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Agents are joining us at work -- coding, writing, design. But how do they actually work, especially compared to humans? Their workflows tell a different story: They code everything, slow down human flows, and deliver low-quality work fast. Yet when teamed with humans, they shine on easily programmable steps.

Zora Wang

2,794 subscribers

94,397 Aufrufe • vor 10 Monaten •via X (Twitter)

Kunst Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

18 Kommentare

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

We directly compared humans and AI agents. Across computer-use jobs. Via 5 essential work skills: data analysis, engineering, computation, writing, and design.

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

We induce workflows to: - uniformly represent diverse work activities - enable systematic comparison across workers

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

Our findings reveal a striking divide: Agents approach all work programmatically -- even open-ended, visual tasks like design. Humans rely on UI-centric, perceptual interaction. Their workflows may look similar at a high level, but low-level processes are worlds apart.

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

AI doesn’t always speed us up. 🧠 Augmentation → +24.3% faster, minimal disruption. ⚙️ Automation → -17.7% slower, workflows reshaped by verification & debugging.

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

Quality? Still shaky. Agents fabricate data, misuse tools -- but finish tasks 88% faster and at a fraction of the cost. Maybe the question isn’t agents vs humans.. But how do we team humans & agents?

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

As agents keep climbing up that "autonomy slider" We'll need: - stronger visual understanding - better action calibration, and - workflow-inspired agent designs

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

Check out our: 📄 Paper: 🧩 Workflow induction tool: With the fantastic team @EchoShao8899 @oshaikh13 @dan_fried @gneubig @Diyi_Yang at @LTIatCMU & @stanfordnlp

Profilbild von Niloofar

Niloofarvor 10 Monaten

Really cool work! Had the pleasure of hearing about it today from Daniel haha:

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

wow that was fast! love that it already made it into Daniel's slides 😎

Profilbild von Niloofar

Niloofarvor 10 Monaten

Haha yeah. He spoke very highly of u and mentioned u numerous time 😎

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

grateful advisee moment 🥹

Profilbild von Oleg Rybkin

Oleg Rybkinvor 8 Monaten

Cool work!

Profilbild von Harsh Trivedi

Harsh Trivedivor 10 Monaten

Great work, @ZhiruoW!

Profilbild von Himanshu Kumar

Himanshu Kumarvor 10 Monaten

AI's current strength lies in rapid execution, not creative depth. Human-AI collaboration leverages the best of both worlds for optimal outcomes. This teamwork approach is key for the future.

Profilbild von Alin

Alinvor 10 Monaten

Human-agent collaboration multiplies rather than replaces effectiveness. The bottleneck isn't agent capability anymore - it's interface design that lets humans steer intelligently. What collaboration patterns are you seeing emerge as most productive?

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

One thing we notice is that agents tend to be more proficient in programming, so it could be effective to let them handle readily programmable steps (e.g., data cleaning) while humans focus on the less programmable ones (e.g., reading bill images).

Profilbild von Yana Hudis

Yana Hudisvor 10 Monaten

what did you use to make this animation!

Profilbild von Zora Wang

Zora Wangvor 10 Monaten

keynote 🤫

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