Agentic AI Scaling Examples

This repository offers practical examples for scaling and hardening agentic AI workflows, focusing on real-world applications and production-readiness.

Included Examples

• Caching Layer (caching_layer.py)
  Implements Redis caching for repeated LLM queries to reduce latency and API costs.

• Async Batching (batch_api_caller.py)
  Demonstrates asynchronous batching of API calls for efficient agent operations.

• Load Testing (load_test_scenarios/locustfile.py)
  Example load test scenario using Locust to simulate agent activity under peak conditions.

• Monitoring Dashboard (monitoring_dashboard.json)
  Starter Grafana dashboard configuration for observing latency, error rates, and usage metrics.

Getting Started

1. Clone the repository:

   git clone https://github.com/<your-username>/agentic-ai-scaling-examples.git
   cd agentic-ai-scaling-examples
   

2. Install dependencies:

• Python packages: redis, aiohttp, locust

• Ensure Redis server is running if you want to test caching.

• Grafana for importing the monitoring dashboard JSON.

3. Review and run the scripts:

• (caching_layer.py): Use as a reference for caching repeated LLM calls.

• (batch_api_caller.py): Run async batches of API requests.

• (load_test_scenarios/locustfile.py): Launch Locust to simulate load.

• (monitoring_dashboard.json): Import into Grafana to visualize metrics.

Contributing

Feel free to fork this repository and add examples for other agentic AI scaling patterns, monitoring tools, or deployment scripts.

License

This project is licensed under the MIT License. See the LICENSE file for details.