technical and operational barriers that slow production adoption, including reliability, cost, governance, and security. The panelists also examine how LLMOps, AIOps, and AgentOps differ from traditional MLOps, and why new approaches are required for generative and agent-based systems. Finally, experts define success criteria for GenAI frameworks, with a focus on robust evaluation, observability, and continuous monitoring across development and staging environments.