Agentic Operational Intelligence

FabrixONE
AI Platform

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One core · every agent governed

Essential Architecture for
Specialist & Generalist AI Agents

Three foundational building blocks connect enterprise data and systems
to agents that can reason, access and act.

Hand-drawn architecture: enterprise data and systems feed Telemetry Pipelines, the Universal MCP Server, and Ontology and Reasoning, supporting specialist and generalist AI agents. No need to manage many disparate MCP servers. Callouts highlight 2,000+ data bots, 250+ data integrations, and experience processing 1T+ events and millions of assets.

Double-click: inside the agentic layer

Build, Operate and Observe Production Agents

One runtime to create agents, connect your systems, ground them in your data, govern every run and verify every interaction. Pre-built agents and solutions are what you deploy on top — not what the platform is.

Fabrix.ai agentic AI platform architecture for IT Ops and SRE Ops — build, connect, ground, operate and observe production agents
Production-grade Agentic AI

The Three Foundations of
Production-Ready AI Agents

The double-click: inside the FabrixONE core, three products make agents production-ready - grounded in context, orchestrated into work, and governed on every run.

Agentic Control Plane
Agent Harness
Ontology & Reasoning
Layer 01 / Control

Agent Control Plane

Every AI action traced, governed, and explainable

A shared control layer for observability, approvals, policy enforcement, audit trails, PII masking, model usage, token spend, and agent behavior across teams, tools, and LLM providers.

3layer agent control plane
Layer 02 / Coordination

Agent Harness

From prompt to production workflow

A no-code orchestration layer that connects agents, tools, APIs, data sources, and workflows - enabling single-agent execution or multi-agent handoffs across ITOps, NetOps, SecOps, and BizOps.

Agent Harness: universal tool connectivity, multi-agent delegation, context caching and a no-code workflow builder
Layer 03 / Context

Ontology & Reasoning

Always-on enterprise context for every agent

A continuously updated enterprise knowledge graph that grounds every agent in real assets, topology, dependencies, events, services, owners, and business context - not stale snapshots or disconnected alerts.

AgentOps Capabilities

Enterprise Grade Capabilities from build to observe - guardrailed,
auditable agents engineered for enterprise scale.

Copilot

Prototype an agent in conversation. Test tool calls, iterate, then package it for production.

Agent Studio

Effortlessly create your own agent with a prompt or start from a template. Customize & iterate or activate right away.

Multi LLM Choice

Support featured LLMs. On-Prem or Cloud. Seamless Integration. Nvidia Ready

Smart Context Mgmt.

Provides context caching for optimal token usage, allows LLMs to work with very large datasets.

Security & Guardrails

Enforce safety, policy, and intent checks on every run-blocking non-compliant prompts and destructive actions-via seamless integrations with dedicated models and providers

Data Protection

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

MCP Tools

Allow LLM access to your data and tools using MCP protocol. Built-in MCP server. Dynamically add new MCP tools with no-code.

Grounding & RAG

Ground agents in your documents, knowledge bases and live topology - answers stay anchored to your environment.

Prompt Templates

Set of instructions for LLMs to process data and results tailored to your use case. Modifiable from UI. No code.

AI Personas

RBAC‑like scoping presents only persona‑relevant MCP tools and data to LLM, improving accuracy and governance.

Prompt to Agent

From prompt to production agent-prototype in Copilot, iterate, then simply ask to create Agent with persona, tools, prompts, and workflow auto-packaged

Orchestration

No-code, drag-and-drop approach to easily build and operate agentic workflows. Built-in task library.

Human-in-the-Loop

Approval gates on high-stakes actions, with full context in your workflow and a complete audit trail.

Agent Triggers

Cron-style schedules and event triggers - ticket created, threshold breached, deployment finished. Every run logged.

The Access Layer

Universal MCP Server

Agents don’t struggle with reasoning - they struggle with access.

Fabrix dynamically wraps REST, SSH/CLI, message systems, and vendor MCP tools into one seamless agent interface, unifying connectivity, authorization, and data mapping into a single cohesive access layer.

AI agents cannot access all your enterprise data and systems AI agents can access all your enterprise data and systems AI AGENTS FABRIX AGENTS 3RD PARTY AGENTS MCP Limited MCP MCP No MCP MCP Limited MCP MCP UNIVERSAL MCP SERVER CONTEXT & CACHE prune / reuse / route Databases REST APIs Network Devices SaaS Apps Servers Message Bus Legacy Systems Files & Storage Monitoring & Ops ITSM / Biz Apps Other Sources Token Burn 12,400 LLM ! NEURAL REASONING LLM RECEIVES Context Overflow $ High Token Usage ! Incorrect / Hallucinated Answers Focused Context Low Token Usage Accurate Answers

Agentic Data Federation

Agents go to the data.
The data stays put.

