LLMs

Cloud agents typically send user messages to remote LLMs and store conversation traces in memory systems (e.g., Mem0, LangMem, Memobase) for long-term personalization. This creates a large privacy attack surface: plaintext prompts and logs may contain PII, medical/financial data, credentials cloud memory stores can leak via retrieval, prompt injection, inversion, or misconfiguration naïve mitigation (e.g., *** masking) destroys task semantics, harming retrieval and personalization Goal: reduce privacy leakage without sacrificing utility.

Everyone’s talking about MCP these days when it comes to large language models (LLMs)—here’s what you need to know.

SLMs are well-positioned for the agentic era because they use a narrow slice of LLM functionality for any single language model errand. LLMs are built to be powerful generalists, but most agents use only a very narrow subset of their capabilities.  They typically parse commands, generate structured outputs such as JSON for tool calls, or produce summaries and answer contextualized questions. These tasks are repetitive (up to the differences in prompt payloads), predictable, and highly specialized—well within the scope of specialized SLMs. An LLM trained to handle open-domain conversations is o

If SLMs have clear advantages, why do most agents still rely so heavily on LLMs? We hypothesize that the barriers are perception-based or caused by organizational culture rather than technical limitations. Shifting to SLM-enabled architectures requires an intentional mindset change. SLM research uses generalist benchmarks, even though agentic workloads demand different evaluation metrics. Plus, LLMs often dominate the headlines. As the cost savings and reliability of SLM-enabled systems become undeniable, momentum will shift. The transition could mirror past shifts in computing, such as the mo