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BlueCat Announces Tech Preview of MCP Servers and Expansion of LiveAssist

BlueCat Networks announced new portfolio innovations designed to enable agentic AI in network operations, including a tech preview of its MCP Servers and the expansion of LiveAssist, its virtual engineer, across the BlueCat platform.

BlueCat unifes network identity, policy, and telemetry into a unified data foundation. This approach allows agentic AI systems to understand context and take coordinated action across the network.

“We unify multi-vendor network data and give customers control over how and where AI runs, whether that’s multiple deployment models, AI agents, or LLMs,” said Scott Fulton, Chief Product & Technology Officer at BlueCat. “Our Intelligent NetOps foundation lets them put AI into production and see real returns, without putting network reliability and performance at risk.”

To extend this foundation throughout the broader AI ecosystem, BlueCat is launching a tech preview of its MCP Servers, which connect BlueCat’s network data and capabilities to a growing range of AI agents and platforms.

These integrations provide secure, structured access to real-time and historical network context, allowing AI tools to query, analyze, and act on network intelligence across integrated development environments, chat interfaces, and enterprise workflows. MCP Servers also include pre-built tools that help NetOps teams accelerate workflow development and improve the efficiency of LLM-driven operations.

BlueCat is also expanding LiveAssist, its virtual engineer, across its entire product portfolio, documentation, and support knowledge base.

LiveAssist uses BlueCat’s unified data foundation to move beyond basic queries. It helps teams investigate issues, understand root cause, and act through a single, conversational workflow. By combining real-time and historical network telemetry with configuration, DDI, and identity data, LiveAssist ensures a seamless progression from question to insight to execution.

“In complex environments, resilience and clarity aren’t optional. BlueCat delivers both,” said Mark Taylor, Enterprise Team Lead (Finance) at Techary, a UK-based technology services partner that delivers end-to-end IT services. “Its flexible architecture and transparent pricing remove friction, and its unified data layer is what allows AI-driven systems to operate with real precision instead of guesswork.”

MCP Servers integrated with LiveAssist are in tech preview across the BlueCat portfolio. In July, MCP Servers will start to become available via a public registry and included with base product licenses for use with other agentic platforms.

LiveAssist, which is generally available with BlueCat's network observability products, will extend to DDI - starting in July - and run on the BlueCat Horizon SaaS platform.

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Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

BlueCat Announces Tech Preview of MCP Servers and Expansion of LiveAssist

BlueCat Networks announced new portfolio innovations designed to enable agentic AI in network operations, including a tech preview of its MCP Servers and the expansion of LiveAssist, its virtual engineer, across the BlueCat platform.

BlueCat unifes network identity, policy, and telemetry into a unified data foundation. This approach allows agentic AI systems to understand context and take coordinated action across the network.

“We unify multi-vendor network data and give customers control over how and where AI runs, whether that’s multiple deployment models, AI agents, or LLMs,” said Scott Fulton, Chief Product & Technology Officer at BlueCat. “Our Intelligent NetOps foundation lets them put AI into production and see real returns, without putting network reliability and performance at risk.”

To extend this foundation throughout the broader AI ecosystem, BlueCat is launching a tech preview of its MCP Servers, which connect BlueCat’s network data and capabilities to a growing range of AI agents and platforms.

These integrations provide secure, structured access to real-time and historical network context, allowing AI tools to query, analyze, and act on network intelligence across integrated development environments, chat interfaces, and enterprise workflows. MCP Servers also include pre-built tools that help NetOps teams accelerate workflow development and improve the efficiency of LLM-driven operations.

BlueCat is also expanding LiveAssist, its virtual engineer, across its entire product portfolio, documentation, and support knowledge base.

LiveAssist uses BlueCat’s unified data foundation to move beyond basic queries. It helps teams investigate issues, understand root cause, and act through a single, conversational workflow. By combining real-time and historical network telemetry with configuration, DDI, and identity data, LiveAssist ensures a seamless progression from question to insight to execution.

“In complex environments, resilience and clarity aren’t optional. BlueCat delivers both,” said Mark Taylor, Enterprise Team Lead (Finance) at Techary, a UK-based technology services partner that delivers end-to-end IT services. “Its flexible architecture and transparent pricing remove friction, and its unified data layer is what allows AI-driven systems to operate with real precision instead of guesswork.”

MCP Servers integrated with LiveAssist are in tech preview across the BlueCat portfolio. In July, MCP Servers will start to become available via a public registry and included with base product licenses for use with other agentic platforms.

LiveAssist, which is generally available with BlueCat's network observability products, will extend to DDI - starting in July - and run on the BlueCat Horizon SaaS platform.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...