Perplexity Computer Is a Super Agent for Everyone Who Doesn't Want to Set One Up
I run OpenClaw locally. I have agents running on a Mac mini, writing blog posts, checking email, managing reminders, and occasionally doing things I didn't ask them to do. It works well, but the setup was not trivial, and the maintenance is real.
Perplexity Computer, which launched yesterday, is a bet that most people want the capability without any of that. I think they're right, and I think the implications are bigger than the launch coverage suggests.
What Perplexity Computer actually does
The feature set is broad enough that it's easy to dismiss as a list of buzzwords, so let me try to be specific about what they've actually shipped:
It can create working websites from a prompt. Not mockups — actual deployable sites. It writes research reports with citations, generates structured datasets, runs analysis on uploaded files, and produces outputs that are ready to use rather than requiring another tool to finish the job.
The 19 AI model access is real flexibility. Different tasks route to different models — a research synthesis task might use one model, code generation another, image work a third. You're not locked into one company's stack for every subtask.
The 400+ app integrations cover the usual territory: Google Workspace, Slack, Notion, GitHub, CRMs, calendar systems. Multi-agent orchestration means it can spin up parallel task streams and coordinate them, which is genuinely different from a single agent trying to do everything sequentially.
All of this runs in the cloud. No local setup. No API key configuration. No maintenance.
How it compares to other agents
The closest comparison points are different enough to be worth separating out.
Local agents (OpenClaw, Jarvis, etc.): If you're already running local agents, Perplexity Computer is not replacing your setup. Local agents have access to your filesystem, your local apps, your system-level integrations. They can be customized deeply and run continuously in the background. The tradeoff is setup complexity and the need to maintain a machine that runs them. Perplexity Computer is a cloud service — it doesn't know what's on your hard drive unless you upload it, and you're running tasks rather than a persistent background assistant.
Operator-style tools (Anthropic Computer Use, OpenAI Operator): These let you direct an AI to actually control a browser or desktop environment. Perplexity Computer doesn't seem to be doing that — it's producing outputs (reports, websites, data) rather than taking actions in existing interfaces. The distinction matters for certain use cases like scraping, form submission, or anything where you need to interact with a system that has no API.
No-code automation tools (Zapier, Make): Those automate workflows between specific apps using predefined triggers and actions. Perplexity Computer takes natural language instructions and figures out the workflow itself. The UX is more like "build me a weekly competitor analysis report and email it to me" than "connect app A to app B when condition X occurs."
ChatGPT with plugins/operators: The closest consumer analogy. OpenAI has been building toward this with their operator features and expanded plugin ecosystem. The difference is likely in the research quality — Perplexity built their reputation on search and citations, and that probably shows in how Computer handles research-heavy tasks. I don't have a side-by-side comparison yet, but the use case targeting feels different.
What this means for non-technical users
This is the part I keep coming back to. I've watched smart, capable people spend a year hearing about AI agents and being unable to actually use them because every tool that does anything interesting requires some level of technical comfort to set up.
"Just run the Python script" is not a trivial instruction for a lot of people. "Configure your API keys in a .env file" is a meaningful barrier. "Set up n8n on a VPS" is not happening for most small business owners, consultants, or researchers who would genuinely benefit from agent-level automation.
Perplexity Computer removes all of that. If it works as described, a journalist can set up a recurring research workflow this afternoon. A small business owner can have a customized competitive analysis tool running by tomorrow. A researcher can automate their literature review process without touching a terminal.
The question is always whether the capability holds up in practice. "Creates websites" could mean anything from generating a single-page HTML file to producing a full deployable Next.js app. "Writes reports" could mean a two-paragraph summary or a properly cited 30-page analysis. Launch marketing tends to show the best cases. What matters is how it handles messy, real-world tasks where the inputs are ambiguous and the desired output requires judgment.
The distribution bet
Perplexity has something that most serious AI agent builders don't: a large, active user base that already trusts them for research tasks. When you launch an agent product on top of an existing search and research tool with real daily active users, you get adoption in a way that a standalone agent product launch doesn't.
This matters a lot. The hardest part of building agent tools isn't the technical capability — it's getting people to try them enough times that the use cases become obvious. Perplexity can surface Computer to users mid-task, in context, when they're already doing something that the agent could help with. That's a very different motion than asking someone to open a new app and figure out what it's for.
OpenAI has comparable distribution through ChatGPT. Google has it through Workspace. The interesting question is whether Perplexity can carve out a distinct position before those two reach feature parity, which they will.
Where I'd actually use this
If I'm being direct: I'd use Perplexity Computer for research-heavy tasks where I want well-cited output and don't want to set up a specialized workflow. Competitor analysis, market research, generating structured data from a description of what I need — these feel like natural fits.
I'd keep my local setup for anything that requires persistent context, filesystem access, or the kind of background monitoring that only makes sense when an agent is always running. Those are different jobs.
For clients who ask me how to start with AI agents, Perplexity Computer probably becomes my first recommendation for people who aren't comfortable with technical setup. It's a better starting point than trying to explain why they need to configure a local environment before they've even validated that agents are useful for their workflow.
The AI agent space is filling in quickly. What's interesting about Perplexity's move is that they're not trying to win the technical user who wants maximum control — they're going after the much larger group who wants maximum capability with minimum friction. That's a bet on distribution over depth, and I think it's probably the right bet for where the market actually is right now.
Steve Defendre is the founder of Defendre Solutions, an AI consulting firm helping organizations adopt AI tools strategically. He writes about AI, veterans in tech, and the future of work.