Evaluation of AutoGLM: Features, Architecture, and Practical Test Results

AutoGLM, released by Zhipu AI on March 31, 2025, is presented as the world’s first "think‑while‑doing" AI agent, marking a shift from passive response tools to autonomous task‑executing partners.

1. Core Capabilities

Deep research + real‑time operation: autonomously plans tasks, accesses browsers (e.g., JD.com, Zhihu), and completes data retrieval, analysis, and report generation.

Cross‑domain generalization: covers academic research, e‑commerce comparison, travel planning, financial analysis, etc., such as generating industry reports or children’s programming tutorials.

Multimodal interaction: supports mixed text‑image processing and simulates GUI actions like clicking and typing.

2. Technical Architecture

Full‑stack self‑developed models: built on GLM‑4‑Air‑0414 (32 billion parameters) with inference model GLM‑Z1‑Air, which is eight times faster than DeepSeek‑R1 and costs only 1/30 of it.

Dynamic closed‑loop system: implements a perception‑decision‑execution pipeline to achieve task closure, e.g., automatic website login, user‑review aggregation, and report output.

3. User Experience

Free access: currently available via the Zhipu Qingyan client or browser plugin.

Execution efficiency: complex tasks (e.g., a ten‑thousand‑word research report) take 5–30 minutes but support long step sequences (up to 54 uninterrupted steps).

4. Limitations

Response latency: deep‑analysis tasks incur noticeable delays.

Data dependency: performance in specialized domains (e.g., medicine) relies on public platform data, affecting authority.

Testing Scenarios

Test 1 – Client operation : The agent only output content without performing any browser actions or completing the intended task.

Test 2 – Chrome extension : The browser launched but the agent terminated prematurely, leaving the task unfinished.

Test 3 – Re‑run with client : The agent accessed websites and learned content but ultimately produced only a course‑design output, not the expected automated workflow.

Conclusion

The practical tests reveal that AutoGLM’s client rarely invokes the necessary tools, resulting in limited output; this may stem from testing methodology or function‑call issues. Further product iterations are anticipated to deliver a more truly intelligent autonomous agent.