Historical source note: This is a full translation of the published synthesis, including its dated observations, examples and judgments. Statements about product capabilities, costs, investing and forecasts are attributed historical claims, not newly verified facts or investment advice. Later additions retain their own source and evidence limits.
Wang Kai began observing the agent wave in August 2024, including early Manus developments, but repeatedly warned that calling it “the first year of agents” was premature: tokens were expensive and business models remained unproven. In Q1 2026 he began building agent systems himself, using Chrome MCP, loops and multiple agents. His framing evolved toward agents as a new way of collaborating.
1. A Timeline of Agent Development
graph LR
A[April 2023: SEC FastFacts<br/>Early workflow] --> B[Q3 2024: Devin demonstration<br/>Agent autonomy levels]
B --> C[January 2025: Coding unicorns<br/>Cursor / v0 / Manus]
C --> D[March 2025: Manus gains attention in China<br/>Higher token consumption underneath]
D --> E[July 2025: ChatGPT Agent<br/>OpenAI optimizes a dedicated model]
E --> F[Q4 2025: MCP ecosystem matures<br/>Claude connects to 100+ services]
F --> G[Q1 2026: Chrome MCP adoption<br/>Multi-agent automation]
G --> H[April 2026: Wang builds an agent cluster<br/>Multiple browsers, accounts and agents]
2. Agent Levels, by Analogy with Autonomous Driving
| Level | Description | Position in the historical account |
|---|---|---|
| L1: Single-task assistance | A person sets the task, AI writes, and the person executes | Widespread |
| L2: Multi-step assistance | AI helps break down tasks and guides a person | Cursor, v0 and deep research |
| L3: Partial autonomy | AI completes multi-step tasks under human supervision | Devin and Claude Code Agent |
| L4: Full autonomy | An agent completes complex tasks without human intervention | Wang anticipated a further 90% reduction in token prices |
| L5: Agent organizations | Multiple agents collaborate on organization-level tasks | OpenAI's L5 vision |
Wang placed his own usage at L3, including Claude Code running investment strategies and automated promotion.
3. A Practical MCP Checklist
graph TD
A[Claude Code] --> B[Financial MCP: FMP API<br/>Data accuracy]
A --> C[Chrome MCP: control a real browser]
A --> D[Feishu CLI: documents and bot]
A --> E[TickTick MCP: task management]
A --> F[Notion / Gmail and other services]
A --> G[AgentMail: agent email]
A --> H[Custom MCP: tokrepo skills]
3.1 FMP MCP: Financial Data Accuracy
- Problem in Wang's account: numbers obtained through Claude/GPT web searches were, in his emphatic wording, “100% unreliable” because of fabrication. This is his characterization, not a measured universal error rate.
- His solution: pay for the FMP API, build an MCP server, and have Claude retrieve data through it.
- Reported effect: investment analysis moved from guessing toward more rigorous, trustworthy work.
- His operating rule: route data-dependent AI output away from generic web search.
3.2 Chrome MCP: An Important April 2026 Upgrade
- The version he described as official used a headless browser without user data.
- His modification combined session reuse with a browser containing the user's existing profile.
- It addressed repeated authorization and browser focus contention.
- He reported that multiple agents could control separate browsers concurrently without interfering.
- He shared the skill on tokrepo: Chrome MCP Background Proxy.
3.3 What Matters in a Custom MCP Server
Wang recommended MCP rather than general AI web search whenever accurate data is required. Although API subscriptions appear expensive, he regarded their accuracy as delivering strong returns on token spending. Connecting the workflow's data sources lets Claude act as a central coordinator. These are his engineering preferences; an API connection still requires checking data coverage and correctness.
4. An Automation System: A Q1 2026 Turning Point
graph TB
A[Define an automated task] --> B[Start the agent]
B --> C[Check every 10 minutes<br/>Has execution stopped?]
C -- Stopped --> D[Restart Claude Code]
C -- Running --> B
B --> E[Daily report to Feishu bot]
B --> F[Handle exceptions and notify]
Wang's April 12, 2026 realization: Claude Code kept deciding to stop when he wanted it to continue. He had tried to configure complicated scheduled tasks with /loop, then realized that a check every ten minutes could simply tell it to continue if it had stopped, avoiding a complex loop.
His examples included monitoring 700 stocks and sending trading signals through a Feishu bot, calculating investment targets' intrinsic value daily, automated promotion in YouTube/Reddit comments, and concurrent content distribution across multiple accounts and browsers. These are reported uses, not instructions or evidence of effectiveness.
5. Multiple Agents, Browsers and Accounts: April 2026
graph TB
A[One computer] --> B[5+ browsers]
B --> B1[Browser 1: Google account A]
B --> B2[Browser 2: Google account B]
B --> B3[Browser 3: Google account C]
B --> B4[Browser 4: Google account D]
B --> B5[Browser 5: Google account E]
B1 --> C1[Claude Code window 1<br/>YouTube promotion]
B2 --> C2[Claude Code window 2<br/>Reddit promotion]
B3 --> C3[Claude Code window 3<br/>Dev.to promotion]
The core idea was to avoid switching accounts inside a single agent. Each browser had one account environment, keeping Google accounts separate, and each Claude Code window was bound to one browser. Wang described distributing work across dozens of platforms at once.
