Personal AI agents crossed a threshold in 2026: they stopped being just chat interfaces and started operating computers. Google’s rollout of computer use in Gemini 3.5 Flash made that shift mainstream, while products like Folk and Super pushed the idea further by wrapping autonomy, memory, and workflow context around it. If you’re comparing Super vs Folk, the real question isn’t model quality—it’s how much real work you want an agent to do on your behalf, and how safely.
Market context
Agentic AI is now defined less by conversation and more by execution. Google DeepMind frames this as “computer use”—models that see a screen, move a cursor, and take actions in software environments [blog.google](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-computer-use-model/). Gemini 3.5 Flash brought this capability into Google’s ecosystem, but recent reporting also highlights new attack surfaces when agents control browsers and desktops [searchenginejournal.com](https://www.searchenginejournal.com/google-gemini-can-now-control-your-computer-hackers-are-already-targeting-ai-agents/). MIT researchers argue that the next competitive edge is not raw autonomy, but bounded, well-instrumented agents that can be audited and corrected [mit.edu](https://news.mit.edu/2026/qa-what-is-agentic-ai-today).
Folk positions itself as a personal agent living in iMessage or Telegram, with a dedicated cloud computer per user. It emphasizes persistence (memory, scheduled tasks, proactive alerts) and claims a strong privacy stance with no training on user data. Super, by contrast, comes from the enterprise search and workflow world: it focuses on connecting internal knowledge, SaaS tools, and repeatable work patterns at lower cost than traditional enterprise AI stacks [super.work](https://super.work/compare/alternative-to-glean). Both rely on modern foundation models, but their design center is different.
Where Super and Folk diverge in practice
Folk’s biggest differentiator is that it behaves like a long-running personal operator. You can ask it to watch flights, book tables, or send morning briefings without re-prompting. This is enabled by its always-on environment and what many builders now call a computer-use cache: a persistent execution context that remembers state between tasks. Gemini’s computer use, by comparison, is still largely session-based unless developers add their own scaffolding [ai.google.dev](https://ai.google.dev/gemini-api/docs/computer-use).
Super’s advantage shows up when work spans many documents and systems. Its agent is optimized for retrieval, synthesis, and workflow automation across company tools, aligning with Anthropic’s guidance that effective agents depend on high-quality tools and constraints rather than unlimited freedom [anthropic.com](https://www.anthropic.com/engineering/building-effective-agents). Instead of a single personal cloud computer, Super orchestrates tasks across APIs and knowledge sources, reducing the risk of brittle UI automation.
How to choose between Super and Folk for real computer work
The choice comes down to scope and control. If you want a personal AI that lives in your texts, runs scheduled tasks, and directly manipulates websites for you, Folk feels closer to a digital concierge. If you need an agent to search, reason, and automate work across business systems—with clearer guardrails—Super is often the better fit. In both cases, pay attention to how state is stored. A robust computer-use cache can save time, but it also requires clear reset and audit mechanisms.
Implementation checklist
- Define the tasks you expect the agent to run without supervision.
- Map which actions require UI-level computer use versus API-level automation.
- Confirm how persistent memory and computer-use cache data can be inspected or cleared.
- Set approval steps for high-risk actions like purchases or deletions.
- Review pricing relative to actual task volume, not message count.
Risks and limits
Computer-controlling agents introduce new security risks. As Search Engine Journal notes, attackers are already probing agent workflows for prompt injection and UI spoofing vectors [searchenginejournal.com](https://www.searchenginejournal.com/google-gemini-can-now-control-your-computer-hackers-are-already-targeting-ai-agents/). Folk’s always-on model magnifies both convenience and blast radius if misconfigured. Super’s tighter integration model can limit autonomy but may reduce exposure. Neither approach eliminates the need for human oversight.
FAQ
Does Folk use Gemini models? Folk primarily uses models from OpenAI and Anthropic, with support for bringing your own API keys, including Gemini via OpenRouter [getfolk.app](https://www.getfolk.app/alternative/gemini).
Is Super a personal assistant like Folk? Super is closer to a work agent: it focuses on search, synthesis, and workflow automation across tools rather than acting as a messaging-native concierge.
Are computer-use agents reliable enough in 2026? They are improving rapidly, but MIT and Anthropic both emphasize constrained autonomy and strong tooling as best practice [mit.edu](https://news.mit.edu/2026/qa-what-is-agentic-ai-today), [anthropic.com](https://www.anthropic.com/engineering/writing-tools-for-agents).