Introduction
AI agents offer a powerful combination of technologies, able to complete tasks with the use of generative AI. However, in some situations, the task required might be larger than is realistic for a single agent. For those scenarios, consider a multi-agent solution. A multi-agent solution allows agents to collaborate within the same conversation.
Imagine you're trying to address common DevOps challenges such as monitoring application performance, identifying issues, and deploying fixes. A multi-agent system could consist of four specialized agents working collaboratively:
The Monitoring Agent continuously ingests logs and metrics, detects anomalies using natural language processing (NLP), and triggers alerts when issues arise.
The Root Cause Analysis Agent then correlates these anomalies with recent system changes, using machine learning models or predefined rules to pinpoint the root cause of the problem.
Once the root cause is identified, the Automated Deployment Agent takes over to implement fixes or roll back problematic changes by interacting with CI/CD pipelines and executing deployment scripts.
Finally, the Reporting Agent generates detailed reports summarizing the anomalies, root causes, and resolutions, and notifies stakeholders via email or other communication channels.
This modular, scalable, and intelligent multi-agent system streamlines the DevOps process. The agents collaborate to reduce manual intervention and improve efficiency while ensuring timely communication and resolution of issues.
In this module, you'll explore how to use the powerful capabilities of Semantic Kernel to design and orchestrate intelligent agents that work collaboratively to solve complex problems. You'll also learn how to use the Semantic Kernel Agents Framework to develop your own AI agents that can collaborate for a multi-agent solution.
After completing this module, you'll be able to:
- Build AI agents using the Semantic Kernel SDK
- Use tools and plugins with your AI agents
- Develop multi-agent solutions