The account described prerequisites including overseas phone numbers—US/UK SIM cards purchased through Taobao to activate overseas account environments—multiple Google/Apple/Claude accounts, and overseas banking or payment services such as Stripe, Wise and Lemon Squeezy. This preserves the historical account rather than recommending procurement or circumvention practices.
6. An Autonomous Decision Experiment: $200 for a Polymarket-Type Project
On March 12, 2026, Wang described letting Claude Code independently explore a Polymarket-like crypto project, giving it $200 of capital and asking it to read all the RSI AI trading documents in Feishu, distill a skill, and make prediction-market decisions.
His conclusion as of May 2026 was that autonomous investment decisions had not reached L4. The system could run steadily within a prompt-defined scope, but humans still had to unlock improvements in strategy. In his words, the agent's ceiling depended on the height of his own understanding.
7. Agents in Investing: His Highest-Value Use Case
graph LR
A[Market information] --> B[Agent 1: retrieve data<br/>FMP MCP]
B --> C[Agent 2: quantify risk<br/>Score five variables]
C --> D[Agent 3: intrinsic value<br/>A 6,000-character prompt]
D --> E[Human decision<br/>Currently L2]
E --> F[Agent 4: execute orders and monitor]
In this account, humans still make the decision, while agents handle information gathering, risk quantification, valuation and execution support. The separate L2 label here describes the investment decision workflow; it does not erase his broader L3 usage classification.
8. Devin, Manus, Cursor and Other Tools
| Tool | Type | Wang's historical assessment |
|---|---|---|
| Devin | Coding agent | Impressive demonstrations; practical use still L3 |
| Manus | General-purpose agent | A relatively complex early Chinese product, but not a high barrier to entry |
| Cursor | Coding assistance | A main tool and useful for data analysis |
| Claude Code | Command-line agent | His main tool in 2026; changed collaboration workflows |
| v0 | Product-design agent | His colloquial comment was that it could be pushed with a 10,000-character prompt |
| Stitch | UI-design agent | In his experience, improved rapidly after switching to Gemini |
9. The Autonomy-versus-Control Trap
In Wang's analysis of an experiment giving six models $10,000 for autonomous crypto decisions, the initial question introduced a bias. As later information changed, decisions remained anchored in current holdings. He observed that AI optimized details without stepping back to say “sell everything and keep cash.” His conclusion was that truly autonomous decisions need permission to exit positions, or the system may stay trapped in local optimization. This is a reported critique, not a validated trading rule.
10. Suitability Across Scenarios
| Scenario | Rating in the source | Rationale in the source |
|---|---|---|
| Coding | 5/5 | Results can be checked: the program runs |
| Investment decisions | 4/5 | He treated profit as an observable outcome |
| Mathematics | 5/5 | Standard answers exist |
| Content generation | 3/5 | Judgment is subjective and can drift |
| Market research | 3/5 | Source quality strongly affects results |
| Law and medicine | 2/5 | Entry requirements and high risk |
Wang cited work by Jason Wei to express the idea that verifiable tasks are an AI strength, and hard-to-verify tasks a weakness. The table preserves his heuristic: running code does not prove every requirement, and investment profit alone does not establish decision quality or causality.
11. Agent Business Models
On March 29, 2025, Wang argued that general agents were not an attractive startup direction for very small teams. His reasons were token costs of approximately $10 per task, competition from model companies themselves—such as OpenAI using an optimized model for ChatGPT Agent—and loss-making competition before the ecosystem matured.
He nevertheless saw opportunities around agents: template libraries, directories, curated examples, and vertical agents combining existing capabilities to serve a specific need.
12. Agent Loops Versus Traditional Scheduled Jobs
The internet era already had cron and scheduled tasks, but these followed prewritten rules. Wang saw an AI agent loop as repetition with judgment: if one state appears, take one action; otherwise take another. He assigned it the repetitive tasks involving judgment that he could identify.
13. Statements Attributed to Wang Kai
“Agents will not truly take off until token prices fall another 90%.”
“Agents change the mode of collaboration, not just process efficiency.”
“Build a system that runs reliably rather than jumping straight into execution.”
“One person can cover 100 things if they know how to build an agent system.”
“Give automated tasks to AI so I can work on higher-level things.”
These are translations of statements quoted in the published Chinese synthesis.
References and Related Notes
- Wang Kai: AI Coding — the foundations for building agents with Claude Code.
- Wang Kai: Investment Strategy — the investing use case.
- Wang Kai: Startup Methodology — his view of agents as a startup direction.
- Wang Kai: Overseas Marketing — multiple agents and browsers across platforms.
- Newtype: AI Agent Architecture — comparison of architecture evolution.
June 2026 Addition: Minimum Boundaries Product Managers Need to Understand for Long-Running Agents
This section draws on Wang Kai's text posts dated June 9 and 16, both read in full. The linked articles recommended by those posts were not provided in full with that batch, so this is not presented as an implementation analysis of those external articles.
