by user | Jul 16, 2026 | Agent Evaluation & Observability
Your AI agent looks ready because the demo was clean. It answered the sample questions, called the right tool, and produced polished output while everyone nodded. Then a real customer arrives with missing context, stale CRM data, and a request that does not fit the...
by user | May 21, 2026 | Agent Evaluation & Observability
You ship a CRM “auto-update” agent into a pilot. On day three, a sales rep messages you: “Why did my top account get downgraded?” You check the logs and realize the agent wasn’t wrong in a simple way. It was confidently wrong in a way that looked plausible, and it...
by user | May 7, 2026 | Agent Evaluation & Observability
You launch an AI agent that looked flawless in the demo. Two weeks later, Sales complains it “makes things up,” Support says it’s slow and pricey, and your ops lead quietly turns it off after one too many escalations. Sound familiar? That’s not a “bad model” problem....
by user | Apr 11, 2026 | Agent Evaluation & Observability
You ship a new AI support agent on Friday. By Monday, containment is up, your backlog looks better, and the dashboard is throwing confetti. Then a customer posts a screenshot: the agent refused to escalate a billing dispute, made up a policy, and sounded weirdly...
by user | Mar 12, 2026 | Agent Evaluation & Observability
Why “it worked in staging” fails at 2:13 a.m. Your support agent is live. It has access to a knowledge base, a ticketing tool, and maybe even refund workflows. Then, at 2:13 a.m., it confidently tells a customer the wrong policy, or it calls the right tool with the...
by user | Mar 9, 2026 | Agent Evaluation & Observability
Why “it worked in staging” is a trap You ship an agent on Friday. By Monday, support drops a screenshot: a confident answer that’s subtly wrong. Meanwhile, compute spend climbed, and nobody can reproduce the exact run that caused the mess. That moment is when...
