Manus: In-depth technical analysis of general AI agents and their potential for industrial transformation (with core targets included)

Introduction: The hardcore beginning of the AI ​​agent era

On March 5, 2025, Wuhan Changchang Technology Co., Ltd. (Monica.im) officially released Manus AI Agent, a technology product positioned as "the world's first general AI agent". Manus's outstanding performance in the GAIA benchmark (Level 3 accuracy of 57.7%, compared to GPT-4's 15%) caused a great shock in the industry. Unlike the conversational output of traditional large language models, Manus's breakthrough lies in its end-to-end task execution capabilities and multi-modal results delivery.

From batch parsing resumes to generate structured rating tables, to generating Tesla stock price trend analysis dashboards based on multi-source data, to rendering teaching videos with experimental animations for physics teachers, Manus uses hardcore engineering to achieve a closed loop from "intention input" to "finished product output."

This article will analyze Manus's technical architecture, performance indicators and application scenarios from a technological perspective, and explore its potential opportunities for the global AI ecosystem and China's A-share market. When AI evolves from language generation to autonomous action, is the "technological singularity" of productivity approaching?

1. Product positioning: Paradigm shift from language model to execution engine

1.1 Technical Mission and Functional Boundaries of Manus

The name Manus comes from the Latin word for "hand" (Manus), which accurately reflects its core design goal: to become an autonomous execution agent for humans in the digital world. Traditional large language models (such as ChatGPT and Claude) are limited to generating text or code suggestions, and users need to complete the subsequent execution steps by themselves. Manus directly delivers actionable results by integrating perception, decision-making and execution capabilities. For example, if you enter "analyze Amazon's financial performance in the past four quarters", Manus will automatically complete the following process:

• • Visit the SEC website to download the latest 10-K filing;

• • Extract key indicators such as revenue, profit margin, logistics costs, etc.;

• • Call Pandas to process data and generate trend charts;

• • Export PDF documents containing charts and analyses.

Technology comparison :

1.2 Technology Philosophy: Integration of Data-Driven and Engineering Optimization

Manus was developed based on the principle of “Less Structure, More Intelligence” and achieves capability emergence through the following elements:

Training data optimization:  A multi-stage cleaning process is used to extract high-quality samples from sources such as financial reports, enterprise ERP logs, and GitHub open source projects, and 70% of low signal-to-noise ratio data is eliminated to ensure the model's adaptability to real-world tasks.

Model architecture design:  Integrate multimodal large models (supporting text, image, and table processing) with a mixture of experts (MoE) to dynamically allocate computing resources between reasoning and generation tasks.

Engineering implementation:  Deployed in a cloud virtual machine cluster, equipped with computing power support of up to 128 vCPU cores and 8×A100 GPUs to ensure efficient execution of complex tasks.

This design avoids the redundancy of parameter stacking and focuses on scene-driven practicality, allowing Manus to find a balance between versatility and professionalism.

2. Technical Architecture: System-level breakthrough of multi-agent collaboration

2.1 Three-tier agent architecture: precise division of labor for task execution

Manus' core technology relies on the Multiple Agent Architecture, which achieves efficient task decomposition and execution through three layers of agents:

(1) Planner Agent: Task Decomposition and Path Optimization

Technical implementation:  A task decomposition engine based on Deep Reinforcement Learning (DRL) combined with a dynamic programming algorithm converts user intent into executable workflows.

Workflow Example:  With the input "Optimize PCB Supplier Selection", the Planning Agent generates the following task sequence:

task_sequence = {
    "step_1" :  "Crawl supplier_data FROM 3000 vendors (delivery_time, yield_rate)" ,
    "step_2" :  "Build TCO_model (total_cost_of_ownership)" ,
    "step_3" :  "Run Monte_Carlo_simulation FOR risk_assessment" ,
    "step_4" :  "Generate Top_3_recommendations WITH confidence_scores"
}
optimized_path = drl_optimizer(task_sequence, constraints=[ "budget" ,  "timeline" ])

Technical details:

• • State awareness: Adjust task priorities in real time based on environmental feedback (such as API response latency).

• • Fault-tolerance mechanism: If a data source is unavailable (such as a supplier website down), automatically switch to an alternative source (such as an industry report).

