A agent implementing a base ReAct agent using LangGraph

Agent generated with googleCloudPlatform/agent-starter-pack

Prod Monitoring Assistant is an intelligent agent that helps monitor production environments, analyze logs, and detect issues in real time. It provides actionable insights and assists developers with debugging through an interactive Slack bot and API interface.

✅ Automated Issue Detection – Analyzes logs, traces, and recent code changes to pinpoint root causes.
✅ Slack Bot Integration – Developers can interact with the agent via Slack for real-time troubleshooting.
✅ Production Monitoring – Monitors multiple production environments 24/7, ensuring continuous uptime.
✅ FastAPI Backend – Serves as the core interface for communication between the agent, the frontend, and external integrations.
✅ Cloud-Native Deployment – Uses Terraform for infrastructure as code and Google Cloud services for scalability.

1. Monitor Production Logs – The agent continuously analyzes logs and traces to detect anomalies.
2. Identify Issues – Using AI, it identifies potential root causes, such as failed API calls, incorrect schema validation, or resource exhaustion.
3. Explain & Suggest Fixes – The agent provides detailed explanations of detected errors and recommends debugging steps.
4. Interact via Slack – Developers can chat with the agent in Slack to request insights and troubleshooting steps.
5. Improve Over Time – The system continuously learns from past issues to provide better recommendations.

This project is organized as follows:

prod-monitoring-assistant/
├── app/                 # Core application code
│   ├── agent.py         # Main agent logic
│   ├── server.py        # FastAPI Backend server
│   └── utils/           # Utility functions and helpers
├── deployment/          # Infrastructure and deployment scripts
├── notebooks/           # Jupyter notebooks for prototyping and evaluation
├── tests/               # Unit, integration, and load tests
├── Makefile             # Makefile for common commands
└── pyproject.toml       # Project dependencies and configuration

Before you begin, ensure you have:

• uv: Python package manager - Install
• Google Cloud SDK: For GCP services - Install
• Terraform: For infrastructure deployment - Install
• make: Build automation tool - Install (pre-installed on most Unix-based systems)

Install required packages using uv:

make install

If not done during the initialization, set your default Google Cloud project and Location:

export PROJECT_ID="qwiklabs-gcp-03-98e7977f8112"
export LOCATION="us-central1"
gcloud config set project $PROJECT_ID
gcloud auth application-default login
gcloud auth application-default set-quota-project $PROJECT_ID

For full command options and usage, refer to the Makefile.

1. Prototype: Build your Generative AI Agent using the intro notebooks in notebooks/ for guidance. Use Vertex AI Evaluation to assess performance.
2. Integrate: Import your chain into the app by editing app/agent.py.
3. Test: Explore your chain's functionality using the Streamlit playground with make playground. The playground offers features like chat history, user feedback, and various input types, and automatically reloads your agent on code changes.
4. Deploy: Configure and trigger the CI/CD pipelines, editing tests if needed. See the deployment section for details.
5. Monitor: Track performance and gather insights using Cloud Logging, Tracing, and the Looker Studio dashboard to iterate on your application.

The repository includes a Terraform configuration for the setup of the Dev Google Cloud project. See deployment/README.md for instructions.

The repository includes a Terraform configuration for the setup of a production Google Cloud project. Refer to deployment/README.md for detailed instructions on how to deploy the infrastructure and application.

You can use this Looker Studio dashboard template for visualizing events being logged in BigQuery. See the "Setup Instructions" tab to getting started.

The application uses OpenTelemetry for comprehensive observability with all events being sent to Google Cloud Trace and Logging for monitoring and to BigQuery for long term storage.