Welcome to the era of agents—not human, but smarter, scalable, always-on digital agents. While everyone’s busy talking about what AI might do, we at Switas are already building with it.

As a verified provider on the Deepin AI Agent Marketplace, we’ve been creating practical, real-world AI agents that actively support Growth, CRO (Conversion Rate Optimization), and Product teams. This article explores the use cases we’re implementing today—and how AI agents are evolving from buzzwords into business tools.

What Is an AI Agent (and Why Should You Care)?

Let’s keep it simple:
An AI agent is an autonomous system that performs tasks on your behalf based on goals you set, using tools, logic, and even multi-step reasoning.

They’re not just smarter chatbots. They go beyond responding—they plan, take action, observe the results, and iterate. Think of them as interns that don’t sleep, don’t miss details, and learn on the job.

Use Case #1: The Autonomous Growth Hacker

Problem: Budget is bleeding on underperforming campaigns. Teams can’t catch it fast enough.
Agent in Action:

  • Monitors ad campaigns across Google, Meta, and TikTok.
  • Detects anomalies like skyrocketing CPC or low CTR.
  • Recommends (or executes) budget shifts, pauses, or creative swaps.
  • Pulls performance data into dashboards and delivers a morning report.

Outcome: Campaigns stay lean, optimized, and high-performing without waiting for the weekly report meeting.

Use Case #2: The UX Conversion Sentinel

Problem: You made a small change. Conversions dropped. No one noticed until the end of the month.
Agent in Action:

  • Connects to Clarity, Hotjar, or GA4 to monitor user flow daily.
  • Flags friction patterns: rage clicks, form abandonments, bounce spikes.
  • Provides quick hypotheses like: “New CTA color decreased conversion by 12% on mobile.”
  • Sends Slack alerts or creates tasks in your project management tool.

Outcome: Real-time conversion surveillance. Proactive UX fixes before revenue takes a hit.

Use Case #3: The Product Feedback Synthesizer

Problem: You have a sea of feedback and feature requests. What should you build next?
Agent in Action:

  • Scans support chats, app reviews, Canny boards, NPS comments.
  • Clusters feedback using semantic search (LLM + embeddings).
  • Ranks by urgency, frequency, and potential impact.
  • Outputs a prioritized product roadmap update.

Outcome: PMs stop guessing. Features are driven by real voice-of-customer insights, not opinions.

Why This Works (and Where It Doesn’t... Yet)

AI agents are best at:

  • Repetitive analysis (what changed?)
  • Pattern recognition (what’s working?)
  • Low-level execution (take action or send alerts)

But they’re not:

  • Fully autonomous decision-makers (yet)
  • Free of hallucination risks
  • A replacement for human intuition

That’s why at Switas, we pair our agents with structured guardrails and human-in-the-loop verification—so you get both speed and accuracy.

What’s Next: The Agent-Powered Stack

We’re building toward a modular AI agent framework—one where every team at a startup or scale-up can have agents plugged into their stack, tailored to their KPIs and tools.

As a verified Deepin provider, we’re excited to push this ecosystem forward—co-developing agents that help businesses:

  • Test more, guess less (Growth)
  • Monitor more, panic less (CRO)
  • Build smarter, not louder (Product)