Enterprise telemetry is fragmented and centralising it no longer scales. Fabrix federates insight in real time - with zero copy.

Real-time access at the source
Reduced integration overhead
Faster insight
Lower platform cost
Any agent, any interaction Agent Agent Agent Agent Agent Agent Agentic Data Federation Direct access · Zero copy Cloud platforms Applications Devices & IoT Logs & events Databases Infrastructure Data stays where it lives

Agent Control Plane

Every AI interaction - traced, explained, and under control.

One dashboard for AI across your entire business - teams, apps, and providers. See usage, outcomes, and spend at a glance, then drill into any team, agent, or run.

  • 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: org → team → run
Fabrix.ai Enterprise-Wide AI Observability Dashboard

Every agent run leaves a complete trace - from the first prompt to the final action. See every tool call, every model invocation, every branch and retry, with full payload visibility and PII masking built in.

  • Persona → Prompt → Context → Tools → LLM → Result
  • Payload view with redaction & PII masking
  • Retries, fallbacks, branching and loops
  • Latency breakdown per step - exportable traces
Fabrix.ai AI Interaction Flow Tracer

Don't guess which model is best for your use case - prove it. Run side-by-side model evaluations on your own data and rank quality, cost, and reliability before you deploy.

  • 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
  • Drift detection: daily analysis catches degradation early
  • Continuous improvement: prescriptive fixes backed by real failures
Fabrix.ai Model Evaluators

Know exactly where every dollar and token goes. Slice by model, team, user, persona, or agent - then drill into any run to understand what drove the cost.

  • 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
  • AI Projects mapped to departments, teams and workstreams
  • Budgets, schedules and caps allocated per project
  • Agents assigned to projects, spend attributed automatically
Fabrix.ai Cost and Token Insights Dashboard

Make every AI decision transparent, auditable, and defensible. Every run includes a complete decision trace - reasoning chain, tool calls, evidence, and policy checks - so you can explain any outcome to any stakeholder.

  • Chain of thought and full reasoning trace
  • Tool call log with parameters and returned outputs
  • Prompt & context snapshots - full LLM input/output view with redaction
  • Run metadata: model, version, MCP tools, persona & scopes
Fabrix.ai Audit & Explainability Dashboard

Frequently
asked questions

The Fabrix.ai Agentic AI Platform is an enterprise platform that lets teams operationalize AI agents at scale for modern autonomous operations. It unifies data and Agentic AI for IT operations, providing Gen AI, a Copilot, an Agent Lifecycle Manager, an Orchestrator, Guardrails, RAG, and multi-LLM integrations so organizations can build, operate, and observe agents in one place.

AgentOps is Fabrix.ai's full-lifecycle control model for enterprise AI agents, summarized as Build, Operate, and Observe. It delivers enterprise-grade capabilities such as multi-LLM choice, smart context management, guardrails, data protection, MCP tools, prompt templates, AI personas, prompt-to-agent creation, and orchestration so agents are guardrailed, auditable, and engineered for enterprise scale.

You can create an agent from a single prompt or start from a template, then customize and iterate or activate it right away. The platform supports prompt-to-agent creation, multi-agent interactions, and agent integration with RAG, so a prototype built in Copilot can be packaged into a production agent with its persona, tools, prompts, and workflow automatically.

Agentic Data Federation lets agents access data where it lives and federate insights in real time with zero copy. Because centralizing fragmented enterprise data no longer scales, agents go to the data and unify insights without moving it, which delivers real-time access at the source, reduced integration overhead, faster insights, and lower platform cost.

The Universal MCP Server bridges AI agents and legacy infrastructure without manual refactoring or custom connectors. It auto-generates Model Context Protocol (MCP) tools that turn existing APIs and databases into agent-ready assets in minutes, enabling interoperability across different AI models and tools with zero-code tool generation.

Fabrix.ai offers multi-LLM flexibility with universal model support, including open-source options and Small Language Models (SLMs). You can bring your own model (BYOM) to keep sensitive data under your control, and cost-and-performance routing automatically sends each prompt to the most optimal model based on complexity, latency, and budget. Models can run on-premises or in the cloud and are NVIDIA-ready.

The platform provides granular control over what agents can see and do. It includes guardrails and PII protection with real-time masking of sensitive data, role-based access control to define precise permissions for users and agents, and comprehensive audit trails that log every agent interaction for compliance and forensic purposes.

Multi-Domain Agents are autonomous AI agents that collaborate across observability, infrastructure, network, security, and collaboration systems to investigate, correlate, and resolve incidents end-to-end. They include Digital Employee Experience (DEX), Root Cause Analysis (RCA), VPN Health Check, and CVE Exposure and Impact agents, and they use cross-domain signal correlation to deliver faster MTTR through autonomous workflows.