General AI Agents
The Complete Guide to Autonomous, General-Purpose AI Systems
Key Takeaways
- General AI agents are autonomous systems capable of executing any digital task—not just specialized functions—across multiple domains
- Manus, launched March 2025, pioneered the category with state-of-the-art performance on the GAIA benchmark
- Major cloud platforms (Azure AI Agent Service, Vertex AI Agent Builder) reached general availability in 2025
- The AI agent market is projected to reach $47-52 billion by 2030, growing at 46%+ CAGR
GENERAL AI AGENT MARKET 2026
Sources: SkyQuest, G2 Enterprise AI Report, Warmly AI Statistics
What Is a General AI Agent?
A general AI agent (also called a general-purpose AI agent) is an autonomous artificial intelligence system capable of performing a wide range of digital tasks across multiple domains—rather than being limited to a single specialized function. Unlike narrow AI assistants that can only answer questions or generate text, general AI agents can plan, reason, and execute complex multi-step workflows independently.
Definition
"A general AI agent is a software program capable of acting autonomously to understand, plan and execute tasks across diverse domains—functioning more like a self-directed digital assistant capable of working autonomously in the background."
The term gained prominence in early 2025 with the launch of Manus, which bills itself as "the world's first general AI agent." Since then, the category has exploded with enterprise platforms, open-source frameworks, and startups all racing to build truly general-purpose autonomous AI.
According to recent industry analysis, an AI agent in 2025 is "fundamentally different from traditional AI. It is proactive, autonomous, and goal-oriented. Defined by its ability to reason, plan, and use tools (like software, APIs, and external systems), an agent can be given a complex, multi-step goal and work autonomously to achieve it with minimal human oversight."
Domain Agnostic
Can work across any digital domain—research, coding, data analysis, content creation
Truly Autonomous
Works independently without continuous supervision or step-by-step instructions
Tool Orchestration
Uses browsers, APIs, code execution, and external systems to accomplish goals
General vs Specialized AI Agents
Understanding the distinction between general and specialized AI agents is crucial for choosing the right solution for your needs. While specialized agents excel at specific tasks, general agents offer flexibility across domains.
| Aspect | Specialized AI Agents | General AI Agents |
|---|---|---|
| Task Scope | Single domain (customer service, coding, scheduling) | Any digital task across multiple domains |
| Examples | GitHub Copilot (coding), Intercom Fin (support) | Manus, OpenManus, Claude Computer Use |
| Training Focus | Optimized for specific workflows and domains | Broad capability across all digital tasks |
| Tool Integration | Limited to domain-specific tools | Extensive—browsers, APIs, code, file systems |
| Setup Complexity | Lower—preconfigured for specific use case | Higher—requires defining scope and constraints |
| Best For | High-volume, repetitive domain-specific tasks | Complex, multi-domain tasks requiring flexibility |
"2025 has emerged as a pivotal year for AI agents. In a recent talk, Andrej Karpathy, founding member of OpenAI and former head of AI at Tesla, said that this will be the decade of AI agents."
— Apideck
Manus: The Pioneering General AI Agent
Manus burst onto the scene on March 5, 2025, when Chinese startup Monica.im unveiled what it called "the world's first general AI agent." The name comes from the Latin phrase "Mens et Manus" (mind and hand), reflecting its ability to both think and execute.
What Makes Manus Different
According to MIT Technology Review, Manus "marks a significant departure from conventional conversational AI. It is designed not merely to respond or suggest, but to independently plan, execute, and deliver results for complex, multi-step tasks."
Multi-Model Architecture
Uses Claude 3.5/3.7 Sonnet and fine-tuned Alibaba Qwen models working together
Dedicated VM Per Session
Each user gets a cloud-based virtual machine for full computing environment access
CodeAct Approach
Uses executable Python code as its action mechanism, not just text generation
Live Observation
Users can watch and intervene in real-time via "Manus's Computer" window
Manus Benchmark Performance
On the GAIA benchmark—the industry standard for measuring AI agent capabilities—Manus achieved state-of-the-art (SOTA) performance across all three difficulty levels, surpassing other AI assistants on the market at launch.
