diegoecab/oci-deal-accelerator
1
1
Fork 2
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
b77a855a3e3ed5b693dcd4c96b5960bab25f92ad
oci-deal-accelerator/kb/diagram/assets/archcenter-refs/deploy-agentic-ai-agent-platform/_description.md
root b30a4f0d32 Diagram generation: ref-arch-driven procedure + spec validator + KB enrichment 
The diagram path now follows a documented standard procedure (lookup
the closest Oracle Architecture Center reference → confirm components
→ author absolute_layout → spec validator → render → visually verify)
and ships persistent guardrails so layout regressions can't recur.

Persistent procedure changes (apply to all users, all sessions):
- tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN,
  CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either
  renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow
  bugs that the post-render drawio validator missed.
- tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the
  spec validator before emitting any output. Adds mysql / mysql_heatwave
  type aliases.
- tools/archcenter_pattern_lookup.py — scores against cached page
  descriptions (not just the 1-line summary), supports --queries for
  multi-fragment composition, and applies synonym expansion via
  kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches
  "load balancer high availability availability domain".
- kb/architecture-center/synonyms.yaml — canonical synonym table
  (load balancer, autonomous database, data guard, …) used by the
  lookup scorer.

KB enrichment:
- tools/archcenter_description_fetcher.py + 121 cached _description.md
  under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime
  dependency on docs.oracle.com when authoring specs and feeds the
  pattern-lookup scorer.
- 110+ cached .drawio / .svg / .png references for offline reuse,
  plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio).

Documentation:
- docs/skill/output-formats.md — new "Standard diagram-generation
  procedure (MANDATORY)" + geometry rules + the new validator entry.
- SKILL.md option 2 — references the mandatory procedure.
- README.md — describes the spec validator, archcenter_pattern_lookup
  and description fetcher, and updates the KB-health table.

Tooling that backs the procedure (cumulative across recent sessions):
tools/archcenter_case_runner.py, archcenter_batch_driver.py,
archcenter_zip_downloader.py, drawio_visual_validator.py,
drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py,
oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-25 21:15:21 -03:00

6.8 KiB
Raw Blame History

Deploy agentic AI with Oracle Cloud Infrastructure AI Agent Platform

Summary (catalog)

Enterprise AI chatbot integrating Visual Builder, Digital Assistant, and OCI AI Agent Platform. RAG from unstructured data, structured queries via ADB-S, custom business logic through serverless Functions.

Architecture (fetched from source)

Architecture

This is an enterprise-grade OCI architecture for building advanced generative AI chatbots that blend conversational AI, secure data access, RAG, and extensible business logic.

Users interact with a web application built using Oracle Visual Builder . The application integrates with Oracle Digital Assistant , which manages conversational interactions and routes user queries to the appropriate backend services. The Digital Assistant connects to the OCI Generative AI Agent Platform, which intelligently orchestrates requests to different tools:

  • The RAG Tool retrieves relevant information from OCI Object Storage for context-aware responses.

  • The SQL Tool queries structured data in the Oracle Autonomous AI Database ( Oracle Autonomous Transaction Processing ) to answer specific data-driven questions.

  • The Custom Tool invokes serverless functions for specialized tasks like document understanding or integrating external data (e.g., weather).

Together, these services leverage Oracle Cloud Infrastructure to deliver an intelligent, responsive, and extensible AI-driven experience that combines conversational AI, real-time data access, and custom functionality.

The following diagram illustrates this reference architecture.

Description of the illustration agentic-ai-oci-ai-agent-arch.png

agentic-ai-oci-ai-agent-arch-oracle.zip

The architecture has the following components:

  • Region An OCI region is a localized geographic area that contains one or more data centers, hosting availability domains. Regions are independent of other regions, and vast distances can separate them (across countries or even continents).

