Enterprise Software in the Agent-First Era: From SOP to Skill

A Signal That Shouldn’t Be Ignored

In March 2026, WeChat made a far-reaching decision: opening the ClawBot plugin interface.

This isn’t launching a “WeChat version of ChatGPT”—it’s opening a standard interface that allows any Agent to connect to the chat interface of 1.4 billion users.

The official announcement states:

“Today, we launched WeChat ClawBot plugin capability: after connecting, you can chat via WeChat to connect to your own ClawBot.”

The key phrase is “your own ClawBot”. This means:

Not only Tencent’s Agents can use WeChat
Not only paying users can enjoy this
Any Agent framework that meets the interface specification can access the chat entry point of 1.4 billion users

At the same time, WeChat Work (Enterprise WeChat) also opened up access capabilities—QClaw, Workbuddy, and Tencent Cloud Agents can all be connected with one click.

The signal couldn’t be clearer: Agents are no longer toys for the tech circle—they are becoming infrastructure.

Not Just WeChat

This trend extends beyond WeChat. Looking globally, major IM platforms are doing the same thing:

Slack: AI integration has become standard—Claude and ChatGPT both have official Slack Apps
Discord: Opened a complete Bot API; numerous AI services operate through Discord
Feishu (Lark): Launched an AI assistant open platform
DingTalk: Integrated Tongyi Qianwen and other AI capabilities

When one super platform after another chooses to open Agent interfaces, the message is clear:

Agents are becoming “utilities”—just as opening the Official Account interface spawned an entire content ecosystem, opening Agent interfaces will spawn an entire Agent ecosystem.

Questions Enterprises Should Ask

When Agents become infrastructure, enterprises need to seriously consider:

Is our software ready to be called by Agents?
Do our internal systems have Agent-friendly interfaces?
Will our employees use Agents to operate admin dashboards?

This isn’t a question of “if it will happen”—it’s a question of “when it will happen.”

Agent-First: An Overlooked Paradigm Shift

Traditional enterprise software development logic:

Business Requirements → Product Design → UI/UX → Backend API → Training Docs

Agent-First logic:

Business Requirements → Skill Code → Agent Calls → Natural Language Interaction

Key difference: UI goes from “necessity” to “optional.”

When an Agent can understand “restart all nginx services in production environment” and execute it, do we still need a complex admin dashboard?

Insights from Junior.so

Junior.so recently introduced an interesting concept: AI Employee.

Unlike task-based Agents like Devin or Manus, Junior emphasizes being “a member of the team”:

1. Org Memory

Junior remembers context from three months ago, decisions made in side channels, commitments others forgot.

This is precisely the shortcoming of current Agents. Most Agents are stateless—every conversation is a “first meeting.” But real employees remember:

Decisions from the pricing meeting three months ago
Special discount approvals for certain clients
Why technical solution A was chosen over B

2. Self-Driven

Junior doesn’t wait for a prompt. They monitor what’s happening, identify what matters, and surface it.

This requires the Agent to:

Connect to various enterprise data sources
Understand what’s “important”
Take initiative at the right moments

3. Real Identity

Has its own email, Slack, and name.

This isn’t a gimmick. When an Agent has its own identity, it can:

Communicate independently with external parties
Be incorporated into the organizational structure
Assume clear responsibilities

Real Challenges from Community Discussions

Recently, the Agents Special Zone community held a sharing session on “SOP Transformation Skill Opportunities and Challenges in Cross-border E-commerce,” featuring Axton Wang (Wang Shuaihui) as the guest. Several insights from the post-discussion are worth pondering:

axtonwang: The delivery logic in the AI era has changed. Much of the work is done by AI, including Skills. Business correctness comes from business experts, but steps + instructions are written by AI.

This points to a key insight: The knowledge source for Skills is business experts, but the expression form is code.

Chen Hao: The methodology of this work—the methodology of implementation and delivery—is the enterprise’s competitive advantage. Industry experts are important. Clients now want more than just having their ideas implemented—they want the vendor to bring even better industry solutions to guide them. The latter commands a premium.

This reveals the commercial essence of Agent implementation:

Methodology is the moat: Whoever can efficiently transform industry knowledge into Skills wins
Industry experts + AI is the best combination: Pure tech teams can’t do it well; pure business teams can’t do it at all
Premium comes from “bringing solutions”: Not helping clients implement their ideas, but using AI to amplify your industry insights

Stone: The client’s processes, their SOPs—don’t change them. Keep them as they are. Just replace what humans used to do with Agents.

