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Cloud & Infrastructure
The New Stack·17d ago·5 min read
The bottleneck for AI agents isn’t the model anymore. It’s the context layer.
TL;DR
AI agents are facing reliability issues not due to the models themselves, but because of inadequate context layers. By focusing on building better infrastructure, such as structured knowledge bases and tools, teams can enhance agent performance.
✦ Why It Matters
Engineers should prioritize developing robust context layers and knowledge infrastructures to enhance AI agent reliability today.
Key Takeaways
- Build a structured context graph for your organization's knowledge.
- Implement a compilation step to transform raw data into usable formats.
- Ensure clear retrieval mechanisms for tool selection to improve accuracy.
- Establish execution boundaries to prevent unintended actions by agents.
How It Works
The context layer is crucial for AI agents, as it compiles raw data into structured formats that agents can query effectively. This involves creating a context graph that reflects the organization's knowledge and integrating tools with clear retrieval mechanisms.
By focusing on this infrastructure, teams can enhance the reasoning capabilities of their agents and ensure reliable performance.