(2) Executor Agent: Deep Integration of Toolchain

Technical implementation:  Built-in multimodal tool execution framework, supporting code generation, browser automation and API calls:

• • Code engine: Integrated Python 3.11 interpreter, pre-installed Pandas, NumPy, Matplotlib, Scikit-learn and other libraries, supporting real-time data processing and visualization. For example, analyzing Tesla stock price trends:

import  yfinance  as  yf
import  matplotlib.pyplot  as  plt
tsla = yf.Ticker( "TSLA" ).history(period= "2y" , interval= "1d" )
tsla[ "MA50" ] = tsla[ "Close" ].rolling(window= 50 ).mean()
tsla[ "RSI" ] = compute_rsi(tsla[ "Close" ], period= 14 )
plt.plot(tsla.index, tsla[ "Close" ], label= "Price" )
plt.plot(tsla.index, tsla[ "MA50" ], label= "50-Day MA" )
plt.savefig( "tsla_trend.png" )

• • Browser Automation: Based on the Playwright framework, it supports headless operations and can perform actions such as login, scrolling, form filling, etc. For example, it automatically handles two-step verification when crawling LinkedIn resumes.

• • API integration: supports 2000+ third-party interfaces, including Bloomberg financial data, GitHub project metadata, Google Maps route planning, etc.

Execution details:  In the "Batch Screen Resumes" task, the execution agent completes the following steps:

• • Unzip ZIP files (supports encrypted formats, automatically tries common password libraries).

• • OCR parsing of PDF (based on Tesseract 5.0, recognition rate up to 98%).

• • Extract skill keywords (such as “reinforcement learning” and “PyTorch”) and generate structured JSON.

• • Call Pandas to generate a worksheet with scores and rankings.

(3) Validator Agent: Ensuring the reliability of results

Technical implementation:  Integrate formal verification and adversarial testing mechanisms to ensure output quality:

• • Formal verification: Check logical consistency, such as verifying the “Assets = Liabilities + Equity” balance relationship of financial statements.

• • Adversarial testing: inject abnormal data (such as setting stock prices to negative values) to test the robustness of the model.

• • Multi-source verification: Cross-comparison of data sources (e.g. Yahoo Finance vs. Bloomberg), with the error threshold set at 5%, which triggers recalculation.

Verification details:  In the "Amazon Financial Report Analysis" task, the verification agent found that the logistics cost data in the official PDF did not match the previous quarter's report. It automatically supplemented the data from the SEC and corrected it, and the final output accuracy was improved to 99.5%.

2.2 Virtual Machine Sandbox: Computing Power Scheduling and Security Isolation

Technical implementation:  Each task runs in an independent Docker container, based on KVM virtualization technology:

• • Resource allocation: Dynamically schedule computing power, allocate 2-core CPU + 16GB RAM for simple tasks, and enable 128-core vCPU + 8×A100 GPU (FP16 precision, peak computing power 312 TFLOPS) for complex tasks.

• • Environmental isolation: supports the coexistence of multiple versions of tools (such as Python 3.8 and 3.11) to avoid dependency conflicts.

• • Asynchronous execution: The task queue is implemented based on RabbitMQ. Users can submit tasks offline and receive results via Webhook or email.

Safety design:

• • Memory encryption: Use AES-256 to encrypt task data to prevent cloud leakage.

• • Process sandbox: Limit container permissions to prevent unauthorized access (such as attempts to access user local files).

2.3 Memory Engine: Context Awareness and User Adaptation

Technical implementation:  Self-developed Manus Vector DB, based on the HNSW (Hierarchical Navigable Small World) algorithm to optimize vector retrieval:

• • Storage capacity: The upper limit of single-user context storage is 10GB, supporting cross-task history tracing.

• • Query performance: Millions of vector queries per second, with latency controlled within 10ms.

Functional details:

• • Short-term memory: Record task preferences (e.g., “prefer line graphs for financial analysis”) and apply automatically within a single session.

• • Long-term memory: Learn user habits across tasks (e.g. “prioritize Bloomberg data for stock analysis”), improving subsequent execution efficiency by 40%.

Application example:  When a user requests "Tesla stock price analysis" three times in a row, the fourth time directly skips the data source selection, calls Bloomberg by default and generates a line chart, shortening the delivery time from 5 minutes to 2 minutes.

3. Performance indicators: the dominant benchmark of GAIA testing

3.1 GAIA Benchmark Framework

GAIA (General AI Assistant Benchmark) was jointly developed by Meta and Hugging Face to evaluate AI's task-solving capabilities in real-world scenarios. It is divided into three levels of difficulty:

• • Level 1: Single-step, clear tasks (e.g., “check the weather in Tokyo in April”).

