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Leading Agentic AI Platform

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AIOps to Agentic AIOps

Capability
Traditional AIOps
Agentic AIOps (Next-Gen)
Anomaly Detection
ML models detect metric/log outliers; usually pre-trained or rule-based
Agents learn context, refine thresholds dynamically, and use feedback to improve detection
Root Cause Analysis (RCA)
Statistical correlation or graph-based techniques suggest probable causes
RCA agents perform deeper diagnostics, reason through dependencies, and infer likely root causes using both structured and unstructured data
Event Noise Reduction
Uses suppression rules, ML clustering, and correlation
Agents autonomously reconfigure suppression logic, identify new patterns without retraining
Incident Response Automation
Predefined playbooks or runbooks execute known scripts
Agents dynamically generate or modify workflows, can choose optimal remediation paths based on context
Service Impact Mapping
Uses topology models and CMDBs to relate alerts to services
Agents build and maintain dynamic, evolving service graphs from real-time signals and behaviors
Human Interaction
Dashboards, alerts, and manual triage
Conversational agents** enable NLP interfaces, offer decisions with explainability, and support escalation
Workflow Adaptability
Manual updates needed to reflect environment changes
Agents rewrite or recompose workflows automatically in response to environment or policy changes

Why Fabrix.ai Agentic Platform?

Challenge
Fabrix.ai Solution
Lack of Data Context
Fabrix.ai Data Fabric provides rich and automated data enrichment with service topology and business context, enabling agents to reason more accurately and act with relevance.
Modern & Legacy Integration
Offers low-code connectors and composable pipelines to ingest data from both modern cloud-native and legacy systems; supports streaming and batch ingestion for hybrid IT environments.
Multi-LLM Integration
Supports integration with multiple LLMs (e.g., OpenAI, Anthropic, open-source models) to enable agent flexibility, model fallback, use-case-specific tuning, and vendor-neutral AI orchestration
Security & Governance
Built-in policy engine, role-based access, and audit trails, with enterprise-grade guardrails to restrict agent permissions, scope of actions, escalation pathways, and ensure compliance with operational and data protection standards.
Interoperability (via MCP)
Powered by a Model Context Protocol (MCP) that allows plug-and-play interoperability with external agents, pipelines, observability tools, and third-party services — without refactoring core systems
Cross-Domain Reasoning
Enables domain-specific agents that collaborate over a shared semantic model; supports use cases across infra, cloud, app, SecOps, and DevOps
Trust & Explainability
Fabrix agents are explainable-by-design, with decision logs, human-in-the-loop workflows, and AI observability dashboards for traceability
Adoption & Usability
Includes a low-code canvas, natural language interfaces, and prompt-driven analytics to reduce friction for IT teams with limited AI experience

AI Personas & Use Cases

The platform provides certain workflows for operational personas, but can be adapted to any personas and agentic workflows.

Enterprise Grade Fabrix.ai Agentic AI Platform

Key Tenets:
  • AI Agent Studio - Agent Orchestration and Lifecycle Management
  • AI Co-pilot - Generative AIOps
  • AI Fabric - Multi-agent Orchestrator using Google A2A (Agent to Agent) protocol
  • MCP (Model Context Protocol) enabled Data Fabric, Automation Fabric and Model Registry
  • AI Guardrails - Managing Data & Action Privileges for Agents
  • Agent Quality Control and Assurance
  • Visibility and Observability of Agents

Quick Start

How to Get Started with Fabrix AI Agents

Choose Your AI Infrastructure
Select your preferred AI deployment model:
  • Cloud-based LLMs: Use OpenAI, Anthropic, or other public APIs. Ideal for agility and ease of access.
  • On-premise / Self-hosted LLMs: Deploy open-source models (e.g., LLaMA, Mistral) within your secure infrastructure. Best for data sovereignty, compliance, or low-latency edge scenarios.
  • Fabrix.ai supports hybrid and multi-LLM architectures.
Fabrix.ai Professional Services Can Help
Fabrix.ai Professional Services offers:
Need support with private LLM hosting or advanced agent configuration?
  • Infrastructure setup for private LLMs
  • Agent deployment and orchestration guidance
  • Integration with observability and NOC environments
Onboard Out-of-the-Box Agents
Accelerate deployment using pre-built agents for common telecom and enterprise use cases:
  • RCA Agent: Performs automated root cause analysis across network and IT domains
  • Anomaly Detection Agent: Identifies behavioral anomalies in KPIs, metrics, or events using AI/ML
  • Service Migration Agent: Assists in migrating services from legacy to modern platforms (e.g., MPLS to SD-WAN, 3G to 5G) with dependency mapping, risk detection, and rollback planning
Build Your Own Agent (BYOA)
Custom agents allow you to embed Fabrix.ai intelligence into your unique workflows.

Steps to Build:
  • Choose a template (e.g., RCA, Migration, Compliance, Detection)
  • Define the goal or operational intent of the agent
  • Use the prompt builder to shape LLM responses and behavior
  • Attach relevant data sources, telemetry, and thresholds
  • Configure constraints, escalation logic, or feedback loops
Leverage the Model Context Protocol (MCP)
Fabrix.ai includes a powerful MCP (Model Context Protocol) server that exposes:
  • Data pipelines and enriched telemetry
  • Automation workflows and runbooks
This allows external agents, tools, or orchestration layers to seamlessly access and interact with Fabrix.ai’s real-time intelligence. Additionally, Fabrix.ai agents can:
  • Discover and collaborate with other Fabrix.ai or third-party agents
  • Participate in multi-agent workflows across network, app, and service domains
  • This enables powerful, scalable, and decentralized decision-making across the enterprise or service provider ecosystem.
Deploy & Test
Launch your agent in a target environment (NOC, service domain, pilot region)
  • Test with synthetic or historical data
  • Use the explainability layer to observe how decisions are made
  • Iterate on prompts, thresholds, or workflows based on real-time performance
  • Agents can run autonomously or with approval workflows in place for sensitive actions.
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