Oracle AI Database Private Agent Factory is Oracle’s no-code platform for building, testing, deploying, and managing enterprise AI agents close to Oracle data. The core proposition is: build agents visually, connect them to enterprise data and tools, choose private or cloud model endpoints, and deploy them without moving sensitive data into unmanaged AI tooling. Private Agent Factory is part of the Oracle AI Database 26ai agentic AI stack, with deployment options spanning cloud, multicloud, on-premises, and air-gapped environments.
We have been receiving many questions from customers about Oracle AI Database Private Agent Factory, especially around deployment choices, model options, use cases, security, and cost. To make those answers easier to find in one place, this blog brings together the most frequently asked questions across key categories. We recommend bookmarking this page, as we will keep updating the FAQs regularly with each new release and as more customer questions come in.
Private Agent Factory Overview
What is Oracle AI Database Private Agent Factory? · Why is it called “Private”? · Who is it for? · Is it no-code or low-code? · How does it relate to Oracle AI Database 26ai? · What pre-built agents are included?
What is Oracle AI Database Private Agent Factory?
It is a no-code platform for building and deploying production AI agents and workflow automations that can connect to any variety of systems including Oracle AI Databases, SaaS Applications, MCP tools, enterprise data, and LLM providers of your choice.
Why is it called “Private”?
Because it is designed to run close to your data – on-premises, in your OCI tenancy, in other cloud providers, or in controlled cloud environments – so agents can operate without unnecessarily sharing data with third-party AI tools. Private Agent Factory can run where your Oracle AI Database is.
Who is it for?
Database Administrators, Database SREs, Business analysts, domain experts, engineers, and enterprise teams that need governed AI agents over private business data and applications.
Is it no-code or low-code?
The primary interface is no-code, using drag-and-drop nodes, templates, testing, and publishing; developers can still extend it through SDK, REST, OpenAPI, MCP, and database integrations.
How does it relate to Oracle AI Database 26ai?
Private Agent Factory is part of Oracle AI Database 26ai agentic AI story, using Oracle AI Database for agent memory, runtime data, vector store, hybrid search, governance, and data access.
What pre-built agents are included?
March 2026 release includes Knowledge Agent and Data Analysis Agent. We plan to release more pre-built agents in coming weeks including Database Knowledge Agent, Structured Data Analysis Agent, and Deep Data Research Agent.
What release is current in the docs?
Oracle’s Agent Factory user guide is for Release 25.3, with April 2026 documentation; the “What’s New” page references release 25.3.0.0.8 updates.
Use cases
What are the strongest use cases? · Can it power internal knowledge search? · Can it act as a data analyst? · Can it support customer service? · Can it support compliance and policy Q&A? · Can it automate engineering or QA workflows? · Can it support finance, HR, or operations workflows? · When is it probably not the right fit?
What are the strongest agentic use cases for Private Agent Factory?
The strongest use cases are multi-system workflow orchestration (Oracle Database, EBS, PeopleSoft, JD Edwards, Fusion Apps, OIC, third party SaaS, etc), enterprise knowledge search, structured data analysis, workflow automation, and multi-agent orchestration over governed Oracle data.
Can it power internal knowledge search?
Yes. Knowledge Agents are essentially managed RAG over enterprise-approved content with traceable, grounded responses. Knowledge Agents continuously ingest unstructured data from websites, REST APIs, local files, Sharepoint, Google Drive, and more.
Can it act as a data analyst?
Yes. Data Analysis Agents run auto-exploration to analyze data, translate questions to SQL, run queries, and return charts, tables, and explanations.
Can it support customer service?
Yes, when support answers can be grounded in approved documents, policies, tickets, product content, or operational data sources connected through Agent Factory.
Can it support compliance and policy Q&A?
Yes, provided the knowledge base contains approved policy documents and security teams validate access controls, retrieval quality, and response traceability.
Can it automate engineering or QA workflows?
Yes. Oracle uses such agents built with Private Agent Factory internally. Check this blog describing an internal example using supervisor/sub-agents, MCP tools, REST exposure, and database-backed job/task tracking for testing automation.
Can it support finance, HR, or operations workflows?
Yes. Several customers are using Private Agent Factory to build agents for invoice processing, inventory management, HR helpdesk assistant, etc.
When is it probably not the right fit?
It is a weaker fit when the organization does not need near-data governance, or mainly wants a general-purpose SaaS chatbot disconnected from enterprise data.
Deployment Options
Where can it run? · Can it run on-premises? · Can it run in public cloud? · Does it support multicloud Oracle Database deployments? · What is Quickstart mode? · What is Production mode? · What are the basic resource requirements? · What platforms are supported?
Where can it run?
