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cloud.google.com·7h ago
System Design
ByteByteGo·Jul 22·5 min read
Best Practices for Building AI Agents That Work in Production
TL;DR
Engineering teams are enhancing AI agents' effectiveness in production by implementing context engines, addressing the limitations of current context maturity levels.
✦ Why It Matters
Evaluate your team's context maturity today to enhance AI agent performance in production.
Key Takeaways
- Control the context the model sees for better accuracy.
- Use deterministic code for predictable tasks.
- Keep the model stateless and manage state externally.
- Design narrow agents to reduce failure surfaces.
How It Works
An AI agent operates as a loop where the model receives context, makes a decision, and returns structured output. This design allows for less branching logic, but in production, it requires careful management of context and control flow to prevent compounding errors and ensure reliability.