Manus Task Capabilities
Wide Research: Multi-Agent Collaboration
In late 2025, Manus introduced Wide Research, a capability where multiple general-purpose Manus instances collaborate on complex tasks. Unlike traditional multi-agent systems with predefined roles (manager, coder, designer), every subagent in Wide Research is a fully capable general agent.
Meta Acquisition
In December 2025, Meta announced it would acquire Manus, signaling the strategic importance of general AI agent technology to major tech companies.
Cloud Platform Solutions for General AI Agents
Major cloud providers have released enterprise-grade platforms for building and deploying AI agents. Both Azure and Google Cloud reached general availability in 2025, marking a maturation of the market.
Azure AI Foundry Agent Service
Microsoft's enterprise AI agent platform
Azure AI Foundry Agent Service reached general availability at Microsoft Build 2025. Since its preview launch at Ignite 2024, over 10,000 customers have used the service.
Key Features
- •Multi-agent orchestration with Connected Agents
- •Agent2Agent (A2A) API for cross-platform interop
- •Browser Automation with Microsoft Playwright
- •Integration with LangGraph, CrewAI, LlamaIndex
Enterprise Integrations
- •SharePoint, OneLake, Azure Data Lake
- •Foundry IQ powered by Azure AI Search
- •Bing Custom Search grounding
- •Azure Cosmos DB for thread storage
Source: Azure AI Agent Service Pricing
Vertex AI Agent Builder
Google Cloud's agent development platform
Vertex AI Agent Builder reached general availability with billing starting March 4, 2025. The platform provides a full-stack foundation supporting the entire agent lifecycle.
Key Features
- •Agent Development Kit (ADK) for Python/Java
- •Agent-to-Agent (A2A) protocol support
- •Sessions and Memory Bank (GA)
- •Bidirectional streaming support
Gemini 2.5 Capabilities
- •1,000,000 token context window
- •"Computer Use" for browser automation
- •Self-fact-checking for reliability
- •Multi-agent system orchestration
NVIDIA ACE (Avatar Cloud Engine)
AI agents for games and digital humans
NVIDIA ACE is a suite of digital human technologies powering agentic workflows for autonomous game characters and digital assistants. At CES 2025, NVIDIA expanded ACE from conversational NPCs to fully autonomous game characters.
ACE Agent Features
- •Agentic vision-language model
- •Multi-language input/output support
- •Speech AI with Riva ASR/TTS
- •LangChain/LlamaIndex integration
Game Partnerships
- •PUBG: BATTLEGROUNDS
- •inZOI
- •NARAKA: BLADEPOINT
- •MIR5 (adaptive AI bosses)
General AI Agent Platform Comparison
Here's a comprehensive comparison of the major general AI agent platforms available in 2025-2026.
| Platform | Provider | GA Date | Key Strength | Context Window | Open Source? |
|---|---|---|---|---|---|
| Manus | Monica/Meta | Mar 2025 | True generality, VM per session | 200K | Partial (planned) |
| Azure AI Foundry Agent | Microsoft | May 2025 | Enterprise integration, security | 128K (GPT-4) | No |
| Vertex AI Agent Builder | Mar 2025 | 1M context, Computer Use | 1,000K | No | |
| OpenManus | MetaGPT Team | Mar 2025 | Manus capabilities, no invite code | Varies by LLM | Yes (MIT) |
| NVIDIA ACE Agent | NVIDIA | Jan 2025 | Gaming, digital humans | Varies | Partial |
| Claude Computer Use | Anthropic | Oct 2024 | Desktop control, safety | 200K | No |
Data compiled from official documentation and Langfuse Agent Framework Comparison
Open Source General AI Agents
The open-source community has rapidly developed alternatives to proprietary general AI agents. According to AIMultiple, 80% of teams rely on open-source frameworks for cost-effectiveness, transparency, and community-driven support.
OpenManus
The leading open-source replication of Manus. Built by the MetaGPT team, the prototype was launched within 3 hours of Manus's release. Provides a modular architecture with PlanningAgent, ToolCallAgent, and SWEAgent components.
LangGraph
The most popular agent framework with 11,700+ GitHub stars and 4.2M monthly downloads. Uses a graph-based architecture where each node handles a prompt or sub-task. Klarna uses it for customer support serving 85M users.
