Fabrix.ai

Agents work today with roughly thirty named commercial products, covering observability and monitoring (Dynatrace, New Relic, Datadog, SolarWinds, PRTG, Zabbix, Prometheus, Grafana), ITSM (ServiceNow, Jira Service Management), network and virtualization (Cisco Meraki, Catalyst Center, Nexus Dashboard, Cisco Firepower, Juniper Mist, Infoblox, VMware vCenter), security and logs (Splunk, Elastic, IBM QRadar), storage and infrastructure (NetApp, BMC, HPE) and cloud (AWS, Azure, GCP), with Dell Technologies, ScienceLogic and PagerDuty as well.

Fabrix.ai lists Cisco, Splunk, IBM, AWS and NVIDIA as partners. The Automation Fabric opens tickets, sends notifications and runs remediations, agents route safe remediation and audit and act on network configuration, and the orchestration layer connects agents, tools, APIs, data sources and workflows. In Fabrix.ai's words, "Your tools don't need to wait for their vendors to ship MCP support.

Fabrix.ai dynamically generates MCP tools for any system," modern or legacy, with zero code. A Universal Connector, guides for integrating data sources and a browsable catalog of 224 bot extensions round it out. Fabrix.ai does not publish a matrix of supported versions.

Sourcefabrix.ai homepage Agents for Any Tool tab and partner pages, docs.fabrix.ai extension catalogread 2026-09-14

Agent Orchestrator is Layer 02 of the platform, "a no-code orchestration layer that connects agents, tools, APIs, data sources, and workflows," running single agents or handing work between agents across four operational domains, from ITOps and NetOps to SecOps and BizOps. No code workflows run "with conditions, approvals, retries, rollback," and the observability layer shows every branch and retry in its traces, along with fallbacks and loops.

Customers build and run agentic workflows by drag and drop with a built in task library, with an Agent Builder for agents and execution flows and a path from prompt to production workflow. The Build Your Own Agent guide sets out the steps, choosing a template and defining the operational intent, shaping responses with the prompt builder and attaching data sources and thresholds, then going on to "configure constraints, escalation logic, or feedback loops."

Agents can also call one another, since they "discover and collaborate with other Fabrix.ai or third-party agents" and "participate in multi-agent workflows across network, app, and service domains," and Fabrix.ai is a member of the AGNTCY collective. Fabrix.ai does not publish versioning or concurrency behavior.

Sourcefabrix.ai homepage Agent Orchestrator layer, fabrix.ai/agenticai orchestration block and BYOA guideread 2026-09-14

Living Ontology, one of the platform's three foundations, is Layer 03, "a continuously updated enterprise knowledge graph that grounds every agent in real assets, topology, dependencies, events, services, owners, and business context," in place of stale snapshots or disconnected alerts, a live graph that keeps mapping every entity, dependency and topology across the IT environment.

It is shared across agents as "one shared intelligence layer," with "full-context reasoning across every team and domain," and Enterprise Ontology is a component of the Agent Orchestrator. The Data Fabric feeds it, providing "rich and automated data enrichment with service topology and business context," with Universal Connector, Telemetry Pipelines, Pipeline Studio and Data Discovery & Enrichment among its named modules and 224 bot extensions.

Domain agents work together over a shared semantic model. Every agent is "grounded in your environment" rather than guessing, and agents working with Splunk "enrich Splunk data with full IT context from the enterprise knowledge graph." Fabrix.ai does not describe a retrieval mechanism, embedding approach or query interface for the graph, though it has its own query language, CFXQL.

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The AIOps agents promise Human-in-the-loop safety, "Your team approves every action," with transparency and control at every step, and for cybersecurity "Every automated action governed by guardrails, explainability, and human approval." Approval steps are part of the orchestration layer, where no code workflows run "with conditions, approvals, retries, rollback," so a customer composes approval steps into a workflow the way they compose conditions.

The Agent Control Plane is "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." Agents "can run autonomously or with approval workflows in place for sensitive actions," and the customer decides per workflow, and rollback lets a team undo an action as well. Fabrix.ai does not describe an approval queue screen, an approver role model, an escalation path or a record of who approved.

Sourcefabrix.ai homepage AIOps and cybersecurity benefit blocks, control plane layer, fabrix.ai/agenticai orchestration building blockread 2026-09-14

Fabrix.ai states it is SOC 2 certified, with "Role-based access, full audit trails, and guardrails on every agent interaction," and keeps a security overview page at /security/. Role based access reaches down to each agent, since AI Personas provide "RBAC for models, tools, prompts," a scoping that "presents only persona-relevant MCP tools and data to LLM," so a buyer can set which tools and data each persona reaches.

