On June 3, Xiaomi released the latest version of its open-source project, SoloEngine. The first low-code Agentic AI development platform.
At the AIGC2026 Summit, Amazon Web Services disclosed a striking statistic: 87% of enterprises claim to have deployed AI, but only 10% have actually extracted real value from it. The current agent industry chain shows a curious pattern—hot at both ends, hollow in the middle. Upstream foundation models and chips attract capital; downstream use-case demand is robust; but the midstream lacks an engineering platform capable of converting domain expertise into reliable agents.
The reasons behind this gap are concrete. Building an AI Agent currently comes down to two approaches. One is low-code workflow platforms: Dify and n8n offer visual canvases where users drag and drop nodes to quickly assemble AI applications. But workflows rely on preset paths—step A leads to step B, step B leads to step C, with if/else conditions controlling branches. Hit something outside the preset, and the flow breaks. At best, they function as AI-powered macro scripts. The other is code-based development frameworks: LangChain and CrewAI support genuine Agentic AI architectures where agents can make autonomous decisions and dynamically adjust strategies. But this requires Python programming skills. A lawyer has to learn Python just to build a legal Agent; a CMO has to configure a CrewAI environment just to set up a marketing Agent team.
Low-code platforms don't support true autonomous decision-making. Code frameworks are only accessible to programmers. SoloEngine fills precisely this gap.
I. SoloEngine: Making Everyone a Creator
Open a browser. Drag Agents onto a canvas. Connect collaboration relationships. Configure the tools you need. Hit run. The backend automatically compiles your design into an executable Agentic AI system—one that plans tasks, executes operations, and delivers results. Users just review and confirm, and the work gets done. No lines of code. No if/else logic to configure.
Each Agent runs a ReAct loop of "think → act → observe → repeat," with all decisions made dynamically at runtime. Take contract review in legal AI as an example: the Agent doesn't follow a preset checklist item by item. It first identifies high-risk clauses, discovers that a non-compete provision is ambiguous, searches relevant case law on its own, and adjusts its review direction based on what it finds. There are no preset paths—every step is dynamically determined by the result of the previous one.
Two contrasting approaches put SoloEngine's direction in perspective.
In early 2026, Manus positioned itself as "the world's first general-purpose AI Agent" and went viral, with invite codes once reselling for 50,000 yuan. Among its 50 official test cases, 87% focused on information gathering and basic analysis; automated reports required manual review 43% of the time; and the mid-step error rate was around 12%. Both Manus and the viral OpenClaw from early 2026 went all-in on the generalist route—capable of doing everything, but the lack of depth made them hard to actually use in production.
Tools like Claude Code, Cursor, and ByteDance's Trae represent the opposite extreme—the vertical route. A single prompt in the IDE and AI handles the entire development lifecycle, extremely efficient—but these are developer-only tools that don't deliver great results for lawyers or marketers.
SoloEngine takes the vertical approach, but with a twist: it lets domain experts in every industry define what their Agents do, how they do it, and what tools they use. A lawyer's Agent handles only legal work. A marketer's Agent handles only marketing—vertical and precise.
Multi-Agent collaboration is another key design element. Multiple Agents independently process the same task and then cross-verify their outputs. One Agent's blind spot is caught by another; one Agent's judgment bias is corrected by another. Bloomberg Law's survey shows that only 5% of lawyers have actually used an AI Agent, and their core concern is accuracy of AI output. Multi-Agent cross-verification directly addresses that concern.
A unified adaptation layer covers OpenAI, Anthropic, Ollama, DeepSeek, Tongyi Qianwen, Zhipu, and other major models—one interface, seamless switching, no lock-in to any single model vendor. Hot-swappable design and progressive disclosure let tools, Skills, and MCP protocols load on demand, cutting token consumption by over 85%.
Assembled Agent teams can be one-click packaged into complete products for anyone to use directly. A lawyer's packaged legal Agent can be sold to fellow practitioners. A marketing team's packaged marketing Agent can be deployed across the entire team, serving 100+ clients.
| Dify/n8n | LangChain/CrewAI | SoloEngine | |
|---|---|---|---|
| True Agentic AI support | ✗ Preset-path workflows only | ✓ ReAct / multi-Agent | ✓ ReAct / multi-Agent |
| Programming required | No | ✗ Must know Python | No |
| Visual orchestration | Partial | ✗ None | ✓ Full canvas experience |
| Can domain experts build independently | Yes (but no true autonomous decision-making) | ✗ | ✓ |
| Multi-Agent collaboration | ✗ | ✓ | ✓ |
II. Three Industries, Three Transformations
Legal. Wolters Kluwer's 2026 survey shows that 92% of legal professionals use at least one AI tool in their daily work. Yet Bloomberg Law's data reveals that only 5% of lawyers have actually used an AI Agent. The vast majority are using AI for basic text processing—drafting emails, formatting documents—rather than letting Agents handle substantive legal work autonomously.
SoloEngine lets lawyers build their own Agent teams. Drag a "Case File Analysis Agent," a "Legal Statute Search Agent," a "Case Precedent Compilation Agent," an "Argument Analysis Agent," and a "Document Agent" onto the canvas, connect their collaboration relationships, and hit run. Multi-Agent cross-verification ensures that outputs are validated by multiple Agents before delivery. Fully zero-code.
Marketing. 34% of enterprise marketing teams are already running at least one autonomous Agent in production—more than double the figure from six months ago. But 87% of marketers are still using AI for basic text polishing and email drafting, rather than building Agent teams that can autonomously handle market analysis, competitive research, and strategy generation.
SoloEngine lets marketers build their own. Drag an "Audience Analysis Agent," a "Competitive Research Agent," a "Strategy Writing Agent," and a "Copy Generation Agent" onto the canvas, hit run, and a complete marketing plan is automatically delivered.
One-Person Companies (OPCs). One-person limited liability companies nationwide have surpassed 16 million, accounting for 27.4% of all enterprises. 2026 has been dubbed "the Year of the OPC," with over 20 cities rolling out dedicated OPC support policies. The core need for this group is "one person + AI = a complete company." SoloEngine's one-click packaging lets OPC entrepreneurs build Agent teams and package them directly into products for sale.
SoloEngine's positioning: No Workflow. No orchestration code. Just Agents that get things done.
From programmers to lawyers, from engineers to marketers—everyone can build their own Agent team. Visit SoloEngine on GitHub and build your first Agent.
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