This is a pragmatic path. Not overthrowing everything, but incremental replacement:

Traditional flow: Human → SOP Document → Human Execution
Agent flow: Human → Skill Code → Agent Execution

Skill Design: Behavior Boundaries Must Be Codified

In Agent-First transformation, the biggest pitfall is trying to constrain Agent behavior with natural language.

Wrong Approach

You are a data analysis assistant, you should:
1. Only read CSV files
2. Do not modify original data
3. Output format as Markdown table

Problem: LLMs forget, misunderstand, and overstep boundaries.

Correct Approach: Enterprise Skills Must Be Codified

class DataAnalyzerSkill(Skill):
    def __init__(self):
        self.guard = PathGuard(allowed_dirs=["~/data"])
        self.allowed_extensions = [".csv", ".json"]

    async def execute(self, file_path: str, **kwargs) -> str:
        # Code-level enforcement, LLM cannot bypass
        self.guard.validate(file_path)
        if not any(file_path.endswith(ext) for ext in self.allowed_extensions):
            return "Error: Only CSV and JSON files are supported"
        # Force read-only mode
        return self._read_only_analyze(file_path)

Core Principles:

Behavior boundaries defined in code, not in prompts
Constraints are testable and auditable
LLMs cannot bypass code-level checks

Security: The First Hurdle of Agent Transformation

Giving Agents permissions is like giving new employees permissions—there must be boundaries.

Traditional Security Model vs Agent Security Model

Dimension	Traditional Software	Agent
Verification Subject	User identity	Agent identity + User intent
Attack Surface	Known API endpoints	Arbitrary natural language input
Traceability Difficulty	Operation logs	Multi-turn dialogue + tool call chains

Insight for enterprise transformation: Before connecting Agents, ask yourself—if an Agent is induced to execute a dangerous operation, is there a circuit breaker?

Commercialization: Skills as a New Delivery Format

In the Agent-First era, the delivery format of enterprise software will change:

Dimension	Traditional SaaS	Agent-First
Deliverable	Web application	Skill code
Development cycle	3-6 months	1-2 weeks
Training cost	Ongoing investment	Near zero
Interaction method	Clicking UI	Natural language
Reusability	Low	High (code is reusable)

Commercial Value:

Development cost reduced by 70% (no need to develop complex UI)
Delivery cycle shortened by 80% (from months to weeks)
Training cost near zero (natural language interaction)
Maintenance cost reduced by 60% (code is testable, version-controllable)

New Business Models

axtonwang: If anyone figures out computer use automation, come find me—we’ll do something bigger. Enterprise-level.

This hints at a trend: Computer Use is the next battleground for enterprise-level Agents.

Three entry points are converging:

API entry: Structured data exchange
Browser entry: Simulating human operations
Desktop software entry: Directly operating the system

Whoever can connect these three entry points will truly achieve “Agent replacing human labor.”

Enterprise Transformation Path Recommendations

Short-term (1-3 months)

Audit existing systems for Agent compatibility
- Are APIs Agent-friendly?
- Is there a command-line interface?
- Are logs structured?
Establish security boundaries
- Sensitive operations require secondary confirmation
- Critical data requires access auditing
- Dangerous commands require circuit breakers

Mid-term (3-6 months)

Select pilot scenarios
- Start with high-frequency, low-risk operations
- Examples: log queries, report generation, status checks
Develop the first batch of Skills
- Define behavior boundaries in code
- Write unit tests to verify constraints
- Internal grayscale validation
Accumulate industry methodology
- Document the SOP-to-Skill transformation process
- Form reusable templates
- This is your core competitive advantage

Long-term (6-12 months)

Redesign the interaction layer
- Admin dashboards become CLI-based
- UI becomes the “display” for Agents, not the operation entry point
Build an Agent ecosystem
- Internal Skill marketplace
- Cross-department Agent collaboration
- Agent-to-Agent communication

Conclusion

Agent-First is not a technology upgrade—it’s a fundamental shift in interaction paradigm.

The trends I see from community discussions:

Knowledge comes from business experts, but expression must be code
Methodology is the enterprise’s moat
Incremental replacement is more pragmatic than overthrowing everything

When WeChat opens Agent interfaces, when AI Employees like Junior start entering the workforce, the signal is clear:

It’s not a question of whether to transform, but how fast you transform.

The sooner you start converting SOPs to Skills, the sooner you accumulate industry methodology, the sooner you establish your own competitive barrier.