Key Considerations When Developing Agents
Essential considerations for building robust LLM agents in production, covering branching logic design, database integration, and observability implementation.
When building LLM agents in production environments, there are many easily overlooked elements beyond the obvious ones - from designing branching logic and database integration to implementing observability. Based on recent project experience, this post compiles practical tips that proved genuinely helpful: developing branching nodes with DSPy, streamlining DB integration using MCP Toolbox for Databases, and building observability around Langfuse. Each section focuses on immediately applicable methods, so I hope this serves as a useful reference for elevating both your team’s agent quality and deployment velocity.
