With kids around, there’s no shortage of entertainment, stress triggers, and analogies that map surprisingly well to work issues. So here's another one.
I asked my four-year-old to brush her teeth, a simple request that, in my mind, had a simple, linear path to completion. Instead, I got a full-blown meltdown. This wasn't just a toddler being difficult; this was a level-ten emotional eruption that had zero to do with dental hygiene. After the storm passed and with a bit of calm, persistent discovery, I found the root cause. Her reaction was a "memory" of a past instruction she didn’t agree with—some perceived injustice from a week ago—influencing her behavior this time around.
If you’ve had kids, you know this isn't a unique phenomenon. Their "reasoning" is often rooted in a deep, complicated history you have to uncover. The only way to get ahead of it is to have enough context to follow their train of thought, understand their memories, and set clear boundaries for the future (or at least attempt to).
This frustratingly human experience is a perfect parallel to a uniquely modern challenge: taking a seemingly-perfect, vibecoded AI agent from a demo environment to production. In a controlled demo, it works flawlessly. But when real users get their hands on it, things fall apart fast. Their queries don't follow the precise paths you've crafted. They have "memories" from previous interactions—state, context, and data from a conversation that went sideways—that can cause the system to have a full-blown meltdown over a simple request.
This is where Evals (Evaluation Systems) come in. They are your key to understanding why your agent is having a tantrum, and the key to building a robust, predictable system. Just as you need to understand your child's past context to predict their future behavior, you need a robust evals system to understand your agent's behavior at scale.
Here’s what a robust evals system for an enterprise-grade agent looks like in toddler speak:
Data Logging and Tracing: This is your way of following the breadcrumbs. Think of it as a detailed diary of every user interaction. For every conversation, your system logs the user's query, the agent's internal thought process (what tools it called, what data it accessed), and its final response. This trace becomes your map to understanding exactly why a decision was made. You're no longer guessing; you're seeing the full conversational history that led to the "outburst."
Unit Tests for Bottlenecks: This allows you to test specific, critical functions. If your agent uses a tool to summarize a document, a unit test checks that function in isolation. Does it work with different document types? What about a 500-page PDF versus a two-line text file? This helps you identify and fix specific weaknesses without having to run a full-scale test every time.
End-to-End Path Tracking: This is about testing the entire journey. You can replay a user's full conversation path through your agent, from the initial query to the final output. This allows you to catch issues that only surface during a complex, multi-step conversation. Did the agent forget a key piece of information it accessed three steps ago? End-to-end tracking reveals those "memory" lapses.
And a final, critical piece of the puzzle: benchmarking new models. With new large language models (LLMs) launching constantly, it's tempting to swap out your current LLM for a "better" one just because it topped a public leaderboard. But what a leaderboard calls "better" may not be what your customers consider "better." A strong evals framework ensures you can scientifically track how any change—a new LLM, a different tool, a new prompt—affects the actual operation. You can run hundreds, even millions, of test calls on your agent and get quantifiable data on how the new change performs across the board.
The ability to move fast and break things is a tech cliché, but in the world of agentic systems, a broken system can cost you customers, not just time. A robust evals framework doesn't slow you down; it allows you to iterate faster and more safely. It's the parenting guide for your AI, giving you the context you need to turn frustrating meltdowns into predictable, well-behaved outcomes.
And, as with everything—start simple and build complexity as you understand more.