Building Observable, Secure, and Resilient AI Agents with Oracle MCP, OpenTelemetry, and LangGraph
This article introduces TalentScout AI, a reference implementation of an observable, secure, and resilient agent system. It discusses the importance of observability for AI agents and the architecture of the system.
Why it matters
As AI systems become more complex and agentic, observability is crucial for safely operating and debugging them. This article provides a production-oriented reference implementation for building observable, secure, and resilient AI agents.
Key Points
- 1Observability is critical for agentic AI systems to understand failures and debug issues
- 2TalentScout AI is built as a graph of agents, not a monolith, to make failures visible and traceable
- 3The system uses Oracle MCP for secure database access, LangGraph for agent architecture, and OpenTelemetry for observability
Details
The article explains that modern AI systems are no longer single-prompt chatbots, but rather 'agents' that plan, call tools, query databases, and make decisions over multiple steps. Without observability, these complex agents can experience failures that are difficult to diagnose. The article introduces TalentScout AI, a reference implementation that aims to be observable, secure, and resilient. The system is built as a graph of agents, each with a single responsibility, to make failures more visible and traceable. It uses Oracle MCP for secure database access, LangGraph for agent architecture, and OpenTelemetry for observability. The article provides a detailed walkthrough of the local development setup, including installing Docker, Oracle AI database, SQLcl, and Ollama, as well as setting up the Python environment and implementing the agents.
No comments yet
Be the first to comment