Blog Cisco Artificial Intelligence

This post is Part 1 of a two-part series on multimodal typographic attacks. This blog was written in collaboration between Ravi Balakrishnan, Amy Chang, Sanket Mendapara, and Ankit Garg. Modern generative AI models and agents increasingly treat...

15. 4. 2026

Lessons from building production AI systems that nobody talks about. The conversation around AI agents has moved fast. A year ago, everyone was optimizing RAG pipelines. Now the discourse centers on context engineering, MCP/A2A protocols, agentic.

10. 4. 2026

We recently discovered a method to compromise Claude Code’s memory and maintain persistence beyond our immediate session into every project, every session, and even after reboots. In this post, we’ll break down how we were able to poison an AI.....

1. 4. 2026

OpenClaw enables powerful AI agent workflows—but introduces new security risks. Learn why securing tools, MCP servers, and agent-generated code is critical, and how DefenseClaw helps protect your environment.

31. 3. 2026

Last week, DJ wrote about why OpenClaw – the agent he uses to help run his family’ life needs a governance layer. He pointed to ClawHavoc, 135K exposed instances, and the growing gap between how powerful OpenClaw is and how little anyone was doing...

31. 3. 2026

Everyone's talking about AI agents, but most explanations overcomplicate it.  An agent is just instructions that tell AI how to think and execute action. Not just what to do, but how to approach problems. Think of it as capturing your best decision-making process in a format that scales and always executes the same way.  Here's what matters when you build one. 

26. 3. 2026

Cisco IT recently evaluated fine-tuning embedding models using NVIDIA Nemotron RAG fine-tuning recipe as part of an effort to improve retrieval accuracy for domain-specific enterprise data. The objective was not to redesign existing retrieval-augmented generation (RAG) systems, but to understand whether targeted embedding fine-tuning could materially improve semantic search quality with reasonable effort and fast turnaround. Through this experiment, Cisco was able to validate firsthand that embedding fine-tuning, combined with synthetic data generation, can deliver measurable accuracy gains within a short time frame. The experiment also demonstrated strong time-to-value, enabling rapid iteration and clear performance signals without long training cycles or extensive manual labeling. The reduced turnaround of only a few days to understand the immediate benefits was a key outcome of this collaboration. The embedding model training and evaluation workflow was executed on Cisco AI PODs running Cisco UCS 885A infrastructure powered by NVIDIA HGX platform.

25. 3. 2026

LangChain makes it easy to move from a working prototype to a useful agent in very little time. That is exactly why it has become such a common starting point for enterprise agent development.   Agents don’t just generate text. They call tools, retrieve data, and take actions. That means an agent can touch sensitive systems and real customer data within a single workflow. 

24. 3. 2026

There's a DGX Spark sitting in my home office running OpenClaw. It's connected to my phone and my laptop through secure tunnels, and it has become, without exaggeration, the operating system for how my family runs.  My wife and I use it to plan our kids' schedules. I built an agent skill that pulls up the school lunch menu every morning as a reminder. Another one tracks their tennis match draws. I've connected Model Context Protocol (MCP) servers through Zapier to sync my email, my calendar, and Discord. It nudges me about things I'd otherwise forget. It holds all the context I can't hold in my head. It has become my deepest thinking partner: the place where half-formed strategy ideas become real before they ever hit a slide deck. 

23. 3. 2026