  • Oracle Services Network The Oracle Services Network (OSN) is a conceptual network on OCI that is reserved for Oracle services. These services have public IP addresses that you can reach over the internet. Hosts outside Oracle Cloud can access the OSN privately by using Oracle Cloud Infrastructure FastConnect or VPN Connect. Hosts in your VCNs can access the OSN privately through a service gateway.

  • Oracle Visual Builder Oracle Visual Builder is an intuitive development experience on top of a development and hosting platform that empowers you to create engaging responsive applications. Focusing on ease of use and a visual development approach, it provides an easy way for you to create applications that are hosted in Oracle’s secure and scalable cloud platform.

  • Oracle Digital Assistant Oracle Digital Assistant is a platform that allows you to create and deploy digital assistants for your users. With Oracle Digital Assistant , you can create AI-driven interfaces (or chatbots) for business applications through text, chat, and voice interfaces. Each digital assistant has a collection of one or more specialized skills to help users complete a variety of tasks in natural language conversations. For example, an individual digital assistant might have skills that focus on specific types of tasks such as tracking inventory, submitting time cards, and creating expense reports.

  • OCI AI Agent Platform Oracle Cloud Infrastructure (OCI) AI Agent Platform provides a fully managed, cloud native solution that lets you build, deploy, and manage AI agents. By leveraging state-of-the-art large language models (LLMs), the AI agents you create can revolutionize the way you interact with customers, perform complex tasks autonomously, automate workflows, approach business problems. The service integrates across the Oracle stack, including databases and cloud infrastructure, enabling efficient data retrieval and API interactions.

  • OCI Object Storage OCI Object Storage provides access to large amounts of structured and unstructured data of any content type, including database backups, analytic data, and rich content such as images and videos. You can safely and securely store data directly from applications or from within the cloud platform. You can scale storage without experiencing any degradation in performance or service reliability.

  • Oracle Autonomous Transaction Processing Oracle Autonomous Transaction Processing is a self-driving, self-securing, self-repairing database service that is optimized for transaction processing workloads. You do not need to configure or manage any hardware, or install any software. OCI handles creating, backing up, patching, upgrading, and tuning the database.

  • Oracle AI Database 26ai Oracle AI Database 26ai with AI Vector Search lets you query data by meaning rather than keywords. Vector representations (embeddings) capture the semantics of text, images, audio, and more so you can find similar content efficiently. Built-in SQL distance functions allow similarity searches using vectors. You can combine semantic similarity and other search criteria to ground large language models (RAG) for more accurate and relevant answers.

  • OCI Functions Oracle Cloud Infrastructure Functions is a fully-managed, multitenant, highly scalable, on-demand, Functions-as-a-Service (FaaS) platform. It is powered by the Fn Project open source engine. OCI Functions enables you to deploy your code, and either call it directly or trigger it in response to events. OCI Functions uses Docker containers hosted in Oracle Cloud Infrastructure Registry .

Deploy

To deploy this architecture, follow the instructions in this Live Lab:

Build Agentic AI Solution with Multi-Tool Capabilities powered by OCI AI Agent Framework

Explore More

Learn more about deploying AI agents using Oracle Cloud Infrastructure .

Review these additional resources:

  • Deploy an ODA Chatbot powered by Generative AI Agents (LiveLab)

  • Deploy a Chatbot powered by Generative AI Agents using 23ai Vector DB (LiveLab)

  • OCI AI Agents

  • OCI AI Agents Documentation

  • Overview of Digital Assistants and Skills

  • Oracle Cloud Infrastructure Documentation

  • Well-architected framework for Oracle Cloud Infrastructure

Acknowledgments

  • Authors : Luke Farley, Abhinav Jain

  • Contributor : Kaushik Kundu

Title and Copyright Information

Deploy agentic AI using Oracle Cloud Infrastructure AI Agent Platform

G39281-01

December 2025

Copyright © 2025,

Oracle and/or its affiliates.

Reference in New Issue View Git Blame Copy Permalink