Private Agent Factory is a containerized application that can run wherever Oracle AI Database run – on OCI, any public cloud, or on-premises.
Can it run on-premises?
Yes. Exadata Cloud@Customer, Compute Cloud@Customer, Exadata Database Machine, Oracle Database Appliance, Oracle Private Cloud Appliance, and Linux x86-64 platforms.
Can it run in public cloud?
Yes. Oracle lists Autonomous AI Database, Exadata Database Service, Base Database Service, and Oracle AI Database on OCI Compute VMs.
Does it support multicloud Oracle Database deployments?
Yes. Oracle Database@Azure, Oracle Database@Google Cloud, and Oracle Database@AWS are supported.
What is Quickstart mode?
Quickstart mode creates a local Oracle AI Database 26ai Free container instance, so teams can experiment without manually preparing a database.
What is Production mode?
Production mode uses user-provided database and LLM endpoint details, letting teams connect their own production Oracle AI Database 26ai and supported model services.
What are the basic resource requirements?
Resource requirements depend on the complexity and number of agents. For getting started, we recommend 250 GB disk / 24 GB RAM / 8 OCPUs.
What OS and platforms are supported?
Oracle Linux 8 on AMD x86-64 and ARM64. MacOS on Apple Silicon and Intel chipsets.
Model and Model Providers
Which model providers are supported? · Which OCI Generative AI models are recommended? · Does it support OpenAI models? · Does it support Gemini? · Can it use local models? · Can it use self-hosted vLLM endpoints? · What embedding models are supported? · Can I use smaller language models instead of frontier LLMs?
Which model providers are supported?
Private Agent Factory supports OCI Generative AI, OpenAI, Gemini, Ollama, and vLLM/self-hosted endpoints, depending on model type and configuration. New model providers are added in every release.
Which OCI Generative AI models are recommended?
Choice of model depends on the use case, cost, and response time requirements. We recommend testing with different models to identify the best fit.
Does it support OpenAI models?
Yes. Custom base URL will be supported in future releases.
Does it support Gemini?
Yes. Release 25.3.0.0.8 added Gemini LLM and embedding model support. Gemini LLMs can be connected using service account or API keys.
Can it use locally hosted models?
Yes, Private Agent Factory comes bundled with local embedding model, multilingual-e5-base. Ollama is also supported for local hosting.
Can it use self-hosted vLLM endpoints?
Yes, vLLM self-hosted model endpoint can be configured using a model ID, URL, and port.
What embedding models are supported?
Oracle lists local multilingual-e5-base (bundled), OCI Cohere embeddings such as cohere.embed-v4.0, vLLM/Ollama endpoints, and Gemini embedding models.
Can I use smaller language models instead of frontier LLMs?
Yes. Smaller language models can be used through supported serving options such as vLLM, Ollama, OCI Generative AI, and OpenAI.
Data sources, tools, and integrations
What data can Knowledge Agents use? · How does Knowledge Agent ingestion work? · What file types are supported? · Can it crawl websites? · Can it analyze structured Oracle data? · Does it support MCP? · Does it support REST and OpenAPI tools?
What data can Knowledge Agents use?
Knowledge Agents can use configured and approved content from SharePoint, Google Drive, internal sites, uploaded files, file systems, and public web sources.
How does Knowledge Agent ingestion work?
The ingestion path is crawling, parsing, storing, chunking, embedding, and vector database ingestion.
What file types are supported?
Oracle’s file source page says .pdf, .txt, and .rtf files up to 1 GB each are supported.
Can Knowledge Agent crawl websites?
Yes. Web sources can crawl public unauthenticated pages, with URL filters, crawl depth, crawl frequency, and proxy configuration.
Can pre-built Data Analysis analyze structured data in Oracle Database?
Yes. Data Analysis Agents connect to Oracle Database 19c and later, understand schemas, generate SQL, and return explanations, tables, charts, and queries.
Does it support MCP?
Yes. Private Agent Factory supports MCP tools, can attach multiple MCP servers to one agent, and supports Streamable HTTP transport with multiple authentication methods such as direct, bearer token, oAuth, Auth Request
Does it support REST and OpenAPI tools?
Yes. you can upload OpenAPI 2.0/3.0 JSON to auto-create tools and orchestrate SQL and REST in one flow. These are useful when connecting with Fusion Apps or other SaaS apps.
Security and governance
How does it protect enterprise data? · Does data have to leave the enterprise boundary? · Does it support SSO? · Does it support role-based access? · Are answers grounded and traceable? · How does Oracle position database-level security? · Can it run in air-gapped environments? · What should teams do for MCP security?
How does it protect enterprise data?
It protects data by running agents close to the database, supporting private/local model paths, and using Oracle Database security controls rather than relying only on application-layer filtering.
