Mind the AI value gap: How operational intelligence is defining the agentic workforce
Image courtesy: The Cube Research
Image courtesy: The Cube Research
Agentic AI Unleashed: The Future of Digital & IT Operations - Access On-Demand
The Agentic AI Operational Intelligence Platform
for ITOps, NOCOps and AIOps
Introducing AI Agent Catalog
Put AI Agents to Work!
One platform to design, operate, and observe AI agents - on-prem or cloud
Full-stack visibility via open standards or your ITOM stack—then pair with autonomous agents to cut noise and MTTR
- Asset Health Reporting
- Uptime Reporting
- Anomaly Detection
- KPI Forecasting
- Alert Optimization Advisor
- More...
Cross-domain correlation, noise reduction, RCA, forecasting, and safe remediation—human-in-the-loop when you want it
- RCA
- Incident Assignment
- Anomaly Detection
- Remediation Agent with Approval
- DEX Analyst
- More...
Single-pane operations across SD-WAN, branch/edge, campus, data center, and service-provider networks—model, validate, resolve
- Network Config Compliance
- Digital Twin
- ACL Change Audit
- Path Tracing Agent
- Dependency & Impact Analysis
- More...
Real-time inventory, utilization, dependencies, and lifecycle health to stay compliant, secure, and cost-efficient
- SACM Asset Intelligence Analyst
- Asset Lifecycle & Capacity Analyst
- Asset Plan-of-Record
- Asset Upgrade Spotter
- More...
Activate outcomes across Splunk Core, Cloud, and ITSI—faster onboarding, cleaner data, richer insights, decisive action
- Splunk SIEM
- Data Prep & Ingestion
- ITSI Analyst & Resilience
- Service Desk
- PlatformOps Analyst
- Data Analyst
- More...
Unify VAPT, SOC, and GRC with agentic speed—and governance you can trust. Shrink dwell time, prove control health, and automate safely
- Automated Reconnaissance (VAPT)
- Exploit Assistant (VAPT)
- User Behavior Analysis (SOC)
- Patch Prioritization (SOC)
- Compliance Mapping (GRC)
- Control Validation (GRC)
- More...
Enterprise Grade AI Platform
From build to observe - guardrailed, auditable agents engineered for enterprise scale.
Support featured LLMs. On-Prem or Cloud. Seamless Integration. Nvidia Ready
Provides context caching for optimal token usage, allows LLMs to work with very large datasets.
Enforce safety, policy, and intent checks on every run-blocking non-compliant prompts and destructive actions-via seamless integrations with dedicated models and providers
Per-persona data masking for Agentic AI. Sensitive fields are masked before prompts reach the LLM and optionally unmasked client-side when rendering results—end-to-end auditable
Allow LLM access to your data and tools using MCP protocol. Built-in MCP server. Dynamically add new MCP tools with no-code.
Set of instructions for LLMs to process data and results tailored to your use case. Modifiable from UI. No code.
RBAC‑like scoping presents only persona‑relevant MCP tools and data to LLM, improving accuracy and governance.
From prompt to production agent—prototype in Copilot, iterate, then simply ask to create Agent with persona, tools, prompts, and workflow auto-packaged
No-code, drag-and-drop approach to easily build and operate agentic workflows. Built-in task library.
AgentOps - Operationalizing the Agentic Platform
Build • Operate • Observe — only on Fabrix.ai
AI Observability
Enterprise-wide Insights, Cost Insights, Visibility for every AI-interaction and more
One dashboard for AI across the business—teams, apps, and providers. See usage, spend, and outcomes at a glance, then drill into the details.
- Snapshot by department / team / app / provider
- Cost, requests, and tokens at a glance
- Leaderboards: top users, agents, personas
- Provider & model mix (share and trends)
- One-click drilldowns from
org → team → run
See where every dollar and token goes. Slice by model, team, user, persona, or agent—then drill into any run
- KPI tiles: cost, requests, tools, tokens
- Cost by LLM / user / persona / agent
- Trends: volume & cost over time
- Tokens: input vs. output, cache savings
- Mix: provider share, agent vs. copilot, tool domains
- Reliability: success rate, failed-run cost
- Drilldowns: user & agent usage tables
Trace any run end-to-end—inputs, tools, models, outputs
- Persona → Prompt → Context → Tools → LLM → Result
- Payload view with redaction & PII masking
- Retries/fallbacks, branching and loops
- Latency breakdown per step; exportable traces
Make AI decisions transparent, auditable, and safe. Every run includes a clear decision trace with rationale, evidence, and policy checks
- Chain of thoughts and reasoning
- Tool call log with parameters and returned outputs
- Prompt & context snapshots; full LLM input/output view (with redaction)
- Run metadata: model/version, available MCP tools, selected persona & scopes
Pick the best model with proof. Run side-by-side “model shootouts” on your use cases and rank quality, cost, and tool-use—so choices are data-driven
- A/B/C tests per use case
- Metrics: accuracy, factuality, coherence, safety
- Ops: tool calls, latency, tokens, $/result
- Human ratings + ground-truth scoring
- Leaderboards, recommendations, audit reports
Agentic AIOps Solution
Enabling Autonomous & AI Driven IT Operations
Prebuilt agents to go. Customize or create new.
