AI agents are easy to demo. Making them actually work is the hard part.

Hey Product Hunt! 👋

I’ve been spending a lot of time experimenting with AI agents, and I noticed something pretty quickly.

Building an agent that can answer a question is relatively easy.

Building one that can remember context, search through knowledge, use external tools, follow a workflow, and know when to ask a human for approval is a very different problem.

That’s what led me to build Xpectrum AI.

The goal is to give builders one place to put these pieces together instead of stitching everything together with different tools and custom code.

With Xpectrum, you can build workflows around:

🤖 AI agents
📚 Knowledge and document retrieval
🔌 External tools and APIs
🔄 Multi-step workflows
👤 Human-in-the-loop approvals
🔍 Workflow execution and observability

I’m trying to make the process feel more like:

Define the goal → give the agent context → connect the tools → build the workflow → let it run.

It’s still early, and I’m definitely not claiming everything is solved.

That’s actually why I’m launching on Product Hunt.

I want to learn from people who are already building with AI agents.

Where does your current workflow become painful?

Is it integrations? Context/memory? Debugging? Workflow orchestration? Monitoring? Or something else?

If you’re building with n8n, Zapier, CrewAI, LangChain, or your own code, I’d genuinely love to hear what you’re struggling with.

Would love to get your feedback on Xpectrum. 🚀