[Feature]: Integrate Vertex AI SDK for observing on cloud

## 🎯 Goal

To enable comprehensive observability for applications deployed on **Google Cloud Vertex AI Agent Engine** by integrating them with **AgentOps** using **OpenTelemetry**. This will allow for detailed tracing, monitoring, and cost analysis of agent performance within the AgentOps platform.

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## 📖 Background

* **AgentOps** ([https://github.com/AgentOps-AI/agentops](https://github.com/AgentOps-AI/agentops)) is an observability platform for AI agents, utilizing OpenTelemetry for tracing and monitoring. It provides an SDK with decorators for easy instrumentation.
* **Vertex AI Agent Engine** ([Cloud Docs](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/reasoning-engine)) is a managed service for deploying and scaling AI agents. It natively supports OpenTelemetry for Google Cloud Trace.
* **Google ADK** ([https://github.com/google/adk-python](https://github.com/google/adk-python)) is a toolkit for building agents, often deployed to Vertex AI Agent Engine.
* **Vertex AI SDK** ([https://github.com/googleapis/python-aiplatform](https://github.com/googleapis/python-aiplatform)) provides the interface for interacting with Vertex AI services, including Agent Engine.

The core idea is to configure agent applications running on Agent Engine to export OpenTelemetry data to *both* Google Cloud Trace (for native Vertex AI observability) and AgentOps (for specialized AI agent observability).

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## 🛠️ Proposed Integration Plan

1. **Agent Application Instrumentation:**
* Develop or modify agent applications (e.g., those built with Google ADK, LangChain, or custom Python code) to incorporate OpenTelemetry.
* Utilize AgentOps decorators (`@session`, `@agent`, `@operation`, etc.) or manual OpenTelemetry span creation for detailed tracing of agent logic, tool calls, and model invocations.
* Adhere to OpenTelemetry semantic conventions for GenAI where applicable.

2. **Configure OpenTelemetry Exporters:**
* Within the agent's Python application code, configure the OpenTelemetry SDK.
* Ensure the **Google Cloud Trace Exporter** is active (often auto-configured in Google Cloud environments). The Agent Engine service account must have permissions to write traces.
* Initialize the **AgentOps SDK** (`agentops.init(api_key="YOUR_AGENTOPS_API_KEY")`). This typically sets up an OTLP exporter pointing to AgentOps' ingestion endpoint.
* If manual OTel configuration is used, explicitly add an OTLP exporter for AgentOps.

3. **Instrument Agent Engine Interactions:**
* Wrap key parts of the agent's logic running within the Agent Engine environment with OpenTelemetry spans.
* For agents built with frameworks like ADK, ensure operations defined within the ADK constructs are instrumented.

4. **Deployment to Vertex AI Agent Engine:**
* Package the instrumented agent application for deployment on Agent Engine (e.g., as a container).
* Include AgentOps SDK and OpenTelemetry libraries as dependencies in the container.
* Securely pass the `AGENTOPS_API_KEY` as an environment variable to the deployed container.

5. **Verification and Monitoring:**
* Invoke the deployed agent.
* Verify that traces appear in both Google Cloud Trace (for Agent Engine infrastructure) and the AgentOps dashboard (for application-level agent behavior and metrics).
* Confirm correlation of trace data where possible.

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## ✅ Key Tasks

* [ ] **Research:** Investigate best practices for dual OpenTelemetry exporter configuration (Google Cloud Trace + OTLP).
* [ ] **Develop Sample Agent:** Create or adapt a simple agent (e.g., using Google ADK) suitable for deployment on Agent Engine.
* [ ] **Implement OpenTelemetry:** Instrument the sample agent using AgentOps decorators and/or manual OpenTelemetry spans.
* [ ] **Configure Dual Export:** Set up the OpenTelemetry SDK in the sample agent to export to both Google Cloud Trace and AgentOps.
* [ ] **Containerize Agent:** Package the instrumented agent into a Docker container.
* [ ] **Deploy to Agent Engine:** Deploy the containerized agent to Vertex AI Agent Engine, ensuring the `AGENTOPS_API_KEY` is configured.
* [ ] **Test & Verify:**
* [ ] Trigger agent execution.
* [ ] Confirm traces are visible in Google Cloud Trace.
* [ ] Confirm traces, events, and metrics (e.g., cost) are visible in the AgentOps dashboard.
* [ ] **Documentation:** Create guidelines and examples for users wanting to integrate their Agent Engine applications with AgentOps.

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## 💡 Considerations & Best Practices

* **Context Propagation:** Ensure W3C Trace Context is correctly propagated across all components and service calls.
* **Sampling Strategy:** Define an appropriate OpenTelemetry sampling strategy to manage trace volume and costs.
* **Custom Attributes:** Encourage the use of custom attributes on spans for richer data in AgentOps (model names, tool usage, token counts, user IDs).
* **Error Reporting:** Ensure exceptions are captured by OpenTelemetry and correctly reported in AgentOps.
* **Security:** Emphasize secure management of the `AGENTOPS_API_KEY` within the Vertex AI environment (e.g., using Secret Manager).
* **Performance Overhead:** Monitor for any performance impact due to instrumentation and optimize if necessary.
* **Framework Compatibility:** Leverage existing AgentOps integrations for frameworks like LangChain if used within the Agent Engine.

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### ✔️ Acceptance Criteria

* Successfully deployed agent on Vertex AI Agent Engine sends telemetry data to AgentOps.
* Key agent operations (e.g., LLM calls, tool usage) are visible as distinct spans/events in the AgentOps dashboard.
* Basic metrics (e.g., latency, token counts, estimated costs) for agent interactions are reported in AgentOps.
* Traces are also visible in Google Cloud Trace for the underlying Agent Engine infrastructure.
* Clear documentation or a working example is available demonstrating the integration.

### 🤔 Related Problem

Doing this in the name of love for @AtomSilverman and @areibman

### 🤝 Contribution

- [ ] Yes, I'd be happy to submit a pull request with these changes.
- [ ] I need some guidance on how to contribute.
- [x] I'd prefer the AgentOps team to handle this update.