Cut through alert noise to explain what broke—and why. Correlates signals across tools to infer the most likely root cause, scope, and impact, with clear next steps
- Probable root cause with confidence and evidence
- Scope & impact: affected services, users, dependencies
- Correlates metrics, logs, alerts, events, incidents
- Uses topology & history to explain “why now”
- Actionable remediation steps or handoff to Remediation Agent
Route incidents to the right team—fast. Analyzes signals and history (similar tickets, services, past resolvers) to recommend the best assignment with confidence and rationale
- Suggested assignment group/owner with confidence score
- Evidence: matching services, components, change history, past resolvers
- One-line incident summary + domain/category tags
- Auto-route to ServiceNow/Jira/Slack (approval-gated)
- Learns from reassignments and resolution feedback
- Detects duplicates/parent-child and links accordingly
Unified telemetry analytics—alerts, events, metrics, logs, incidents. Blends ML baselines with Gen-AI reasoning to flag anomalies, explain impact, and recommend next steps
- Unified intake (Fabrix + ITOM/NMS/EMS)
- ML baselines: seasonality, trends, change points
- Gen-AI correlation: “why now,” likely cause, impact
- Actions: threshold tuning, noise suppression, playbooks/tickets
Executes fixes safely with human approval. Picks up RCA recommendations, creates an approval task (user/CAB/manager), and runs only after approval—fully logged and auditable
- Intake from RCA Agent with proposed actions
- Approval-gated execution (user/CAB/manager) with notifications
- Runs RDAF no-code pipelines or hands off to Ansible/Terraform/Cisco NSO/BPA
- Full logs & results for audit/traceability; status posted back to ticket/Slack
- Post-execution verification and success/failure summary
Daily snapshot of lifecycle + ops health. Reconciles CMDB/inventory with live telemetry to cut noise and surface actions
- Retired Asset Detector: decommissioned but still sending data → suppress/remove
- Upgrade Readiness: EOS/EOL, age, warranty, DEX → refresh/patch list
- Monitoring Hygiene: ghost alerts, orphaned monitors, missing owners/tags
- Stale/Unused: 60–90+ days no login/usage → reclaim/retire
- Outputs: push to ServiceNow/Jira/Slack with savings impact
Fabrix.ai Agentic Operational Intelligence Platform
AI Agents to match your Use Case
Fabrix.ai Joins The NVIDIA Inception Program
We are thrilled to avail the benefits and leverage critical relationships through the NVIDIA Inception Program
How to Build a Business Case for AIOps in your Organization?
AIOps Operating Model & Its Economic Benefits
- ROI -
457
- Benefits PV -
$
6
- NPV -
$
5
- Payback -
4
Fabrix.ai in Media
Check our latest News & Press Releases
Why AI pilots fail — and how agentic operations aim to bridge the enterprise gap
Image courtesy: The Cube Research
Image courtesy: The Cube Research
Telemetry to takeoff: Inside the race for an agentic AI platform that scales
Image courtesy: The Cube Research
Image courtesy: The Cube Research
AI-driven operations take center stage as enterprises shift to proactive intelligence
Image courtesy: The Cube Research
Image courtesy: The Cube Research
The new AI stack: How Fabrix.ai aims to tame enterprise complexity
Image courtesy: The Cube Research
Image courtesy: The Cube Research
Beyond the demo: Why production-grade agentic AI is critical for success
Image courtesy: The Cube Research
Image courtesy: The Cube Research
Actionable discussion on AgentOps and AI-driven IT Operations with Fabrix.ai, theCUBE, and leading industry analysts.