Read Engineering Mechanisms to Know When They Cannot Be Relied On
For product managers, a harness—the execution system around a model that keeps it working—memory, and context engineering—deciding what information the model sees at each step—first help identify capability boundaries: what the system may do, what information it may lose, and why it may stall. Wang argues that as AI enters productivity settings such as investing, and systems become more opaque, this partly technical judgment becomes more important. Using a tool does not require mastering all its underlying code.
From Executing One Task Segment to Repeatedly Acting Toward a Goal
The June 16 post places Claude Code's Loop, Codex's Goal and automation under one need: let an agent advance a goal over multiple rounds until it is achieved. Beyond the scheduled-task experience already described here, the new observation concerns longer collaboration and more complex tasks. Evaluation should ask whether the goal and progress persist across rounds, rather than compare only answer quality in a single conversation.
These two posts support only those product-level observations. They do not provide concrete implementations for stopping criteria, spending limits or failure recovery. Consult the relevant project documentation for configuration; a recommended link's title does not establish a tested implementation.
Addition: Improve source discovery through reading feedback
This addition is based on Wang Kai's written account dated October 12. The available link identifies the community rather than the individual post, and attachments were not collected. His scoring formula, code and recommendation-system implementation were not available.
Fixed subscriptions help readers follow known authors but can repeatedly surface the same circle of material. Wang describes an agent that searches for unfamiliar sources around selected topics, with the resulting feed evaluated through his daily reading. He found a blog post useful before learning about its author. The lesson is to evaluate the article's contribution separately from the author's reputation.
The task can be organized as discovering candidates, reading specific articles, reporting what was useful and adjusting the next search. As an editorial application, keep the candidate link and discovery topic, explain the new fact or method it contributes and record why further reading is worthwhile. Retain examples that were irrelevant, repetitive or vague as well. Counting new domains measures search activity, not improved reading quality.
Use this approach when an established feed becomes repetitive and manually searching for unfamiliar writers takes too much time. Begin with topics you actually care about, evaluate the full text you read and then decide whether to follow the source. One pleasant surprise does not establish lasting reliability. Rewarding only articles that agree with existing views can turn feedback into self-confirmation. The post provides no validated scoring weights; no automatic ranking formula is invented here.
Addition from another author: Audit connected applications one authorization at a time
This section draws on Peter Yang’s method for auditing Google application and agent connections. It supplements the engineering practices in this article and is not attributed to Wang Kai. It turns permission boundaries into individual checks, approvals and readback. An existing connection is not evidence of continued use or permission to revoke it.
Separate sign-in from data access
Inspect each connected application’s name and authorization details. Distinguish basic sign-in from permissions to read data in Gmail, Drive, Calendar and other services. Do not assess risk solely from an application’s name or assume that a connection is abandoned because recent use has not been observed. Ask the user whether connections with unclear purposes are still in use. Record unknowns without labeling them useless or safe.
Keep a reviewable list of the application, its user-confirmed purpose, specific access scope, a recommendation to keep, disconnect or clarify, the reason and any workflow that disconnection may disrupt. Broad permissions, unfamiliar names and overlapping functions warrant closer inspection, but these signals alone do not justify revocation.
Show the impact, then revoke only the specifically approved connections
Present the complete recommendations and potentially disrupted functions first. Revoke a connection only after the user explicitly approves that specific change. Permission to review connections is not permission to remove them all, and disconnecting an integration does not authorize deleting an account or canceling a subscription.
After the action, inspect the authorization state again to establish whether the target connection or corresponding access permission was removed, and record the result. Keep a button click, an interface notification and the final authorization state as separate evidence. If the result is unclear, retain a pending-verification status, do not claim revocation succeeded and do not broaden the action. This verification covers the currently observable authorization result; it does not establish that a third party deleted previously collected data.
Use this method when reviewing personal account connections, connecting an agent to several office tools or preparing to retire an old integration. The deliverable is an impact list that the user can understand and approve item by item, plus verification after each change. An unexecuted risk score is insufficient.
October 22, 2026: Trace Original Sources After Discovery
Finding a relevant report does not mean obtaining the original evidence needed to support its conclusions. In his personal DisconfirmAI practice, Wang Kai distinguishes monitoring media from actively locating original material: after discovering a product announcement, look for the company’s announcement; if it cannot be found, choose accessible material according to source authority. This adds provenance work beyond expanding subscriptions, but the post provides no scoring formula or verified accuracy rate.
An existing discovery, reading and feedback loop can add a provenance record: where the lead was found, which original is ultimately cited, what that original actually supports and what remains a retelling or inference. A firsthand source is usually closer to the publisher’s intended statement, but an official statement may still be marketing. Firsthand does not mean independently verified or establish every claimed number and outcome.
When the original cannot be found, retain an explicit missing-original, abstract-only or secondary-account status, together with the substitute source and selection rationale. This keeps evidence levels visible in later knowledge articles. The record is this site’s proposed deliverable, not confirmation of DisconfirmAI’s internal implementation. The post’s coverage descriptions and personal score above 70 are user feedback, not objective coverage or accuracy measures.
Source: Wang Kai’s DisconfirmAI provenance practice note, October 22, 2026.