Market context
Personal AI agents are no longer a speculative idea. Large organizations are deploying them at scale, while platform vendors are racing to add direct computer control to their models. Google’s introduction of computer use in Gemini 3.5 Flash is a clear signal that operating real interfaces — not just generating text — is becoming table stakes in this market. At the same time, researchers and executives have acknowledged that agent reliability and true acceleration remain uneven, especially once agents leave sandboxed demos and touch production systems.
This tension explains why the market has split. On one side are experimental tools like Orchids that explore new agent metaphors and automation ideas. On the other are systems like Super that narrow the problem to one hard thing: doing computer work repeatedly without falling apart. Security research has also shown that loosely designed agents expand attack surfaces, making intentional design and reuse strategies increasingly important.
How to evaluate and use this workflow
How to run a fair Super vs Orchids evaluation
- Define a genuinely repetitive task. Choose a workflow you actually run weekly, such as pulling metrics from a dashboard, updating a spreadsheet, and emailing results. This matters because Super’s computer-use cache only shows value when repetition exists, while Orchids-style experimentation often shines in one-off scenarios.
- Execute the task end to end in both tools. Let each agent operate with minimal hand-holding. Observe how often you need to correct navigation, authentication, or state. Take notes on friction rather than speed alone, since operational cost shows up as human supervision.
- Repeat the same task multiple times. Run the identical workflow again on a different day. With Super, note what is reused from prior runs via the computer-use cache. With Orchids, note whether each run feels like starting over.
- Track failure recovery. Intentionally introduce a small change, such as a renamed button or delayed page load. Watch how each agent adapts. This highlights whether the system is robust or brittle under realistic conditions.
- Decide based on ownership cost, not novelty. Weigh the ongoing effort to keep the workflow running. For operators, the best agent is the one that quietly keeps working, not the one that demos well once.
Implementation checklist
- Workflow clarity. Document the exact steps of your task before testing any agent. Clear boundaries reduce false positives where an agent appears capable simply because the human filled in gaps unconsciously.
- Permission scoping. Limit what the agent can access during evaluation. This reflects real deployment conditions and surfaces whether the tool relies on overly broad permissions to succeed.
- Repetition count. Plan at least three identical runs. One successful execution is not evidence of durability in computer-use agents.
- Error logging. Keep a simple log of where the agent hesitates, loops, or asks for help. Patterns matter more than isolated mistakes.
- Change simulation. Introduce a minor UI or data change mid-test. This reveals how tightly the agent is coupled to brittle assumptions.
- Operator time tracking. Measure how much attention you spend supervising each run. Reduced human oversight is often the real ROI lever.
Risks and limits
- Security exposure. Agents that operate real computers inherit real risks. Research has shown common injection flaws in poorly designed agents, making sandboxing and cautious rollout essential.
- Brittleness under change. Even strong agents can fail when interfaces change. Systems without reuse mechanisms often regress completely, increasing maintenance cost.
- Over-automation temptation. It is easy to push agents into tasks better handled by simple scripts or humans. Discipline in choosing agent-worthy work is critical.
- Market noise. The agent space is crowded with bold claims. Many tools labeled “agents” are still thin wrappers around chat models without durable execution.
FAQ
Is Orchids a bad product?
No. Orchids represents a legitimate exploratory approach within the agent ecosystem. It can be useful for experimentation, prototyping, or understanding emerging interaction models. The limitation is not quality but suitability for repeated, operational computer work.
Why does computer use matter more in 2026?
As models improve, text generation has commoditized. The hard problems now live in messy interfaces, authentication flows, and stateful systems. Direct computer use is where real productivity gains — and real risks — appear.
What makes Super cheaper over time?
Super’s reuse of a computer-use cache means repeated workflows can leverage prior execution context. You are not paying the same cognitive and operational cost every run, unlike agents that re-discover the UI each time.
How does this compare to ChatGPT or Gemini?
ChatGPT and Gemini are powerful general assistants evolving toward agents. They excel at breadth. Super trades breadth for depth in computer operation, making it a better fit for narrow but persistent workflows.
Where do Siri, Grok, and Folk fit?
Siri remains voice-first and device-centric. Grok emphasizes real-time and social context. Folk and similar tools occupy niche automation roles. None are primarily optimized for reusable computer-use workflows.
Who should choose Super over Orchids?
If you run the same computer-based task repeatedly and care about reducing supervision and long-term cost, Super is the more practical choice. If you are exploring what agents might become, Orchids-style tools can still be informative.