When your team ships an AI product, do you ever wonder
“How do we actually know if it’s working as intended — or silently failing?"
Are they are any better ways to trace user logs and identify failures quickly and reliably?
“Before an update or launch, how confident are we about our products’ performance?"
Are there any systematic ways to test and evaluate to make AI Agents that performance reliably?
"AI agents are becoming more complex — how do we monitor and evaluate performance across so many moving parts?"
Are there any ways to confidently measure performance when agents involve so many steps, tools, and scenarios?
Still, there are Gaps
Native approaches and current tools leave critical problems unsolved when it comes to shipping AI Agents.
Monitoring is manual, slow, and error-prone
When running AI agents, monitoring usually means manually checking logs line by line. It takes a lot of time and costs a lot of money, since humans need to make decisions at every step. On top of that, failures are often misclassified as successes — leading to human errors that silently slip through.
Testing before launch is unreliable and expensive
Before an update or launch, testing is still done in ad-hoc ways. Teams either trust gut feeling after quick internal checks, or hire external testers to use the agent manually. The result: low accuracy, high costs, and a process that feels more like guessing than systematic validation.
Current tools only scratch the surface
Yes, there are tools that make it easier to see input-output logs and tell whether something succeeded or failed. But here’s the issue: AI agents are becoming increasingly complex.
When an agent fails, these tools don’t show at which step the failure occurred. Teams still need to dig into raw logs manually to trace back the root cause — a process that becomes harder as agents grow larger and more complex.
Evaluation or testing features in tools remain shallow and limited, offering little help in deeply understanding multi-step agent performance.