CrewAI
Role-based multi-agent collaboration where each agent has distinct skills. The "Crew" container coordinates workflows, allowing agents to share context. Excellent for systems requiring human-AI or multi-agent cooperation.
Microsoft AutoGen
Microsoft's open-source framework for multi-agent AI systems. Allows multiple agents to communicate by passing messages in a loop. Designed to simplify development and enable cooperation among agents for complex tasks.
For a deeper comparison of frameworks, see our Agentic AI Frameworks Guide.
How to Build a General AI Agent Like Manus
Building an autonomous general AI agent is complex but achievable using open-source tools. Based on technical investigations into Manus and the OpenManus architecture, here are the core components:
Choose a Capable LLM (The "Brain")
The LLM handles high-level thinking: understanding requests, solving problems, and creating plans. Manus uses Claude 3.5/3.7 and Qwen. For open-source, consider Llama 3, Mistral, or Qwen 2.5.
Implement the Agent Loop (Perception → Reasoning → Action)
The core architecture is an iterative loop: analyze the task → plan steps → execute actions → observe results → adjust. Use frameworks like LangGraph or implement the ReAct pattern directly.
observation = perceive(environment)
plan = reason(observation, goal)
result = execute(plan)
task_complete = evaluate(result)
Create a Sandboxed Execution Environment
Give the agent access to a safe computing environment. Manus provides a dedicated VM per session. For local development, use Docker containers with the browserless framework for web automation.
Implement Tool Integration
Connect the agent to external tools: web search (Google, Bing), code execution (Python, Node), file systems, APIs. The OpenManus tool layer includes PlanningTool, PythonExecute, GoogleSearch, and BrowserUseTool.
Add Memory and Knowledge Retrieval
Implement persistent memory for context across sessions and knowledge retrieval (RAG) for grounding responses. Use vector databases like Pinecone, Weaviate, or pgvector.
Quick Start with OpenManus
The fastest path to a working general AI agent is using the OpenManus framework:
Source: OpenManus Documentation
Use Cases and Applications
General AI agents are being deployed across industries. According to Index.dev research, workflow automation is the top use case in 64% of agent deployments.
Research & Analysis
- Market research and competitive analysis
- Investment research and due diligence
- Academic literature review
- Supplier and vendor identification
Software Development
- Autonomous code generation and debugging
- Repository management and bug fixes
- Test generation and execution
- Full application building (90 min for simple apps)
Healthcare
- Medical report writing and summarization
- 24/7 virtual health agents
- Patient symptom monitoring
- Doctor-patient conversation summarization
Finance
- Automated trading strategy formulation
- Financial news and market data analysis
- Compliance document processing
- Risk assessment and fraud detection
Industry Impact Statistics
Source: Warmly AI Agents Statistics
The Future of General AI Agents
The trajectory is clear: general AI agents are becoming the dominant paradigm for AI deployment. Here are key predictions from industry analysts:
Key Industry Predictions
AI agent market size (from $5.1B in 2024), with general agents leading growth
Multiple analysts
Economic value generated through cost savings and revenue uplift across 14 countries
McKinsey
Enterprise software will include agentic AI (up from <1% in 2024)
Gartner
Productivity gains unlocked through AI agents
McKinsey
Challenges Ahead
Not all implementations will succeed. Key challenges include:
- •Trust gap: Only 22% trust fully autonomous AI agents (down from 43% in 2024)
- •Data integration: 80% of IT leaders cite connecting agents to existing systems as the biggest challenge
- •Project cancellations: Gartner predicts 40%+ of agentic AI projects will be canceled by end of 2027
Summary: What You Need to Know
DEFINITION
General AI agents are autonomous systems capable of performing any digital task across multiple domains—not limited to specialized functions.
KEY PLAYERS
Manus (Monica/Meta), Azure AI Foundry Agent Service, Vertex AI Agent Builder, and open-source alternatives like OpenManus and LangGraph.
HOW TO BUILD
Combine a capable LLM with an agent loop (perceive → reason → act), sandboxed execution environment, tool integrations, and persistent memory.
MARKET OUTLOOK
$47-52B market by 2030, with 57% of enterprises already running agents in production and 85% expected to adopt by end of 2025.
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