Building and Deploying a Highly Available Agentic System with Log-Reading Capabilities
The system prompt of an LLM allows you to define the context of the agent's role. For this project, we selected a basic response structure to streamline the user experience. The full system prompt is in /farseer/backend/routes.py:
- Error Analysis
- Source of Error
- Suggestion to Fix
- Web Server
- System
- Application
- Docker Containers
- Event
xAI 'grok-beta' avg request: ~$0.0483
The app can be structured better to support expanding capabilities for the agent. An example of this is by moving the LLM API calls to a separate module, in case other APIs are introduced.
The app can use a model that performs better against prompt injections.