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‎ai-data-strategy/enablement-assets/README.md‎

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# Enablement Asssets
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AI and Data Strategy enablement presentations

‎ai/gen-ai-agents/custom-rag-agent/start_ui_mcp.sh‎

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streamlit run ui_mcp_agent.py
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‎ai/gen-ai-agents/mcp-oci-integration/README.md‎

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* **Integrate** MCP Servers with OCI **APM** for **Observability**
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* **how-to** create a **docker** image for your MCP server
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**Author**: L. Saetta
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**Reviewed**: 15.10.2025
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![MCP console](./images/mcp_cli.png)
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## What is MCP?
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Access to Oracle 23AI Vector Search is through the **new** [langchain-oci integration library](https://github.com/oracle/langchain-oracle)
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## Adding security
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## Adding Security
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If you want to put your **MCP** server in production, you need to add security, at several levels.
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Just to mention few important points:
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For **Select AI** configuration, see [here](./configure_select_ai.md)
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## OCI Consumption Analysis
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Another use case demonstrated in this set of demos is leveraging an AI Assistant powered by MCP servers to analyze the **OCI tenant consumption** in a natural and interactive way.
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Using the [MCP Consumption Server](./mcp_consumption.py), you can explore various dimensions of consumption and ask questions such as:
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* List the top 10 services by total amount for a given period (start_date, end_date).
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* List the top 10 compartments by total consumption.
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* For a specific service (or list of services), show the consumption breakdown across the top 5 compartments.
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The key advantage of this approach is that you don’t need to export or replicate data into a Data Warehouse (DWH) — all information is retrieved directly from the OCI Usage API in real time.
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How to Use:
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* Configure your OCI credentials.
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* Start the MCP Consumption Server
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* Launch the AI Assistant
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* Point the Assistant to the MCP URL (or to your MCP Aggregator).
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### Security (Optional)
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You can enhance security by placing the MCP server behind an OCI API Gateway and enabling JWT-based authentication using OCI IAM.
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‎ai/gen-ai-agents/mcp-oci-integration/start_aggregator.sh‎

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python mcp_aggregator.py
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‎ai/gen-ai-agents/mcp-oci-integration/start_mcp_consumption.sh‎

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python mcp_consumption.py --port 9500
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‎ai/gen-ai-agents/mcp-oci-integration/start_mcp_explorer.sh‎

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streamlit run mcp_explorer.py
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‎ai/gen-ai-agents/mcp-oci-integration/start_mcp_internet_search.sh‎

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python mcp_internet_search.py --port 8500
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‎ai/gen-ai-agents/open-agent-spec/LICENSE‎

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MIT License
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Copyright (c) 2025 Luigi Saetta
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎ai/gen-ai-agents/open-agent-spec/README.md‎