The agentic platform adds "a built-in policy engine, role-based access, and audit trails, with enterprise-grade guardrails to restrict agent permissions, scope of actions, escalation pathways." Fabrix.ai's vX release lists single sign on (SSO) among its enterprise foundation capabilities, and data masking and PII redaction round it out. Fabrix.ai does not name the SOC 2 type or auditor, and does not publish a report date, scope or SCIM detail.

Sourcefabrix.ai homepage security block and footer badge, fabrix.ai/agenticai personas and governance rowsread 2026-09-14

A distinct Agent Control Plane records the agents' own runs, with "every AI action traced, governed, and explainable." "Every agent run leaves a complete trace," from the first prompt to the final action, showing every tool call, every model invocation, every branch and retry, with full payload visibility and PII masking built in.

The AI Interaction Flow Tracer lays out the chain from persona and prompt through context and tools to the model and result, with a payload view with redaction, retries, fallbacks and loops, a latency breakdown per step and exportable traces.

AI Explainability promises to "make every AI decision transparent, auditable, and defensible," and each run includes a decision trace of the reasoning chain and tool calls, with evidence and policy checks, "so you can explain any outcome to any stakeholder," and run metadata on model and version, MCP tools, and persona and scopes.

Admins get one dashboard for AI across teams, apps and providers, with drilldowns from organization to team to run, leaderboards by user, agent and persona, and reliability metrics such as success rate and failed run cost. Fabrix.ai does not publish a retention period for traces.

Sourcefabrix.ai homepage Agent Control Plane tabs, flow tracer and explainability panelsread 2026-09-14

The Living Ontology describes the customer's estate rather than what an agent keeps about its own work. Agents "learn context, refine thresholds dynamically, and use feedback to improve detection," and reconfigure suppression logic on their own, which changes a model or a configuration. Smart Context Management offers caching and retrieval for large datasets with savings in tokens and latency, "a high-speed intelligent cache that supplies your AI agents by overcoming context window limitations." It exists to save tokens, and the control plane reports cache savings as a cost metric.

Within a session, Agent-0, the Copilot, works in an iterative loop, "Prototype in Copilot, iterate, then simply ask to create agent with persona, tools, prompts, and workflow auto-packaged," and copilots "allow teams to validate responses before agents are operationalized." Fabrix.ai does not describe a memory layer, a retention period, expiry or purge path, a memory object per user or team, or an agent carrying state from one run into the next.

Sourcefabrix.ai/agenticai smart context and prompt-to-agent tabs, fabrix.ai homepage cost insights cache savingsread 2026-09-14

The platform is "SOC 2 certified, deployable on-prem or in your private cloud," one agentic platform to design, deploy, operate and observe AI agents, on prem or in the cloud, so the software runs inside the buyer's boundary.

Models can run there too, with "on-premise / self-hosted LLMs" deploying open source models within the customer's secure infrastructure, "best for data sovereignty, compliance, or low-latency edge scenarios," so a buyer who cannot let prompts leave the network can still run the product. Agents "run natively on your NVIDIA GPUs," on bare metal, private cloud or hosted endpoints.

Fabrix.ai offers a Sovereign AI use case and installation guides for RDA Fabric and its components. It does not publish a region list or residency commitment for the hosted option.

Sourcefabrix.ai homepage security and deployment blocks, fabrix.ai/agenticai multi-LLM tab and quick start, docs.fabrix.ai installation guidesread 2026-09-14

A browsable Agent Catalog lists the prebuilt agents at /agenticai/agent-catalog/ and /ai-agents/. Eight named families, Agent-0 (Copilot), Digital SRE / AIOps, Observability Agents, NetOps Agents, ServiceOps Agents, CollabOps, SecOps Agents and BizOps Agents, hold roughly thirty individually named agents.

Operations work has the Asset Health Reporting Agent, Uptime & Availability Agent, Anomaly Detection Agent, KPI Forecasting Agent, Alert Optimization Advisor, RCA Agent, Incident Assignment Agent, Remediation Agent and DEX Analyst. Network and asset work is covered by the Config Compliance Agent, ACL Change Audit Agent, Netflow Analysis Agent, BGP & Routing Intelligence Agent, SACM Asset Intelligence Analyst and Asset Lifecycle & Capacity Analyst.

For Splunk and the service desk there are the Splunk SIEM Agent, ITSI Analyst & Resilience Agent and Service Desk Agent, and the security set runs to the Automated Reconnaissance Agent (VAPT), Exploit Assistant, User Behavior Analysis Agent, Patch Prioritization Agent, Compliance Mapping Agent and Control Validation Agent. Fabrix.ai suggests adopting them a team at a time, "Start with one team, then scale across ITOps, NetOps, SecOps, SRE, and BizOps."