• • Level 2: Multi-step reasoning tasks (e.g., “Compare three insurance policies”).

• • Level 3: Open domain complex problems (e.g. “writing a crawler to analyze SEC filings”).

3.2 Manus's explosive performance data

Technical details:

• • Level 1: The average task response time is 10 seconds, and the accuracy is limited by the quality of external API data.

• • Level 2: involves 5-10 tool calls, takes an average of 2 minutes, and reasoning depth is the key bottleneck.

• • Level 3: The code generation executable rate reaches 100%, and complex tasks take 5-10 minutes, which is close to the efficiency of human experts.

Data source: OpenAI blog [introducing-deep-research], Manus official website [manus-ai.com].

3.3 Performance Advantage Analysis

Multimodal processing: Supports extracting tables from PDF, parsing data from images and generating Matplotlib charts, increasing processing efficiency by 3 times.

Tool calling efficiency: A single task calls an average of 8 external tools (API, browser, code library), with a throughput of 100 times per minute.

Comparative analysis: Compared with Claude's unimodal reasoning or DeepSeek's limited tool support, Manus's asynchronous execution and result verification mechanism makes it 30%-50% ahead in complex scenarios.

4. Application Scenario: Examples of Productivity Improvement

4.1 Enterprise-level scenarios: efficient engines that replace entry-level manpower

Case details:  In the "Screening New York Real Estate" task, Manus extracted real estate data from Zillow, called the CrimeReports API to analyze the community safety index, combined with Google Maps to calculate commuting time, and generated an interactive web page with a heat map in 3 minutes. The data points covered more than 500 properties with an accuracy of 98%.

4.2 Consumer Scenario: Personal Productivity Assistant

• •  Travel planning:  Enter “Japan Cherry Blossom Tour in April 2025”, Manus integrates JTB attraction data, JR Pass fares and weather API, and generates a PDF manual in 5 minutes, including detailed itinerary, transportation plan and budget details, and map navigation links are accurate to minute-level transfers.

• •  Investment decision:  Analyze "Tesla vs. Nvidia", pull two years of daily data from Yahoo Finance, calculate RSI, MACD and volatility, generate dynamic HTML dashboards, and mark key buying points and risk ranges.

• •  Learning support:  Generate "Momentum Theorem" courseware for middle school teachers, call Manim to render spring collision animation, combine knowledge graph and formula derivation, and deliver a 15-minute teaching video in 10 minutes.

5. Team and founder: the technical genes of entrepreneurial geeks

5.1 Founder Xiao Hong: Practitioner of Engineering AGI

Academic background:  Born in 1992, Bachelor of Software Engineering from Huazhong University of Science and Technology. His master's research direction is distributed system scheduling and resource optimization. His published papers involve multi-task parallel algorithms.

Entrepreneurial experience:

• • 2015: Founded Nightingale Technology and launched “Yiban Assistant” and “Weiban Assistant”, serving over 2 million corporate users. Later, it received hundreds of millions of yuan in investment from Tencent and Zhen Fund.

• • 2022: Incubate Monica, integrate Claude 3.5 and DeepSeek models, have more than 10 million overseas users, and become a Top 3 product in the AI ​​plug-in field.

Technical concept:  Emphasizes that "engineering is the path to realizing AGI", opposes blindly pursuing parameter scale, and advocates the emergence of capabilities through scenario adaptation and system design.

5.2 Core Team: Hard-core Technical Team

• •  Zhang Tao:  Multi-agent system expert, led the design of the DRL engine for planning agents, and participated in Huawei's distributed computing projects.

• •  Yichao Ji:  Head of model optimization, optimized MoE architecture, reduced inference latency by 20%, previously worked in Baidu’s deep learning team.

Technology inheritance:  Inheriting Monica's experience in browser plug-in development, and excelling in tool chain integration and user interaction optimization.

6. Industry Impact: Chain Reaction of Computing Power, Data and Ecosystem

6.1 Hardcore upgrade of computing power requirements

Technical status:  The peak computing power consumption of a Level 3 task is 8×A100 GPUs (312 TFLOPS), which is 2-3 times higher than GPT-4.

Industry Trends:

• • Edge computing: Enterprise-class workstations are upgraded to AI-optimized models (such as Dell Precision 7865, which comes standard with dual H100).

