There is no single “best” tech stack for AI agents in production, but the most successful teams tend to focus on reliability, observability, and scalability rather than just model performance.

A common production architecture includes:
• Foundation models such as GPT, Claude, Gemini, or open-source LLMs.
• Agent frameworks like LangGraph, CrewAI, AutoGen, or custom orchestration layers.
• Vector databases for retrieval and memory.
• Traditional databases for transactional data and state management.
• Monitoring and observability tools to track agent behavior, costs, latency, and failures.
• Containerized deployment using Kubernetes, cloud services, or serverless infrastructure.

One trend I’ve noticed is that as systems move from prototypes to production, teams often reduce agent autonomy and increase workflow control. Purely autonomous agents can be unpredictable, while structured workflows with clear guardrails tend to deliver better reliability and business outcomes.

For teams already running AI agents in production: What stack are you using, and what has been your biggest challenge—scalability, cost, latency, observability, or reliability?