I've lost count of how many times I've seen an AI agent demo that looked amazing, only to try it myself and watch it fall apart at the first unexpected scenario.
Building agents that work in demos is easy. Building agents that work reliably in production? That's a completely different challenge.
After months of building Fantomu, we've learned what actually works—and what doesn't. Here's our honest take.
The Reality Check
Most AI agents fail for predictable reasons:
- They have no error handling, so any failure breaks everything
- They can't manage context, so they forget what they're doing
- They can't make real decisions, so they just follow scripts
- They have no observability, so when they break, you have no idea why
We made all these mistakes. Our first agents worked perfectly in testing, then failed spectacularly in production. It was humbling, but it taught us what actually matters.
What Actually Works
1. Structured Outputs Are Non-Negotiable
I cannot stress this enough: never parse natural language for critical decisions.
I've seen systems that ask an LLM "Should I retry?" and try to parse responses like "Yes, I think that would be a good idea, maybe wait a bit first though."
This is a recipe for disaster. Use structured outputs with defined schemas. Always. No exceptions.
It's more reliable, easier to debug, and prevents the weird edge cases that will haunt you at 3 AM.
2. Error Recovery Is Not Optional
Things will fail. APIs will go down. Networks will timeout. Data will be malformed. This is not a question of "if"—it's a question of "when."
Build retry logic with exponential backoff. Implement fallback strategies. Design for graceful degradation.
An agent that fails once and gives up isn't an agent—it's a script with better marketing.
3. Define Clear Boundaries
Agents need to know their limits. They need clear rules for:
- When to act autonomously
- When to ask for human clarification
- When to abort and report failure
Don't let agents try to do things they can't do. Set confidence thresholds. Define maximum retry counts. Create clear escalation paths.
An agent that tries to do everything will fail at everything.
4. Observability from Day One
You cannot debug what you cannot see. Log every decision. Log every action. Log every failure. Log the reasoning behind each choice.
Build dashboards. Create alerts. Make it trivial to understand what the agent is doing and why.
When something breaks at 2 AM (and it will), you'll thank yourself for building observability from the start.
What Doesn't Work (And Why We Tried It Anyway)
Here's what we tried that failed spectacularly:
Parsing Natural Language for Decisions
We thought we could ask an LLM "What should I do?" and parse the response. We were wrong. The responses were inconsistent, ambiguous, and impossible to reliably parse.
Structured outputs solved this completely.
Linear Workflows Without Decision Points
We built agents that just executed steps in order. They worked great until something unexpected happened, then they broke completely.
Real agents need decision points where they can evaluate and adapt.
No Error Handling
We assumed things would work. They didn't. Everything broke. We learned to assume everything will fail and plan accordingly.
Building Everything at Once
We tried to build the perfect agent from day one. It was too complex to debug, too fragile to maintain, and too confusing to understand.
Incremental development is the only way.
Our Current Approach
We've learned to build agents incrementally:
- Start with the simplest possible version that works
- Add complexity only when you need it
- Use structured outputs everywhere
- Build observability from the start
- Always have a plan for when things go wrong
It's not perfect. But it's getting better. And that's the goal—continuous improvement, not perfection.
Building reliable agents is hard. But with the right approach, it's possible. And when it works, it's worth it.