Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
System Design
Martin Fowler·Jun 16·5 min read
Building Reliable Agentic AI Systems
TL;DR
Many AI systems struggle with reliability and safety, leading to unpredictable behavior. A framework called 'Agentic AI' was developed to enhance the reliability of AI agents by incorporating robust decision-making processes.
✦ Why It Matters
Engineers can implement the Agentic AI framework to improve the reliability and safety of their AI systems.
Key Takeaways
- Leverage Retrieval-Augmented Generation for complex data queries.
- Implement context engineering to improve information processing.
- Use multi-agent systems for orchestrating workflows effectively.
- Focus on resilience and error handling in AI architectures.
How It Works
PRINCE utilizes a multi-agent architecture orchestrated by LangGraph, which manages workflows through a series of specialized agents. Each agent is responsible for distinct tasks, such as clarifying user intent, conducting research, and generating responses.
This structured approach allows the system to handle complex queries effectively, ensuring that the right tools and data sources are utilized at each step.