Cisco to Acquire Galileo to Strengthen AI Observability and Trust in Enterprise Deployments

Cisco is planning to acquire Galileo Technologies, pointing to a change in how enterprise AI is being managed. As more companies roll out AI agents across development, support, and operations, the focus is moving beyond testing ideas to making sure these systems work reliably in real use.

That’s where Galileo fits in. Its platform helps teams understand how AI systems behave in real-world scenarios, including issues like hallucinations, bias, and output quality. As AI agents take on more responsibility, even small errors can create real business risk. Galileo gives teams a way to track and manage those risks continuously, not just during development but after deployment.

Extending observability into the AI lifecycle

For Cisco, the acquisition strengthens its position in observability, especially through its Splunk Observability Cloud platform.

A Cisco spokesperson told ChannelE2E, "Galileo will strengthen Splunk’s observability portfolio and expand its AI agent monitoring capabilities, giving customers real-time visibility across the full agent development lifecycle to help build trust in AI systems." The spokesperson added that Galileo’s solution provides insights from early stages, such as prompt optimization and model selection through to production monitoring, observability, and enforcement of guardrails.

What this means for enterprise AI teams

This matters for enterprises trying to operationalize AI. Without clear visibility into how AI systems behave, teams are left reacting to failures instead of preventing them. The spokesperson said, "Galileo’s expertise in AI observability, evaluation engineering, and multi-agent system development will complement Splunk’s capabilities and help meet growing enterprise demand for controlled, measurable AI deployments."

The integration also points to where observability is heading. With Galileo helping power Splunk Observability Cloud, Cisco aims to establish a stronger standard for how AI agents are evaluated in enterprise environments. As AI becomes part of core business workflows, the ability to track behavior, enforce guardrails, and measure outcomes is becoming central to adoption.

The direction is clear. As AI moves deeper into business use, reliability and accountability are becoming just as important as innovation. Vendors that can provide clear control and visibility are likely to play a bigger role in how enterprises scale AI.