Build Reliable, Secure, and Performant Agents using Fabrix.AI AgentOps Platform

Fabrix.AI addresses the evolving AI operations landscape with an AgentOps platform that builds reliable, secure, and high-performance agents. The company, formerly Cloudfabrics.com, rebranded as Fabrix.AI in response to customer demand for agentic functionality, moving beyond traditional AIOps, which relies on manual remediation after correlation and root-cause analysis. This shift was motivated by real-world challenges, such as an 8-hour telco outage caused by inadvertent access control list changes, highlighting the need for autonomous or semi-autonomous remediation workflows powered by Large Language Models (LLMs). However, this transition introduces new complexities, including the non-deterministic nature of LLMs, context and data management at scale, and the challenge of connecting to diverse data sources, which can lead to issues such as hallucination and an “agentic value gap,” where experimental demos rarely translate to enterprise value.

Fabrix.AI’s solution centers on proprietary middleware that serves as a critical intermediary between AI agents/LLMs and various data sources. This middleware comprises two main components: the Context Engine and Universal Tooling. The Context Engine ensures “purity of context” by providing only curated, summarized data to the LLM, thereby preventing context corruption and reducing hallucination, while also maintaining state across interactions. The Universal Tooling dynamically connects to over 1,700 disparate data sources, including MCP-enabled endpoints, API-based systems, and raw or legacy data, by creating necessary wrappers and normalizing data schemas for LLM understanding, and can even dynamically generate tools by scraping public APIs. This approach allows the platform to integrate seamlessly with existing IT environments, offering a full-stack solution from data acquisition to automation.

The platform is purpose-built for real-time data environments, differentiating it from generic agentic frameworks that may not meet these requirements. It offers a “co-pilot” for conversational queries and an “Agent Studio” for building custom agents, supplementing its library of 50 out-of-the-box agents across AIOps, Observability, SecOps, and BizOps. Fabrix.AI emphasizes operationalizing agents through its AgentOps model, which incorporates trust via prompt templates and dynamic instructions, governance through FinOps models, security via a “least agency” principle, and comprehensive observability at the agentic layer with audit trails and real-time flow maps. By consolidating tools, reducing Mean Time to Resolution (MTTR) and alert noise, and enabling faster deployments, Fabrix.AI positions itself as a robust, enterprise-grade platform that complements and enhances existing observability and ITOM tools.