Fabrix.ai: Bringing Agentic AI to IT Operations and Observability
By Bob Laliberte, The Cube Research
Image courtesy: The Cube Research
By Bob Laliberte, The Cube Research
Image courtesy: The Cube Research
Fabrix.ai Recognized in Enterprise Management Associates (EMA) Impact Brief for Production-Ready AgentOps Platform
What AI Agents Are Getting Right – And Wrong – In Enterprise IT (Source: By Kolawole Samuel Adebayo, Forbes)
Image courtesy: Forbes Technology Council
The Network-First Advantage: How Fabrix.ai Redefines Observability from the Ground Up (Source: Guest column by Andrew Mallaband, Growth Engineering)
Key Customer & Partner Engagements
Case Studies
See what customers are saying about Fabrix.ai
Athletic Apparel & Footwear Brand
How a Top Athletic Apparel & Footwear Brand Leveraged Network Asset Intelligence
Healthcare Provider
Leading Healthcare Provider accelerates Datacenter Consolidation with Digtal Asset Intelligence Solution
Telco Service Assurance
Customer is one of the largest Telecom service providers in the United States, with 100B+ revenue.
Incident Resolution
Large Retail Company in the US gains major time savings with Automated Incident Resolution
Alert Noise Reduction & Predictive Insights
Optimize IT Operations: Alert Suppression, Alert Deduplication and Forecasting
Recognized by Leading Analyst Firms
For Observability, AIOps & GenAI
Trending News & Insights
"Fabrix.ai (Formerly CloudFabrix) has demonstrated outperforming leadership in our 2023 Gigaom Radar report. Their Observability Data Modernization Service is a significant development for the OpenTelemetry (OTel) ecosystem. Any Observability provider can now consume non-OTel data using this service to deploy in hybrid environments that are not OTel compliant and still deliver all the OTel benefits. This enables a path for end users and OTel providers for phased OTel adoption. We will be watching this development as it matures," said Ron Williams, Principal Analyst at Gigaom.
Enabling Digital Resiliency Through Data Fabric and AI: Looking into Fabrix.ai (Formerly CloudFabrix)
With large advanced clients across sectors and growing partnerships with leaders like Cisco and IBM, the company is primed to build on its early traction. As enterprises look to harness AI for managing next-generation multi-cloud architectures, Fabrix.ai (Formerly CloudFabrix) brings differentiated capabilities that promise to shape the future of AIOps.
Data-Centric AIOps: The Next Frontier With Observability Pipelines
Data-centric AI is the new frontier in AI, where the models themselves now remain stationary while tools, techniques and engineering practices improve data quality. "Data-centric AI is the discipline of systematically engineering data to build an AI system."
What the growth of AIOps solutions means for the enterprise
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The autonomous enterprise is near, but there are still some missing pieces
Enter an emerging approach, robotic data automation (RDA), which promises to establish the intelligent data supply chain needed for well-functioning AI...
Data quality can make or break efforts to bring artificial intelligence to IT operations
Artificial intelligence for IT operations, or AIOps, could help IT run in a more unattended fashion. But the necessary data may not be ready to sustain it...
How Fabrix.ai (Formerly CloudFabrix) uses robotic data automation fabric to unify observability and AIOps
AIOps needs data to function, but challenges along the AIOps data pipeline mean that AIOps doesn’t often produce the right results...
Market Trends And Predictions For 2022
1. The Data Economy 2. No-Code/Low-Code Platforms For Citizen Developers 3. Cybersecurity And The Rise Of 5G, IoT And Edge AI 4. Rise of Observability, AIOps And Hyperautomation 5. Data Fabric 6. Conversational AI & Explainable AI...
Robotic Data Automation Fabric & AIOps Conference
Data Value Gap - Data Observability and Data Fabric - Missing piece of AI / AIOps. Embark on your Autonomous Enterprise Journey. Unifying Observability, AIOps, Hyper Automation with RDAF. Evolution of AIOps - Log Intelligence and more...
Shailesh Manjrekar, Forbes Councils Member
Shailesh Manjrekar is a business strategist, responsible for AI strategy and strategic alliances, for key vertical markets such as Artificial Intelligence (AI) and Machine Learning (ML), genomics, finance & high-performance computing
Fabrix.ai (Formerly CloudFabrix) featured in Forrester Q2'2022
CloudFabrix featured in Forrester Q2'2022 NowTech report for its wholly owned, single codebase and unified UI functionality and customer wins across Financial, Healthcare and MSP's/Telcom verticals
Fabrix.ai (Formerly CloudFabrix) interviewed for Forrester's NowTech Data Overview and AIOps Reference architecture report
More and more people are talking about business outcomes, but that can’t be fully realized until we have more pervasive digital experience capabilities. The vendor support for collecting and generating this type of sensory data has not yet arrived in AIOps tools, according to the data collected.