Beyond just build frameworks: My top 5 tools for managing AI agents at scale

Building an agent is a weekend project. Operating twenty across a team without breaking things is a completely different story. Most roundups conflate build tools with everything that comes after, so here is a breakdown of 5 tools across the stack that actually solve production pain: LangGraph: Still the strongest option when you need deterministic, graph-based control over complex multi-agent steps. Port.io: Solves the agent sprawl problem. It gives us a centralized registry where every agent is logged with an owner, environment scope, and approved permissions so infosec doesn't panic. PydanticAI: Great if you hate bloated abstractions and just want typed, predictable outputs that integrate cleanly into existing microservices. Langfuse: Essential for debugging token costs, prompt drift, and failed tool calls when agents act up in background jobs. Relevance AI / n8n: Excellent for low-code/internal ops workflows where non-engineering teams need to configure decision steps without touching the main codebase. What's in your core agent stack right now?