My team built an agent to triage incoming support tickets. The idea was simple: analyze the ticket, classify it, and route it to the right department or, if simple enough, draft a direct answer. It worked great in dev. Then we shipped it. For weeks, we thought it was humming along, processing tickets efficiently and reducing our human agents’ load. Until a few key accounts started complaining about slow resolutions, or worse, no resolution at all.
The agent wasn’t crashing. It was failing silently. It’d process a complex refund request, decide it was a “general inquiry,” and then just… sit there, waiting for a human that was never assigned. No error logs, no alerts, just a growing pile of unhappy customers. That’s when I really had to figure out how to evaluate AI agents in a way that actually mattered for production, beyond just checking if the code ran.
The Silent Killer: Undetected Failures
This isn’t about an agent crashing. It’s about an agent completing its run, reporting “success,” but failing to achieve the actual business objective. We needed more than just “did it finish?” We needed to know “did it do the right thing?” This is harder than it sounds.
The agent’s internal logic, while seemingly sound on paper, didn’t account for all the nuances of customer intent. It could identify keywords like “refund,” but it couldn’t always connect them to the specific workflow required. Without proper evaluation, these subtle failures compound, leading to significant customer churn and wasted resources.
Consider an agent designed to automate onboarding for new users. If it misses a crucial step, like sending a welcome email with a specific setup guide, the user experience suffers immediately. The agent might report “task complete,” but the user is left confused. You can’t just trust the agent’s self-assessment; you need external validation.
Defining Success: Beyond Token Counts
Our first mistake was focusing too much on token count and latency. Those are table stakes. If your agent is burning through thousands of dollars daily or taking minutes to respond, you’ve got problems, sure. But the real challenge is measuring task effectiveness. For our support agent, “success” had to mean something concrete:
- Correctly classifying the ticket (e.g., refund, technical issue, billing).
- If an answer was drafted, was it accurate, appropriate, and did it sound like a human wrote it?
- If routed, was it sent to the correct team with all necessary context?
- Most critically: did it resolve the customer’s stated problem, or at least move it significantly towards resolution without human intervention?
We realized quickly that these metrics required a blend of automated checks and human review. An agent can tell you it made a tool call; it can’t tell you if that tool call was the *optimal* one for the situation. You need a feedback loop, often a human one, to close that gap. This is where many teams stumble, thinking a purely automated system will self-correct.
We also started tracking secondary metrics like agent “satisfaction” from human reviewers, who would grade the agent’s output on a scale. This provided a qualitative layer to our quantitative data, giving us a more complete picture of performance.
Tools for the Job: Tracing, Scoring, and Custom Hooks
We started with LangSmith. It’s a solid choice for tracing, and if you’re deep in the LangChain ecosystem, it’s a natural fit. You get excellent visibility into agent steps: tool calls, LLM inputs/outputs, the whole chain. But interpreting those traces for *business success* is where the real work begins. It’s not enough to see “tool X was called.” You need to know if tool X *should* have been called, and if its output was used correctly.
Langfuse offers similar tracing capabilities, and I’ve found their UI a bit more intuitive for quick debugging. Their scoring features, where you can programmatically or manually rate traces, became crucial for us. We’d feed in a subset of agent outputs to human reviewers and then use those scores to train an evaluation model (another agent, sometimes) or simply track performance over time. This approach helped us identify patterns in misclassifications and incorrect tool usage.
But for the “did it resolve the customer’s problem?” metric, we had to build custom hooks. We integrated our agent with our CRM, logging the agent’s actions and then tracking the subsequent human interaction. If a human had to immediately re-classify or re-route the ticket, that was a failure. If the customer replied “this didn’t help,” that was a failure. We built simple event listeners that would tag the initial agent trace as a failure.
This meant adding custom metadata to our LangSmith/Langfuse traces. For example, when the agent decided a ticket was a “refund request,” we’d attach that classification as a tag. Later, when a human reviewer disagreed, that trace would get a “misclassified” tag. This allowed us to filter and analyze specific failure modes, helping us pinpoint exactly which part of the agent’s logic needed adjustment. We also used these tags to track agent efficiency over time, seeing if our iterative improvements actually reduced failure rates.
For teams building more bespoke agents, say with AutoGen or CrewAI, similar principles apply. You’ll need to instrument your code to emit these traces and custom events. Even a simple agent built with the Vercel AI SDK needs this level of scrutiny if it’s handling anything critical. If you’re building in an environment like Replit Agent, you have the flexibility to bake in custom logging and monitoring from the ground up, which can be a huge benefit when off-the-shelf solutions don’t quite fit your specific needs.