• • Cloud service: Alibaba Cloud launched the “Agent dedicated instance”, providing a flexible combination of 32-core vCPU + 4×A100, with Q1 orders increasing by 150%.

6.2 Hard-core transformation of data infrastructure

Vector database:  Manus Vector DB supports millions of queries per second, with a 30% improvement in storage efficiency. Its valuation potential is comparable to Pinecone (valued at US$750 million in 2024).

Privacy computing:  Integrates federated learning and homomorphic encryption to support enterprise data training without leaving the domain, and has reached pilot cooperation with financial and medical customers.

VII. Technical bottlenecks and evolution direction: challenges and prospects

7.1 Current Technical Limitations

• •  Complex logical reasoning:  In legal clause analysis or multivariate financial modeling, the accuracy rate drops to 40%, which requires the injection of domain expert knowledge.

• •  Tool coverage:  Currently supports 200+ interfaces, which is far from covering the entire industry tool ecosystem (such as CAD software and industrial simulation).

• •  Computing power cost:  In high-concurrency scenarios, the cost of a single task is 0.5-1 US dollars, and the peak latency rises to 15 seconds, requiring optimized resource scheduling.

7.2 Future Technology Evolution

• •  Adaptive tool generation:  Evolve from calling existing tools to independently developing tools, such as generating dedicated Python scripts or API adapters for new tasks.

• •  Multi-agent collaboration:  Introduce professional agency collaboration (such as financial agency + legal agency) to solve complex cross-domain problems.

• •  Ethics and governance:  Develop a responsibility tracking framework to record each step of the decision-making path and avoid legal or economic disputes caused by autonomous execution.

8. Conclusion: Manus’s Enlightenment and the Future of the Industry

The birth of Manus is not only a breakthrough in technology products, but also a hard-core verification of the general AI agent paradigm. It demonstrates the capability emergence path from high-quality data to system optimization with its multi-agent architecture and engineering design.

Technical Inspiration:  The realization of AGI does not rely on the scale expansion of a single model, but rather on the engineering practice of multi-module collaboration.

Industry prediction:  2025 may become the "first year of AI Agent", and computing power infrastructure, data infrastructure and vertical scenario integration will become core growth points.

Philosophical thinking:  When AI takes over repetitive tasks, the focus of human roles will shift to strategic decision-making and creative output.

For technology practitioners and investors, Manus is not only a weather vane, but also a call to action. The hard-core engine of the next generation of productivity revolution has been started, and its impact will radiate from Wuhan to the world.

9. China A-share investment opportunities: AI Agent-driven capital mapping

9.1 Market Status and Response

The release of Manus (March 5, 2025) has triggered a chain reaction in the secondary market:

• •  Alibaba (9988.HK):  Share price rose 7.2%, boosted by cloud computing business and AI open source ecosystem.

• •  Tencent Holdings (0700.HK):  rose 5.8%, WeChat for Business plans to integrate Agent functions, and SaaS revenue expectations were raised.

9.2 Detailed table of A-share listed companies

The following are A-share listed companies affected by the Manus technology spillover effect, covering the fields of chips, computing power, ERP/OA applications and vertical scenario integration, with a special focus on upstream and midstream ERP/OA manufacturers:

9.3 Investment Logic and Trends

Chips and computing power:  Manus's high computing power demand (single-task peak 8×A100) directly stimulates the domestic chip and server market. Companies such as Inspur Information, Sugon, and Cambrian have adjusted their production capacity, and it is expected that shipments in Q1 2025 will increase by 20%-40% year-on-year.

Cloud computing and ERP/OA:  Agent's asynchronous execution in the cloud and integration with enterprise software have driven companies such as UCloud, WeNet, and Yonyou Network to accelerate the deployment of "Agent-ready" services, with a potential market size of over 50 billion yuan. As the leader in OA, WeNet's combination of approval process automation and Agent is particularly prominent, and it is expected to launch a dedicated module in Q2.

Vertical scenarios:  The implementation of Agents in education, medical care, finance and other fields has generated new demands. Companies such as iFlytek and Runda Medical are expected to seize the opportunity.

Data shows that after the release of Manus, the trading volume of the A-share AI sector surged by 15%, and investors' attention was concentrated in the fields of computing power and ERP/OA integration. Manufacturers such as UFIDA Network, Kingsoft Office, Fanwei Network, and Zhiyuan Interconnect were hotly discussed due to their high synergy.