You’ve probably heard about AI agents like OpenClaw and Hermes Agent. They promise to do your work automatically, but the reality is different: local deployment, API configuration, and environment tweaks make them off-limits for most people. The hype fades quickly when users realize the barrier to entry is still high.
Yet the demand for AI that truly works for you is real. What the market needs is an agent that doesn’t require a technical setup—something you can start using immediately. After testing Skywork (the international version), I recently tried its domestic counterpart: Tiangong Super Agent (let’s call it Tiangong). It skips all the complicated deployment and lets you sign up and run tasks right away. More than a tool, it feels like a cloud team ready to execute your goals, even solving problems on its own when things go wrong.
This “team” is powered by their newly released in-house model, SkyClaw-v1.0. But before we dive into that, let me walk you through three tasks I assigned to it.
Task One: Monitor the Market While You Sleep
I’ve been tracking AI hardware stocks, especially chips and memory, so I wanted a daily US stock briefing delivered to my Lark (Feishu) every morning. Previously, this meant manually scraping data each day. This time, I connected Tiangong to Lark and wrote a prompt: “Monitor US stocks each trading day, focus on three areas: market indices, chip and memory stocks, China internet stocks, plus sentiment and outlook.” I set it as a scheduled task.
The next morning, the report was in my Lark.
Two details surprised me. First, the previous day was a US holiday—markets were closed. Tiangong recognized this and automatically shifted to the most recent trading day, returning real data instead of an empty file. Second, when one data source was blocked during collection, it didn’t crash; it switched to another source and continued.
These small actions reveal a key difference: Tiangong doesn’t just blindly execute instructions—it handles problems. That’s why I call it a team member, not a tool. You don’t have to watch over it; it navigates obstacles on its own.
“The real value of an AI agent isn’t just speed—it’s the ability to adapt when things don’t go as planned.”
Task Two: Build a Deep-Dive Presentation Within Minutes
After that morning briefing, I got interested in the AI compute and HBM (High Bandwidth Memory) sector. I asked Tiangong to research the business completely and create a PowerPoint deck I could share with friends.
I didn’t give it a framework. I simply said: “I want a deck that explains ‘The AI compute business: where does the money come from, where is the bottleneck, who is making it.’ Target audience is tech-savvy but not finance-savvy. Use plain language.”
Tiangong first performed deep research. During that process, one of the sub-tasks failed to launch; it automatically changed strategy and restarted parallel collection. Then it built the deck. In about ten minutes, an 11-slide PPT was ready.
The result exceeded my expectations. Clean, minimal colors, consistent style across all slides—no template-like feel. What impressed me most was that several slides mixed dense text, charts, and real product photos—layouts that usually look messy—but Tiangong arranged them neatly. All data had source citations: NVIDIA earnings, CNBC, BIS official site.
Better yet, the deck wasn’t a one-off deliverable. Every page and element is editable online. You can select any piece and ask AI to modify it. I told it to optimize the layout, and it pointed out that the vertical axis scale on slide 4 was too small, cutting off the tallest bar—then fixed it.
“When AI can point out its own mistakes and correct them, you’re not just using a tool; you’re collaborating with a thinking assistant.”
Task Three: Research an Unfamiliar Topic Thoroughly
Recently, the debate between Codex and Claude Code has been heating up, with both products updating rapidly. I use Claude Code daily but haven’t kept up with Codex. So I asked Tiangong to do a deep research: “Investigate Codex’s core moves in product, model, and marketing over the past month, and generate a multi-format analysis report with images.”
This time I switched models. Tiangong offers multiple AI brains—you can choose SkyClaw 1.0, Deepseek V4 Pro, GLM 5.1, Kimi K2.6, or let the system auto-select. For this task, I used Deepseek V4 Pro.
The result was a four-chapter report with table of contents and references, covering product-level agentization, model benchmarks, marketing, and pricing—all with numbered citations. For factual product comparisons, having sources is crucial; it means the AI isn’t hallucinating.
Like the PPT, the report isn’t static. Every paragraph, every image is directly editable. You can rewrite sections or add content on the fly.
“The real test of an AI research assistant isn’t speed—it’s the ability to produce something you can trust, verify, and build upon.”
What Makes Tiangong Different: Autonomous Problem-Solving
Across these three tasks, one pattern emerges: Tiangong doesn’t just execute commands—it detects anomalies, switches strategies, and self-corrects. This is fundamentally different from traditional automation tools that break when conditions change.
The underlying technology, SkyClaw-v1.0, seems designed for this kind of autonomous decision-making. Combined with the flexibility to switch between multiple language models (Deepseek, GLM, Kimi), the platform becomes a customizable workforce for non-technical users.
Broader Implications: The Democratization of AI Labor
For years, automation has been the privilege of developers and large enterprises. Tools like AutoGPT or BabyAGI still require command-line knowledge. Tiangong removes that barrier entirely. Any professional—marketers, analysts, entrepreneurs—can now “hire” a cloud worker that researches, creates, and iterates.
A 2024 McKinsey report estimated that generative AI could automate up to 60% of current work activities, but adoption has been slowed by complexity. Products like Tiangong could accelerate this shift, especially for small teams with limited technical resources.
Of course, there are limitations. The AI still sometimes misunderstands nuanced instructions or produces irrelevant information. But the ability to edit and refine inline significantly reduces the frustration compared to earlier AI tools.
The Bottom Line: Your New Team Member Is a Prompt Away
Tiangong represents a new category: the accessible AI employee. It’s not a toy, not a research project—it’s a functional tool that handles real work tasks with surprising autonomy.
Whether you’re an investor wanting daily market briefings, a content creator needing a presentation foundation, or a curious mind exploring a new topic, this cloud worker can cut your research time from hours to minutes. The key insight: you don’t need to become an AI expert to benefit from AI expertise.
If you’ve been hesitant to adopt AI agents because they seem too technical, give Tiangong a try. The future of work isn’t about owning smarter tools—it’s about having a team that works smarter for you. And that team is now one registration away.