Fabrix.ai Demo – Building Agentic AI at scale for Production

Fabrix.ai is building agentic AI at scale for production, moving beyond proofs of concept to deliver robust solutions. In the video from the Fabrix.AI channel, Rached Blili demonstrated the Fabrix.ai platform, highlighting its agent catalog, where users can access and manage a variety of agents, both developed by Fabrix.ai and custom-built. The platform offers an AI Storyboard dashboard that provides a comprehensive view of AI operations, enabling agents to be organized into projects with distinct permissions and toolsets. A significant emphasis is placed on observability, including detailed AI cost tracking at both global and project levels, and visibility into individual “conversations” or agentic sessions. Uniquely, Fabrix.ai provides performance evaluation for agents, treating them as digital workers by monitoring their performance over time, identifying top and underperforming agents, and suggesting specific fixes, such as modifying system prompts, to continuously improve their efficacy.

The demonstration showcases two types of agents: autonomous and interactive. Autonomous agents operate in the background, triggered by events, alerts, or schedules, as exemplified by a Network Root Cause Analysis agent. This agent automatically diagnoses network failures, such as router configuration errors, by analyzing logs, incident data, and router configurations. It generates comprehensive reports detailing the root cause, impact assessment, and multiple remediation plans, which a remediation agent can then use for automated implementation and verification. For interactive use, Fabrix.ai’s copilot, Fabio, enables users to converse directly with agents to manage complex tasks, such as verifying VPNs or configuring Netflow in a lab network, significantly reducing manual intervention and saving time.

Delving into the underlying architecture, the presentation revealed that complex problems are tackled using multi-agent complexes, where an orchestrator agent calls specialized sub-agents, each handling a specific part of the problem with a sequestered context. This approach enhances individual agents’ capabilities while enabling detailed cost management, tracking token usage, time, and expenses, and capturing individual agent contributions within a hierarchical structure. A detailed example illustrated an application root-cause analysis in which the orchestrator agent systematically investigated incident details, application dependency maps, and even interpreted plain-English change requests from a ticketing system. The platform’s advanced context and tooling engines are critical to operating at scale, enabling mass operations across numerous devices in parallel and efficiently processing vast tool outputs by storing them in a context cache for later retrieval and analysis, ensuring effective, secure, and reliable agent deployment.