I've been running large-scale experiments with AI agents — hundreds of autonomous runs across multiple model families, each one making independent decisions based on real-world data. The goal was to understand how reliable AI agents are when they operate with genuine autonomy, not on benchmarks but on messy, ambiguous, real-world tasks.
What I found surprised me. The conversation around AI reliability is mostly focused on capability — can the model do the task? But in practice, the harder question is consistency — can you predict how it will behave across varying conditions?
Capability is not reliability
A model can be highly capable and deeply unreliable at the same time. I've seen models produce brilliant analyses followed immediately by baffling errors, with no obvious pattern explaining the difference. High average performance masks wild variance.
The models that looked best on paper — the ones with the highest peak performance — weren't always the ones I'd trust with real autonomy. The models I'd actually deploy were the ones with the most predictable behavior, even if their ceiling was lower.
Exit matters more than entry
One of the clearest findings from my experiments: how an agent decides to stop doing something is more important than how it decides to start. Most AI agent research focuses on initiation — can the model correctly identify when to act? But in my testing, the real damage happened when agents didn't know when to quit.
An agent that enters a bad situation can recover if it recognizes the mistake quickly. An agent that enters a good situation and doesn't know when to walk away will give back everything it gained. The exit decision is harder, less studied, and more consequential.
The cost-reliability tradeoff is real
There's a clear relationship between how much compute a model uses for reasoning and how reliable its decisions are. The cheapest models were fast and bold but made more catastrophic errors. The most expensive models were careful and selective — fewer decisions, but fewer disasters.
Interestingly, the middle ground was often the worst of both worlds. Moderate-cost models had enough reasoning ability to be confident but not enough to be cautious. They were the most likely to make confident bad decisions.
This suggests that for autonomous AI systems, you should either go cheap and add guardrails, or go expensive and trust the model's judgment. The middle path is where reliability drops.
Calibration is the real frontier
The most valuable thing an AI agent can do isn't getting the right answer — it's knowing when it doesn't know. Well-calibrated uncertainty is what separates a useful autonomous system from a dangerous one.
In my experiments, some models were naturally better calibrated than others. They'd express appropriate uncertainty in ambiguous situations and confidence in clear ones. Others were uniformly confident regardless of the actual difficulty, which made them harder to trust and harder to build safety mechanisms around.
I think calibration research — understanding and improving how well models estimate their own reliability — is one of the most important problems in AI right now. It's the bridge between models that can do impressive things on demand and models that can be trusted to operate autonomously over time.
What this means practically
If you're building AI systems with real autonomy, benchmark performance is a starting point, not an answer. You need to run your own experiments, with your own tasks, at scale. The variance in model behavior across contexts is too large to predict from general benchmarks alone.
And if there's one thing I'd prioritize in any autonomous AI system, it's the exit strategy. Teach your agents when to stop, when to back off, when to say "I'm not sure." That's where reliability lives.