The Quick Start suggests customers "onboard out-of-the-box agents" to speed deployment, using prebuilt agents for common telecom and enterprise use cases such as RCA, Anomaly Detection and Service Migration, and "use out-of-the-box agents, customize them for your workflows, or build your own." Solution Packs appear separately under the Automation Fabric, and the 224 bot extensions are integration building blocks.

Sourcefabrix.ai homepage agent tabs and family lists, fabrix.ai/agenticai products navigation and quick startread 2026-09-14

The Data Fabric ships Telemetry Pipelines, a Universal Connector and Pipeline Studio and supports streaming and batch ingestion for hybrid IT environments, and Fabrix.ai treats event driven architecture, with real time streaming and orchestration, as a required capability. Each agent family acts in real time.

NetOps agents "detect config drift and policy violations the moment they happen", observability agents "identify emerging issues and forecast failures before they impact services" and "correlate and suppress alert noise before it reaches your team", and asset agents keep asset records current. Agents also trigger each other, with action nodes invoking other agents, agents that "discover and collaborate with other Fabrix.ai or third-party agents," and work passing between agents across the four operational domains. Agents deploy on a schedule or on an event trigger. Fabrix.ai does not describe a webhook endpoint, cron syntax, an event subscription model or an inbound API trigger.

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Multi LLM Choice leads the platform's capabilities, "Support featured LLMs. On-prem or cloud. Seamless integration," and is NVIDIA ready. Fabrix.ai calls it a differentiator that "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."

The Quick Start has the buyer "select your preferred AI deployment model" from two options, cloud LLMs from OpenAI, Anthropic or other public APIs, or on premises or self hosted LLMs, with open source models such as LLaMA or Mistral running within the customer's secure infrastructure, adding that "Fabrix.ai supports hybrid and multi-LLM architectures." Customers can bring their own model and host it themselves.

The control plane reports the mix of providers and models, with share and trends, and cost by LLM, user, persona and agent, so a customer can see which model each agent uses and what it costs. AI Personas act as "RBAC for models, tools, prompts," letting admins control which models a role may reach, and Evaluators lets a buyer "run side-by-side model evaluations on your own data and rank quality, cost, and reliability before you deploy." Fabrix.ai does not publish a model list, version matrix or default.

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Fabrix.ai offers a RESTful API, served as a Swagger interface at /beginners_guide/swagger_api/, and a Command Line Interface guide at /beginners_guide/sdk/, both public on its documentation site at docs.fabrix.ai. The developer material also covers architecture, installation and data ingestion guides, CFXQL, a named query language with its own reference, Grok pattern references, and a catalog of 224 bot extensions browsable by name, among other references and examples.

The platform also runs an inbound MCP server. "Fabrix.ai includes a powerful MCP server that exposes data pipelines and enriched telemetry, automation workflows and runbooks," which "allows external agents, tools, or orchestration layers to seamlessly access and interact with Fabrix.ai's real-time intelligence." Universal MCP Server & Tooling is a component of the Agent Orchestrator.

Sourcedocs.fabrix.ai index, RESTful API and CLI guides, fabrix.ai/agenticai MCP server descriptionread 2026-09-14

Evaluators sits in the Agent Control Plane beside Agent Observability and Cost & Token Insights, and its message is "Don't guess which model is best for your use case," inviting a buyer to "run side-by-side evaluations on your own data and rank quality, cost, and reliability before you deploy."

The agent lifecycle runs through review, a dry run that simulates without executing and a test on sample data for limited iterations, then deployment and decommissioning.

Evaluations include A/B/C tests per use case, metrics for accuracy and factuality, coherence and safety, operational measures of tool calls, latency and tokens, and cost per result, human ratings and scoring against ground truth, and leaderboards, recommendations and audit reports, all run by the buyer per use case on its own data.

In production, the observability layer reports quality, accuracy and coherence metrics for every run, plus success rate and failed run cost, and the Quick Start closes with testing on synthetic or historical data and iterating on prompts, thresholds or workflows based on real time performance. Fabrix.ai does not publish its methodology or regression suite detail.

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Fabrix.ai does not describe a hosted or local browser, a desktop session or remote computer control that the agent drives. Agents act programmatically. The Automation Fabric runs workflows and remediations, the orchestration layer "connects agents, tools, APIs, data sources, and workflows," and integrations reach roughly thirty named systems through connectors, bots and generated MCP tools.

Fabrix.ai "dynamically generates MCP tools for any system," modern or legacy, "API-driven or not," with zero code, from modern APIs to decades old legacy systems, but it does not say how it reaches a system with no API, and no screen, browser, cursor, UI element or visual model is named as a target. Streaming telemetry collection feeds the knowledge graph.

Sourcefabrix.ai homepage Agents for Any Tool tab, fabrix.ai/agenticai MCP tools block, docs.fabrix.ai extension catalogread 2026-09-14