Javier Antich

Principal Product Management Engineer

CTO Office - Provider Connectivity Group

Javier Antich has been in the Networking Industry for 25 years. He is currently Principal Product Management Engineer at the Provider Connectivity group’s CTO Office at Cisco, where he is connecting the dots between networking, automation, and AI. He has been researching the Art of the possible with LLMs in Networking with the Industry's first autonomous network troubleshooting agent (Anetta.ai). Javier is also an accomplished author of the best-selling book “Machine Learning for Network and Cloud Engineers”. He holds a BS Degree in Telecommunication Engineering from the ETSIT at the Polytechnic University in Valencia, an Executive MBA at the IE Business School, and a Data Science and Deep Learning master’s degree from the Madrid Institute of IoT.

Articles

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Javier Antich

Gurudatt Shenoy

The impact of AI on wide area network traffic: we need to talk

3 min read

This blog introduces a new Cisco report that looks at how AI—and especially agentic AI—is reshaping global wide area network traffic patterns. AI isn't just adding traffic. It's changing the shape of traffic.

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Javier Antich

Making Agentic AI Observable: How Deep Network Troubleshooting Builds Trust Through Transparency

7 min read

Building trust in AI-driven network operations starts with transparency. In this third and final post in the series, we show how visibility and observability make every agent action auditable—so engineers can verify and improve outcomes.

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Javier Antich

Building AI You Can Trust for Network Troubleshooting with Deep Network Solutions

4 min read

AI is transforming network troubleshooting, but trust is critical. In part two of our series, discover how Deep Network Troubleshooting combines verified knowledge, advanced reasoning, and human oversight to deliver automation you can rely on.

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Javier Antich

Revolutionizing Network Troubleshooting with Deep Research AI Agents

6 min read

This first blog in our series covers Deep Network Troubleshooting Agentic AI. See how a multi-agent approach accelerates root cause analysis and empowers engineers in multivendor environments while maintaining human oversight.