Does data have to leave the enterprise boundary?
No. Oracle says Agent Factory can run in public clouds or on-premises without requiring customers to share data with third parties; model endpoints can also be private or local depending on architecture.
Does it support SSO?
Yes. Oracle documents SSO support for Oracle IDCS, Google, Okta, Auth0, Microsoft Azure AD, and Amazon Cognito.
Does it support role-based access?
Yes. Private Agent Factory provides three user roles: Chat-only Users, Editors, and Administrators.
Are answers grounded and traceable?
Yes for Knowledge Agents responses are grounded and traceable to enterprise-approved sources, with source links.
How does Oracle position database-level security?
row-, column-, and cell-level controls, dynamic masking, SQL Firewall, and Deep Data Security so users and agents only see authorized data.
Can it run in air-gapped environments?
Yes. it can operate in secure isolated environments.
What should teams do for MCP security?
Use least-privilege tool design, authenticated MCP endpoints, scoped database users, and strong tool descriptions so the model selects only appropriate tools.
Cost and licensing
Is there an additional Agent Factory license cost? · What costs still apply? · Does model choice affect cost? · Does Quickstart cost less than Production? · How should teams budget for it?
Is there an additional Agent Factory license cost?
Private Agent Factory is included as a no-cost add-on to Oracle AI Database 26ai and is available at no additional cost to Oracle AI Database customers.
What costs still apply?
Expect costs for database infrastructure, compute, storage, network, GPUs if used, OCI services, and external model/API usage.
Does model choice affect cost?
Yes. OCI Generative AI, OpenAI, Gemini, local Ollama, and vLLM/self-hosted endpoints have different cost and operations profiles.
Does Quickstart cost less than Production?
Not automatically. Quickstart lowers setup friction by using local containers, but production cost depends on your database, compute, model, storage, and network architecture.
How should teams budget for it?
Budget by agent workload: number of users, prompts, tools, retrieval volume, embedding refresh frequency, model endpoint choice, and availability requirements.
Operations, limitations, and getting started
How do agents get published? · Can applications call agents through APIs? · Does Agent Builder have memory? · Are Knowledge Agents conversational? · Is observability & monitoring built in? · What known issues should teams check? · How do teams start hands-on? · What should a first pilot prove?
How do agents get published?
After testing, teams publish an agent and copy an Agent API Endpoint URL or SDK for external invocation. Agents can be invoked programmatically, business event triggers, embedded in customer apps such as APEX, Streamlit, Visual Builder, etc.
Can applications call agents through APIs?
Yes. Published Knowledge Agents, Data Analysis Agents, and Agent Builder flows can be called via REST-style POST requests.
Does Agent Builder have memory?
Yes. Custom flows built in Agent Builder support conversational memory, and the Agent Builder page states the previous 10 queries or messages are retained as conversational history. Private Agent Factory will provide first-class support for Oracle Agent Memory in upcoming releases.
Are Knowledge Agents conversational?
Not currently in the same way. Knowledge Agents are stateless and respond to one question at a time.
Is observability and monitoring built in?
Private Agent Factory uses Oracle AI Database to store all configuration and agent conversation history and will support exporting Wayflow traces to OpenTelemetry compatible endpoints in next release.
How do teams start hands-on?
Start with Oracle’s LiveLabs workshop, which covers installation, first agent creation, template bootstrapping, and Agent Builder customization.
What should a first pilot prove?
A pilot should prove secure data access, answer traceability, model quality, cost per workflow, operational ownership, and whether the agent can be embedded into a real application or process.
Practical evaluation checklist
Evaluate five things before deploying enterprise agents in production:
- Data boundary: where prompts, retrieved context, embeddings, and outputs are stored and processed.
- Model strategy: OCI GenAI, OpenAI, Gemini, Ollama, vLLM, or a hybrid model pattern.
- Access model: SSO provider, roles, database users, and tool-level least privilege.
- Agent quality: grounded answers, source links, SQL accuracy, hallucination handling, and human review.
- Run model: who owns deployment, patching, logs, monitoring, diagnostics, and cost controls.
Private Agent Factory is strongest when you want to run agents close to your business critical data, and is seamless to implement when you are already using Oracle Database as the system of record and want agents to operate under the same governance model rather than outside it.
Resources
- Install from OCI Marketplace
- Download and Install Anywhere
- Product documentation
- Try in LiveLabs Sandbox
- Blog: Simplifying Contract Renewals: An AI Agent for EBS with Private Agent Factory
- Blog: Turning Invoice Compliance into a Scalable, Auditable Workflow with Private Agent Factory
- Blog: Secure Agents, Built for Enterprise
