What's the most valuable lesson you learned from a failed AI agent project?
Hello, Product Hunt community!
We often celebrate the wins and the "hockey stick growth" moments here, which is incredibly inspiring. However, I believe there's a treasure trove of knowledge in the projects that didn't work out, especially in the rapidly evolving world of AI agents.
I'm currently in the trenches building an AI agent, and while the potential is exciting, the path is riddled with unexpected challenges. I'm sure many of you have been here before.
I'm not looking for success stories today. Instead, I want to create a space for us to share the nitty-gritty of our "failed" attempts at building AI agents. Think of this as a 'post-mortem' for the projects that taught you the most.
To get the ball rolling, I can share a brief (and slightly painful) experience: We spent weeks developing a sophisticated agent designed to automate a complex data entry task. We were so focused on the core AI logic that we completely underestimated the variability in the input data formats in the real world. The agent worked perfectly in our controlled environment but was so brittle in production that it was practically useless.
The big lesson?
Robust data preprocessing is just as, if not more, important than the AI model itself.
I'm hoping to learn from your experiences to avoid similar pitfalls. Specifically, I'd love to hear about:
Misjudged User Needs: Did you build an agent that solved a problem nobody actually had?
Technical Black Holes: What unforeseen technical hurdles completely derailed your project? (e.g., unexpected API limitations, model hallucinations, spiraling costs)
The "Last 10% Hell": Was there a feature that seemed simple but turned out to be incredibly complex to implement reliably?
Onboarding & Trust: How did you try to get users to trust and adopt your agent, and where did you go wrong?
Let's learn from each other's mistakes and build better, more resilient AI agents together. What's your story?
Replies
Hey Hansel! Really appreciate this post, it’s rare to see people openly sharing what hasn’t worked in the AI agent space. Totally agree that these stories are often where the real learning happens.
I’m working with a small research team on a study exploring how people are using or building agentic AI tools in everyday life (outside of work). Your experience really struck a chord with some of the themes we’re digging into.
No pressure at all, but if you’d be open to it it'd be great to hear more about your experiences in a brief chat (we'd of course compensate you for this). lmk if you'd be interested. Either way, thanks for starting such a valuable conversation!
@julian_gopffarth Hey Julian, thank you so much for the thoughtful comment and the generous offer! I really appreciate it. Your research into how people use agentic AI in daily life sounds fascinating and incredibly important for the whole community.
While I'd love to contribute, we're in a deep, heads-down development phase right now, so I can't dive into specifics at the moment.
However, one key learning I can share—which expands on my original post—is that building user trust with a personal AI agent is less about technical perfection and more about designing the interaction to feel predictable and emotionally safe. The real challenge isn't just getting the AI to be "correct," but getting the user to feel understood and secure, especially with nuanced input.
I'd be very interested to read your team's findings when they're ready. Thanks again for starting this conversation and for the work you're doing!
This hit home, Hansel.
One of our early AI agent attempts was for auto-tagging support tickets. We assumed NLP + semantic matching would solve everything. But here’s what went wrong:
Misjudged User Needs: Users didn’t want full automation. They wanted “confidence suggestions” not hard tags. We overpromised autonomy, but what they really valued was assistive AI that worked with human context.
Last 10% Hell: Handling edge cases like multilingual tickets or sarcasm completely broke the pipeline. We ended up spending 40% of dev time just patching for exceptions all of which were invisible in our training set.
Onboarding & Trust: We shipped without building gradual trust. Users didn’t feel in control, and one mis-tag led to rejection of the whole system.
Biggest takeaway?: Don’t assume automation is always the goal. Sometimes augmentation wins. And give users override + feedback loops from Day 1.
Curious what kind of agents you’re building now and how you're designing for trust this time around?
@priyanka_gosai1 this is an incredible share—thank you. You've perfectly articulated the classic "automation vs. augmentation" dilemma. Your point about users wanting 'confidence suggestions' not 'hard tags' is so sharp and resonates deeply.
You asked what I'm building now and how I'm designing for trust. Your experience is precisely why my approach is completely different this time.
Building Gradual Trust: You're spot on. We're designing for this by starting with an extremely 'low-risk' task for the user. The initial interaction with the agent happens in a completely private, contained space. There are no external consequences. It's a safe sandbox to interact with the AI before the user is ever asked to trust it with a higher-stakes action.
Explaining the AI (for Trust): Instead of a "black box" that promises to solve everything, we're building a "structured sounding board." The goal of the AI isn't to provide the 'right answer,' but to help the user process their own thoughts and find clarity. This means we prioritize model predictability and data privacy over unconstrained creativity. The user needs to feel they are in complete control, so we're making the AI's role as a facilitator very explicit from the start.
My biggest takeaway, much like yours, is that for personal and professional challenges, the goal isn't automation, it's augmentation. The agent I'm building is designed to help people navigate emotionally complex situations, providing a safe space for thought before they ever have to communicate with another human.
Thanks again for sharing your experience—it’s stories like this that help us all build better products.