AgentScore

Let's be real: the AI agent gold rush has a dirty little secret. Everyone's shipping agents that can "do things," but almost no one can tell you if those agents are *improving* over time. AgentScore tackles that head-on with a daily score—a pulse check for your agent's capabilities. That's the kind of boring-but-critical tooling that separates demos from durable products. We're in the era of agentic everything, from coding copilots to autonomous support bots. But evaluation is still stuck in the Stone Age: manual QA, anecdotal "it felt better today," or just crossing your fingers after a prompt tweak. AgentScore flips that by turning agent quality into a time-series metric. Founders live for dashboards, and this one answers the most important question in the room: "Is my agent learning or just pretending?" There's a deeper pattern here—the shift from building models to *operating* them. As agents become the new SaaS, the winners won't be the ones with the smartest base model; they'll be the ones with the tightest feedback loops. A daily score is the heartbeat of that loop. It forces you to define "better," to log changes, and to confront the brutal truth of your own prompt engineering. If AgentScore can nail the balance between simplicity and depth—one number today, breakdowns when you need them—it could become the standard way to speak about AI agent performance. The source article (https://www.producthunt.com/products/latitude-4) positions this as a fresh take on agent observability, and honestly, the timing is perfect. We're about to see a wave of agent-specific analytics tools, but the ones that make "better" measurable on a daily basis will win the dashboard.