Building an AI agent that works in a demo is pretty exciting.
But getting that same agent to work reliably with real users? That's where things get interesting.
I've been exploring AI agents, RAG, and Python automation, and there are a few challenges I keep coming back to:
Tool calling: What happens when an agent picks the wrong tool or sends invalid arguments?
Memory: How do you keep useful context without filling the prompt with unnecessary history?
RAG: How do you handle outdated documents, poor retrieval, and conflicting information?
Reliability: What's your strategy when an LLM produces inconsistent results?
Cost: How do you manage token usage when multiple agents are working together?
One approach I find useful is treating every tool call like an external API request: validate inputs, enforce permissions, set timeouts, and log failures.
But I'm curious how other developers approach this.
If you've worked with LangGraph, LangChain, CrewAI, or your own agent framework, what's been your biggest technical headache?
And if you're just getting started, which part seems the most challenging?
Would love to hear different approaches, especially solutions that have worked in real projects.
Always interesting to see how other engineers solve the same problems. 🚀
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