Top 12 AI Autonomous Agents for Web Interaction in 2025
As artificial intelligence continues to evolve, Autonomous Interactive Agents (AIA) are rapidly transforming how machines interact with the web and operating systems. These AI agents are advanced AI-powered systems designed to perform complex tasks like navigating websites, executing commands, gathering data, and making decisions with minimal human input. Their capabilities are benchmarked using evaluation frameworks like WebArena (web interaction) and OSWorld (system-level problem-solving), which provide key insights into their real-world performance. This blog will compare the latest AI agents, both open-source and closed-source, based on the most recent benchmark results (as of January 2025), helping you understand their strengths, limitations, and use cases.
Understanding the Benchmarks
To effectively measure the performance of AI autonomous agents, two widely used evaluation frameworks—WebArena and OSWorld—provide comprehensive insights into their capabilities in web-based and system-level tasks, respectively. These benchmarks are crucial for assessing how well these agents can handle real-world scenarios and complex operations.
WebArena
WebArena focuses on evaluating an agent's ability to navigate and interact within dynamic web environments. It tests an agent's competence in performing tasks such as:
- Form Filling: Completing online forms accurately and efficiently.
- Website Navigation: Traversing through complex website structures to gather information or execute specific commands.
- Action Automation: Simulating user behaviors, such as clicking buttons, scrolling pages, or managing dropdown menus.
This benchmark mimics real-world tasks commonly encountered in e-commerce, customer support, and data collection scenarios. A higher WebArena score reflects the agent's precision, adaptability, and ability to work in diverse online settings.
OSWorld
OSWorld, on the other hand, evaluates agents on their proficiency in handling system-level tasks. These tasks are more technical and involve:
- File Management: Creating, deleting, or organizing files within a system.
- Application Interaction: Opening, closing, and using various system applications effectively.
- Problem-Solving: Performing troubleshooting tasks, resolving errors, or making logical decisions based on system feedback.
- Operational Efficiency: Managing CPU and memory usage efficiently while completing assigned tasks.
The human baseline accuracy for OSWorld tasks is set at over 72.36%, serving as a reference point to determine how close agents come to human-level performance.
Key AI Autonomous Agents and Their Performance
| Model | WebArena | OSWorld | Openness | Notes |
|---|---|---|---|---|
| OpenAI Operator | 58.0% | 38.0% | Closed | Best overall performer across both benchmarks |
| Jace.AI | 57.1% | N/A | Closed | Provides action descriptions and screenshots |
| ScribeAgent | 53.0% | N/A | Closed | Proprietary training data enhances task handling |
| ORCHESTRA | 52.1% | N/A | Closed | Developed by UNC and Ventus |
| Learn-by-Interact | 48.0% | N/A | Open | Best open-source performer on WebArena |
| AgentOccam-Judge | 45.7% | N/A | Open | Prominent open-source agent |
| UI-TARS-72B-DPO | N/A | 24.6%. | Open | Top performer on OSWorld among open-source agents |
| OSCAR | N/A | 24.5% | Open | Specializes in screenshot-based interaction |
| Aguvis-72B | N/A | 17.04% | Open | Employs a multimodal approach |
| Aria-UI | N/A | 15.15% | Closed | Collaboration between HKU & Rhymes AI |
| OS-Atlas | N/A | 14.63% | Open | Offers multiple model sizes for diverse use cases |
| SeeClick | N/A | 9.21% | Open | Focused on basic web-interaction scenarios |
Closed-Source Leaders
Here is a list of the leading closed-source autonomous interactive agents:
- OpenAI Operator
- Jace.AI
- ScribeAgent
- ORCHESTRA
- Aria-UI
Leading the pack, OpenAI Operator boasts scores of 58% on WebArena and 38% on OSWorld, making it the best overall performer. Its proprietary data and robust algorithms position it as the go-to choice for businesses seeking high performance.
With a WebArena score of 57.1%, *Jace.AI* provides action descriptions and screenshots, offering transparency in task execution, which is beneficial for users managing complex workflows.
This agent achieves 53% on WebArena, leveraging proprietary training data for precise task execution. It’s an excellent choice for businesses requiring advanced task-specific functionality.
Developed by UNC and Ventus, *ORCHESTRA* scores 52.1% on WebArena and is designed for collaborative, multi-agent scenarios.
Scoring 15.15% on OSWorld, Aria-UI is a closed-source agent developed in collaboration between HKU and Rhymes AI. While its OSWorld score is modest, it specializes in niche use cases involving system-level tasks and UI interactions.
Top Open-Source Alternatives
Here is a list of the leading open-source aautonomous interactive agents:
- Learn-by-Interact
- AgentOccam-Judge
- UI-TARS-72B-DPO
- OSCAR
- Aguvis-72B
- OS-Atlas
- SeeClick
As the top open-source performer on WebArena (48%), *Learn-by-Interact* is ideal for developers who prioritize flexibility and community-driven improvements.
Scoring 45.7% on WebArena, it is a versatile open-source agent suitable for research and custom use cases.
The top open-source performer on OSWorld with 24.6%, this agent excels in system-level interaction tasks, making it a valuable option for operating system-level automation.
With a score of 24.5% on OSWorld, *OSCAR* specializes in screenshot-based interactions, offering an edge in UI-driven tasks.
Scoring 17.04% on OSWorld, this agent’s multimodal approach allows it to handle diverse inputs such as images and text.
Scoring 14.63% on OSWorld, OS-Atlas allows developers to tailor the agent’s capabilities to specific operational needs, whether for lightweight tasks or more complex system-level interactions.
With a score of 9.21% on OSWorld, SeeClick is ideal for simpler applications where advanced capabilities are not required, such as basic website navigation or data entry tasks.
Key Observations
- Closed-Source Dominance: Closed-source agents like OpenAI Operator and Jace.AI continue to dominate, leveraging proprietary data to achieve superior performance.
- Open-Source Innovation: Open-source options such as Learn-by-Interact and UI-TARS-72B-DPO are increasingly viable for developers seeking transparency and customization.
- Task-Specific Specialization: Agents like OSCAR and Aguvis-72B are designed for niche applications, such as screenshot-based and multimodal tasks.
Conclusion
The choice of an AI autonomous agent depends on your priorities:
- For performance-driven results, closed-source agents like OpenAI Operator are unmatched.
- If customization and cost-efficiency are critical, open-source options like Learn-by-Interact and AgentOccam-Judge are excellent choices.
- For specific use cases like UI interactions, OSCAR and Jace.AI provide tailored solutions.
As benchmarks evolve, AI autonomous agents will become even more efficient, redefining how machines interact with the web. This blog was inspired by the data available on the GitHub repository, showcasing some of the latest developments in this space. Whether you’re a developer or a business owner, understanding these agents' capabilities is essential for making informed decisions.