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# 🧩 Open Agent Specification — Overview
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A **unified declarative standard for AI agents**, designed to bring interoperability across frameworks such as **LangGraph**, **AutoGen**, and **Oracle Agent Runtime**.
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> From fragmented agent frameworks to interoperable agentic systems
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> 📄 Source: [arXiv 2510.04173 (October 2025)](https://arxiv.org/abs/2510.04173)
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---
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## 🎯 Design Objectives
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| Objective | Description |
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|------------|--------------|
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| **Portability & Interoperability** | Move agents seamlessly between frameworks (LangGraph, AutoGen, OCI Agent Runtime). |
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| **Declarative Definition** | Define agents in YAML/JSON instead of hardcoded logic. |
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| **Modularity & Composability** | Reuse flows, tools, and sub-agents. |
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| **Explicit Control & Data Flow** | Clearly define how steps connect, branch, or loop. |
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| **Validation & Conformance** | Built-in schema validation ensures compatibility. |
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| **Multi-Agent Composition** | Enable collaboration and orchestration among agents. |
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---
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## 🧠 Core Concepts and Components
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| Concept | Explanation |
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|----------|--------------|
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| **Agent** | The reasoning or conversational entity. |
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| **Flow** | Structured workflow defining execution steps (nodes, branches, loops). |
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| **Tool** | API, function, or service the agent can call. |
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| **Memory / Prompt Templates** | Mechanisms for contextual state and conversation history. |
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| **Edges** | Define relationships and data flow between nodes. |
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These building blocks form the **agent graph**, which can be executed on compatible runtimes.
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---
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## ⚙️ Serialization, SDKs, and Runtime Adapters
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### Serialization Layer
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- Uses **YAML/JSON schemas** for transparent, portable definitions.
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- Supports versioning, validation, and interchange.
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### Python SDK — `PyAgentSpec`
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- Reference SDK for building, validating, and exporting agents.
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- Provides schema validation, object composition, and serialization.
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### Runtime Adapters
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Bridge the specification to concrete frameworks:
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- **OCI Agent Runtime**
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- **LangGraph**
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- **AutoGen**
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Adapters support **import/export** interoperability:
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---
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## 🔄 Control Flow & Data Flow Semantics
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- **Directed edges** define execution order.
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- **Branching and loops** for dynamic logic.
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- **Inputs/outputs** explicitly mapped between steps.
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- **Nested flows** and **sub-agents** enable modular reuse.
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This model ensures predictability, traceability, and easy debugging across runtimes.
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---
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## 💡 Benefits & Value Proposition
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| Stakeholder | Benefits |
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|--------------|-----------|
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| **Developers** | Portability, validation, and reuse of components. |
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| **Framework Vendors** | A standardized interchange format. |
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| **Researchers** | Reproducibility and comparability across experiments. |
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| **Enterprises** | Governance, modularity, and reduced vendor lock-in. |
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> **In essence:** “Write once, run anywhere” for AI agents.
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---
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## ⚠️ Limitations & Challenges
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| Challenge | Description |
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|------------|--------------|
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| **Early-Stage Adoption** | Specification is still experimental. |
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| **Runtime Mismatch** | Execution semantics differ between frameworks. |
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| **Performance Overhead** | Translation layer introduces minimal latency. |
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| **Safety & Observability** | Delegated to runtime implementations. |
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---
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## 🗺️ Roadmap & Future Directions
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Planned enhancements include:
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- **Memory, Planning, and Datastore** extensions.
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- **Agent-to-Agent (A2A)** communication protocols.
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- SDKs for more languages (Java, TypeScript, Go).
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- **Conformance tests** and **visual editors**.
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- Community-driven **registry of agents**.
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---
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## 🔍 Critique & Strategic Considerations
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### Strengths
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- Framework-agnostic and modular.
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- Promotes ecosystem collaboration.
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- Declarative, composable design.
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### Risks
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- Slow adoption curve.
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- Runtime complexity.
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- Divergent adapter implementations.
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### Recommendations
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- Start small and modular.
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- Contribute runtime adapters early.
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- Prioritize **observability** and **safety instrumentation**.
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---
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## 🧾 Summary & References
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The **Open Agent Specification** defines a **declarative, interoperable schema** for building modular AI agents across multiple runtimes and ecosystems.
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| Resource | Link |
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|-----------|------|
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| 📄 Paper | [arXiv 2510.04173](https://arxiv.org/abs/2510.04173) |
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| 💻 GitHub | [https://github.com/oracle/agent-spec](https://github.com/oracle/agent-spec) |
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| 📘 Docs | [https://oracle.github.io/agent-spec/index.html](https://oracle.github.io/agent-spec/index.html) |
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| 📰 Blog | [Oracle AI & Data Science Blog](https://blogs.oracle.com/ai-and-datascience/post/introducing-open-agent-specification) |
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---
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### ✅ Summary Statement
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> **Open Agent Specification** is a key step toward **standardizing AI agent design**, enabling transparent, portable, and interoperable agent systems across enterprise and open-source ecosystems.
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---
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