AI Data Platform agents

What Is Oracle AI Data Platform? (2:09)

Oracle AI Data Platform gives teams one place to build, deploy, and govern enterprise AI agents alongside the data they need. Using AI Data Platform’s workbench, developers and domain experts can create agentic workflows visually or in code; connect agents to enterprise data, knowledge, models, and tools; test every step; and deploy to production on managed AI compute with built-in identity, controls, and observability.

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Why Oracle AI Data Platform agents?

  • One platform, two ways to build

    Use a drag-and-drop visual canvas or build with Python, the AI Data Platform SDK, utilities, and notebooks. Both paths share the same tools, MCP connectivity, testing environment, compute, and route to production.

  • Connect agents without bespoke integration

    Give agents governed access to SQL, RAG knowledge bases, prompt templates, custom code, HTTP APIs, and remote MCP servers instead of rebuilding every connection for each project.

  • Move from prototype to governed production

    Test agent behavior in the integrated playground, deploy on managed AI compute, protect invocation with role-based access control or OAuth, and monitor sessions, traces, activity, and usage.

Explore agents and agentic development in AI Data Platform

Building enterprise AI agents often stalls at three points: specialist skills, bespoke tool integration, and the infrastructure required for production. AI Data Platform addresses those barriers in one governed environment, so teams can build the agent and its supporting workflow beside the data, knowledge, models, and controls it needs.

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Design and compose agents

Visual agent canvas

Build flows by dragging chat triggers, supervisor agents, executor agents, guardrails, and tool nodes onto the canvas and connecting the path a request should take.

Single- and multi-agent design

Start with the smallest design that meets the need, or use a supervisor to plan, route, and delegate work to focused specialist agents when separation improves reliability, security, or maintainability.

Guardrails and bounded behavior

Apply personally identifiable information (PII), content-moderation, and prompt-injection controls, and restrict each agent to the instructions, memory policy, data, and tools appropriate to its role.

Build with code

Python SDK and utilities

Develop agents in the AI Data Platform workbench using Python, platform utilities, notebooks, and the same model and tool capabilities available to visual flows.

Bring your LangGraph code

Upload an existing LangGraph codebase or create a new LangGraph agent directly in AI Data Platform, with entry files and dependencies managed as part of the agent project.

Open development choices

Use supported Oracle Cloud Infrastructure (OCI) Generative AI models and third-party Python libraries while keeping the agent connected to AI Data Platform data, tools, testing, and deployment services.

Connect agents to enterprise knowledge and action

Built-in tool templates

Use SQL, RAG, prompt, custom code, and HTTP tools for structured queries, document retrieval, reusable generation tasks, bespoke Python logic, and calls to enterprise APIs.

Knowledge bases and RAG

Register document sources, chunk and embed supported files with Oracle AI Vector Search, and retrieve semantically relevant content through agent RAG tools.

Remote MCP connectivity

Connect visual or code-built agents to supported remote MCP servers, expose only the required tools and parameters, and reuse existing services and governed workflows, including human approval steps where the connected system provides them.

Test, deploy, and operate

Integrated playground and testing

Run test conversations, test individual tools, and inspect how agents route requests, call tools, and produce results before deployment.

Managed AI compute and deployment

Use AI compute for the playground and production hosting, then expose deployed agents through stable, authenticated endpoints for applications and supported agent-to-agent communication.

Observability and lifecycle control

Review session history, traces, spans, status, duration, token usage, metrics, errors, and deployment activity across development and production, with permissions and session-retention controls defined by you.

See AI Data Platform agents in action

A unified platform for building, managing, and deploying enterprise AI.

Design and compose multiagent systems in a visual drag-and-drop canvas. Connect prompts, SQL tools, RAG knowledge bases, and multiple specialist agents, and then deploy on managed AI compute.

Visual drag-and-drop canvas view

High-code agent development with full platform power.

Build agents in Python with AI Data Platform SDK and the open source frameworks your team already uses. Develop, version, and deploy agents alongside shared data, tools, and enterprise controls.

High-code agent development view

End-to-end observability built into the platform.

Monitor each agent session with detailed visibility into tool calls, reasoning, inputs, outputs, status, duration, and step-level events. Track latency, token usage, error rates, and business KPIs from development through production.

Agent session monitor view

Flexible agent building, testing, and debugging with traceability and an integrated playground.

Test agents interactively before deployment and inspect each step of their execution in the integrated playground. Use session traces to debug behavior, validate outputs, and maintain a complete audit trail across environments.

Agent testing view

Featured AI Data Platform blogs

Agents vs. Workflows: Where Does the ROI Actually Live?

Most enterprises aren’t failing with AI agents because of the technology—they’re failing because they’re using agents where simple workflows would do the job better. This blog explains the key differences between workflows and autonomous agents, and how choosing the right approach impacts ROI, scalability, governance, and cost. It also provides a practical framework for deciding when agentic AI is truly worth the investment.

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