Meet the Agent Performance Console: Manage AI Like You Manage Your Team

AI is no longer a pilot project.

It’s answering guest questions at midnight. It’s handling hundreds of repetitive inquiries that used to drain your team’s time. In many organizations, AI agents are already functioning like digital employees.

And yet, there’s one question leadership teams keep circling back to:

Is it actually working?

Not “Is it live?”
Not “Is it responding quickly?”
But is it driving revenue, improving engagement, reducing workload, and delivering measurable outcomes?

At Satisfi Labs, we’ve spent nearly a decade deploying AI agents across destinations, experiences and campuses. We learned something critical along the way: building AI is not the challenge anymore. Proving its impact is.

Most dashboards still focus on surface-level metrics like conversation volume or response time. Those numbers can look impressive, but they rarely answer what executives actually care about. Volume doesn’t equal value. Speed doesn’t equal revenue. A spike in conversations doesn’t necessarily mean your business moved forward.

The real issue isn’t data scarcity. It’s measuring the wrong things.

So we decided to build something that solves this.

Introducing the Agent Performance Console

The Agent Performance Console is a management platform designed to help you manage your AI agents the way you'd manage employees, with clear performance expectations, measurable objectives, and transparent accountability.

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The idea is simple: if you're going to treat AI agents like digital staff members (and you should), you need to manage them like digital staff members. That means going beyond "how many conversations happened" and into "did this agent accomplish what it was supposed to accomplish."

The OKR framework: managing agents like employees

We built the Console around Objectives and Key Results: the same OKR framework that companies like Google have used for decades to align teams around outcomes.

Here's how it works for AI agents:

Every agent gets a set of objectives based on its specific role. A Guest Experience Agent has different goals than a Ticketing Agent. An F&B Agent is measured on different things than a Meetings & Conventions Agent. Because they have different jobs, they should be held to different standards.

Under each objective, there are key results: specific, measurable targets that tell you whether the agent is on track. You log in and see green, yellow, or red, allowing the Satisfi Labs team to proactively act on where the improvement is needed.

The benchmarks are based on nearly ten years of performance data from hundreds of businesses across sports, entertainment, and tourism. They account for seasonality, venue type, industry norms, and what actually works in the real world. So when the Console says you're on or off track, it means something.

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Campaigns: the missions your agents run

Here's where the Console changes the traditional SaaS model.

Inside the Console, agents run Campaigns: structured, AI-driven missions that are each tied to a specific business goal. Each campaign comes with defined objectives, measurable key results, integrations, and what we call journey workflows.

Journey workflows are the piece that most AI tools skip. It's not enough to generate an answer. The hard part is orchestrating how a user's question gets guided into a real outcome. That's the difference between "answering a question about tickets" and "guiding someone from a question about tickets to an actual purchase."

Campaign examples are: event discovery campaign, ticket concierge campaign, season ticket promotion campaign. Each one is designed around the outcome you're trying to drive. More campaigns means more value, and all of it is tracked in one place.

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Talk to your agent about its job

This is probably the feature that excites leaders most.

Inside the Console, you can chat with your AI agent about its own performance. Like you're having a check-in with an employee.

You can ask things like:

• "How are we tracking against our goals this month?"
• "What topics are trending this week?"
• "How are you driving revenue?"
• "What should we fix?"

The agent responds with real insights pulled from its own data. And when the agent spots something that needs human attention, like maybe a workflow isn't performing, it can flag it directly to your AM. So improvements happen proactively, not reactively.

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Think of it as a weekly check-in with your digital team

The best way to use the Console is the simplest: treat it like a weekly management meeting.

Log in. Check your topline metrics: inbound messages, daily active users, quality response rate. Click into each agent to see how their campaigns are tracking. Chat with your agent about what's working and what's not. Make adjustments based on data.

Your AI agents should be held to the same standards as your human staff. The Console makes that possible.

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Ready to see it?

Schedule a demo and we'll show you what outcome-driven AI management actually looks like.