Arango Agentic AI Suite

The Arango Agentic AI Suite is the agent-focused capability layer of the Arango Contextual Data Platform. It connects LLMs to governed enterprise data using contextual retrieval and tool-based execution—so agents and co-pilots can deliver relevant, policy-aligned outcomes with human-guided control. 

Arango Contextual Data Platform

Outcomes and Benefits

The Arango Agentic AI Suite enables organizations to operationalize AI agents and co-pilots on governed enterprise data—improving decision quality, accelerating issue resolution, and supporting explainable, policy-aligned outcomes across production workflows. The benefits below reflect the impact teams can achieve once agentic workflows are deployed on contextual enterprise knowledge.

OutcomeBenefits
Context-Grounded Agent WorkflowsAI agents deliver more relevant, consistent responses by retrieving enterprise context grounded in entities, relationships, and evidence.
Governed Agent ActionsAgents access enterprise data through policy-aligned tools, enabling guided decisions while maintaining compliance and operational control.
Natural Language Data InteractionBusiness and technical users can investigate issues and interact with enterprise data without query expertise, accelerating troubleshooting and exploration.
Improved Signal DetectionAgents identify relationship-driven patterns across enterprise data, improving detection of risk, anomalies, and operational dependencies.
Flexible LLM DeploymentOrganizations can align AI deployments to security and compliance requirements using hosted or enterprise-managed model endpoints.
Production-Ready Agent DevelopmentEngineering teams can build, test, and deploy context-aware agent workflows using APIs and repeatable pipelines.

Core Components and Capabilities

The Arango Agentic AI Suite includes the following components for building and operating AI agents and co-pilots on contextual enterprise data:

AutoGraph automatically structures enterprise data into connected knowledge graphs and domain-specific knowledge models, enabling AI agents to retrieve relevant business context through domain-aware retrieval strategies at inference time.

Organize structured and unstructured enterprise data into contextual knowledge graphs and combine lexical, semantic, and graph traversal retrieval to ground LLM responses in enterprise context.

Provides safe, read-focused tools for AI assistants to discover schema, sample documents, receive AQL guidance, and execute contextual queries.

Integrate with hosted or enterprise-managed LLM endpoints, with Triton Inference Server available as an option for organizations requiring self-managed model deployment.

Translates natural language intent into executable AQL queries for contextual retrieval across multimodel enterprise data.

Enables link prediction, node classification, and embedding generation to enhance downstream AI applications.

Dedicated compute for graph algorithms that support pattern detection and relationship modeling across enterprise datasets.

Optional environments for developing and testing GraphRAG workflows, GraphML models, and AI applications interactively.

Integrate with hosted or enterprise-managed LLM endpoints, with Triton Inference Server available as an option for organizations requiring self-managed model deployment.

How It Fits in the Platform

The Agentic AI Suite runs on ArangoDB’s multimodel data foundation and the Arango Platform Suite. AutoGraph provides contextual retrieval, while the suite powers agents and co-pilots to access governed enterprise knowledge and deploy policy-aligned AI workflows across SaaS, self-managed, on-premises, and hybrid environments.

Getting Started

To begin using the Arango Agentic AI Suite, explore the official documentation at docs.arango.ai/ai-suite/. The documentation provides detailed guides, API references, tutorials, and deployment instructions to help you leverage the full capabilities of the suite.

Ready to activate AI with context, trust, and scale?