Just as dust began to fall on DeepSeek, another breakthrough from Chinese startups swept the internet. This time, this is not a generated AI model, but a completely autonomous AI agent Manus launched by Chinese company Monica on March 6, 2025. Unlike Chatgpt and DeepSeek (such as Chatgpt and DeepSeek), it is simply a response to prompts, Manus is designed to work independently, make decisions, perform tasks, and generate decisions with minimal human involvement. This development marks a paradigm shift in AI development, from a reactive model to a completely autonomous proxy. This article discusses the architecture of Manus AI, its advantages and limitations and its potential impact on the future of autonomous AI systems.
Explore Manus AI: A hybrid approach to autonomous agents
The name “manus” comes from the Latin phrase Mens et al. This means thoughts and hands. This nomenclature perfectly describes the dual ability of Manus to think (process complex information and make decisions) and ACT (execute tasks and generate results). To think, Manus relies on large language models (LLM), and to act on it, it integrates LLM with traditional automation tools.
MANUS follows a neural symbolic approach to performing tasks. In this approach, it adopts LLM, including anthropomorphic Claude 3.5 sonnets and Alibaba’s Qwen, to interpret natural language cues and develop viable plans. LLMS has deterministic script enhancements for data processing and system operations. For example, while LLM may draft Python code to analyze datasets, the backend of MANUS executes code in a controlled environment, validates outputs and adjusts parameters when errors occur. This hybrid model can balance the creativity of generating AI and the reliability of programming workflows, allowing it to perform complex tasks such as deploying web applications or automating cross-platform interactions.
At the heart of Manus AI is running through a structured proxy loop that simulates human decision-making processes. After a task is granted, it first analyzes the requests that identify the target and the constraints. Next, it selects tools from its toolkit (such as a web scraper, data processor, or code interpreter) and executes commands in a secure Linux sandbox environment. This sandbox allows MANUS to install software, manipulate files and interact with web applications while preventing unauthorized access to external systems. After each action, the AI evaluates the results, iterates over its methods, and refines the results until the task meets predefined criteria for success.
Agent architecture and environment
One of the key features of Manus is its multi-agent architecture. The architecture relies primarily on a central “executor” agent responsible for managing various professional sub-agents. These sub-agents are able to handle specific tasks such as web browsing, data analysis and even coding, which allows MANUS to solve multi-step problems without other human intervention. Additionally, Manus runs in a cloud-based asynchronous environment. Users know that the agent will continue to work in the background and send the results after completion, can assign tasks to MANUS and then disconnect from contact.
Performance and benchmarking
Manus AI has achieved great success in industry-standard performance testing. It shows the latest results of the Gaia benchmark, which was created by Meta AI, embrace surfaces and AutoGPT to evaluate the performance of proxy AI systems. This benchmark evaluates the ability of AI logical reasoning, to process multimodal data, and to perform real-world tasks using external tools. Manus AI’s performance in this test puts it ahead of well-known players such as OpenAI’s GPT-4 and Google’s model, and has established it as one of the most advanced General AI agents today.
Use Cases
To demonstrate the practical capabilities of Manus AI, the developers showed an impressive range of use cases during the release process. In one case, Manus AI is required to handle the recruitment process. When you get a collection of resumes, Manus is more than just classified by keywords or qualifications. This is further developed by analyzing each resume, cross-reference skills with job market trends, and ultimately providing users with detailed recruitment reports and optimization decisions. Manus accomplished this task without the need for other human input or supervision. This case shows its ability to automatically handle complex workflows.
Similarly, when asked to generate a personalized trip itinerary, Manus takes into account not only user preferences, but also external factors such as weather patterns, local crime statistics and rental trends. This goes beyond simple data retrieval and reflects a deeper understanding of user-definition needs, which illustrates Manus’ ability to perform independent, context-aware tasks.
In another demonstration, Manus was tasked with writing a biography and creating a personal website for tech writers. Within minutes, Manus scratched social media data, created a comprehensive biography, designed the website and deployed it on-site. It even automatically pins hosting issues.
In the financial field, Manus’s task is to conduct correlation analysis of stock prices of NVDA (NVIDIA), MRVL (Marvell Technology) and TSM (Taiwan Semiconductor Manufacturing Company). MANUS first collects relevant data from the Yahoofinance API. It then automatically writes the necessary code to analyze and visualize stock price data. Afterwards, Manus created a website to display analytics and visualizations, generating shareable links for easy access.
Challenges and moral considerations
Despite the extraordinary use cases, Manus AI faces some technical and ethical challenges. Early adopters reported issues with the system entering a “cycle”, which repeatedly performed invalid actions and required human intervention to reset the task. These failures highlight the challenges of developing AI that can consistently navigate unstructured environments.
Furthermore, while Manus runs in isolated sandboxes for security purposes, its web automation capabilities raise concerns about potential abuse, such as scratching protected data or manipulating online platforms.
Transparency is another key issue. Manus’ developers highlighted the success story, but independent verification of its features is limited. For example, while its demo dashboard generation runs smoothly, users observe inconsistencies when applying AI to new or complex scenarios. Lack of transparency makes building trust difficult, especially when companies consider delegating sensitive tasks to autonomous systems. Furthermore, the lack of clear indicators to evaluate the “autonomy” of AI agents gives room for doubt whether Manus represents real progress or simply complex marketing.
Bottom line
MANUS AI represents the next frontier of artificial intelligence: autonomous agents that can perform tasks independently or without human supervision in various industries. Its emergence marks the beginning of a new era where AI does more than just assist, it is a fully integrated system capable of handling complex workflows from start to finish.
Although it is still in the development of Manus AI, the potential impact is obvious. As AI systems like Manus become more complex, they can redefine industries, reshape the labor market, and even challenge our understanding of what we mean by work. The future of AI is no longer limited to passive assistants, but about creating systems for thinking, acting and learning for themselves. Manus is just the beginning.
