Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In our last post, we explored how short-term memory enables agentic AI to hold a conversation that doesn’t reset after every message. That form of memory is all about flow—preserving context, user intent, and logic within a single session, even as interactions stretch across multiple turns. The longer the session, the more memory is required to maintain continuity. But not all memory needs to be verbose. Long-term memory serves a different purpose: persistence across sessions.

As part of the DevSecNext AI series, Jit hosted Michael Balber—Principal Software Architect at CyberArk—for an in-depth session on how his team built and evaluated real Retrieval-Augmented Generation (RAG) agents in production. Unlike abstract discussions about LLMs and assistants, Michael shared a grounded view of what it takes to deploy agents that don’t just talk—they act.

As part of the DevSecNext AI series, Jit hosted Sahar Carmel—Principal AI Engineer at Flare—for an inside look into what it really takes to become an “AI-first” developer. With nearly a decade of experience in AI and machine learning, Sahar has been hands-on with copilots and agents long before they were mainstream. In this session, he walks through his radical shift in workflow: from writing code line-by-line to orchestrating prompts, tokens, and memory banks.