The Evolution of AI Agents: Building Your Own Super Agent and Shifting from Executor to Orchestrator

In an era where Artificial Intelligence is rewriting the rules of productivity, the pressing question is no longer “What can AI do?” but rather “How do I transform AI into a super agent that acts as my proxy, shifting my role from an executor to an orchestrator?” This comprehensive guide dissects the logic behind Agent evolution, the critical importance of Context, and how to build personal workflows using Skills to secure a competitive edge in the age of information asymmetry.

The Essence of Agents: Beyond Chat to Tool Use

Core Question: What is the fundamental difference between a Chatbot and an Agent?

The answer lies in the ability to use tools. Just as the distinction between humans and animals is defined by our ability to utilize tools, the leap from a standard Chat interface to an Agent is defined by the capacity to actively invoke tools to complete tasks. This is not merely a functional addition; it is a fundamental shift in underlying logic.

The Formula for a Super Agent

A “Super Agent” capable of handling complex, real-world tasks can be defined by a simple yet powerful formula:

Super Agent = Base Model + Agent Framework + Context

In this equation, the Base Model (e.g., GPT, Claude) determines the intellectual ceiling, and the Agent Framework provides the skeleton for execution. However, the component that truly determines whether an Agent “understands you” and can execute tasks precisely is Context.

Why Context is the True Core

All engineering measures are essentially methods to construct context for the model. Whether it is writing Prompts, setting Rules, configuring MCP (Model Context Protocol), or defining Skills, the ultimate goal is to enable the model to understand your intent, background, and constraints within a limited timeframe.

As users, we cannot modify the base models produced by Anthropic or OpenAI. The only controllable variable—and our true competitive moat—is the continuous refinement of our own Context.

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Author’s Insight:
In the early stages of building an Agent, it is easy to become obsessed with finding the “perfect model,” believing that a smarter model solves everything. However, practical application pain points usually stem from the model not knowing “who you are,” “what you want,” or “what the standards are.” Investing time in building a high-quality context library offers a far higher return on investment than waiting for the next model release. Context is your digital asset; it is more personal and non-replicable than the model itself.

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Tooling and Model Selection: Finding the Optimal Solution

Core Question: Faced with a myriad of models and tools on the market, how do we make the most efficient choice?

Selection criteria should be based on “Task Attributes.” Blindly pursuing an “all-in-one” solution often sacrifices efficiency.

Openclaw vs. Claude Code

In the current Agent ecosystem, Openclaw and Claude Code represent two distinct design philosophies:

Expert Recommendation:
In terms of Agent capability, Claude Code currently outperforms Openclaw. For technicians pursuing extreme efficiency, the recommended models are Opus 4.6 and GPT 5.4. Their stability in logical reasoning and code generation forms the bedrock for building Super Agents today.

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The Memory Paradox and Context Management

Core Question: Does an Agent need long-term memory? How do we handle massive historical data?

Intuition suggests that more memory equals a smarter Agent. However, in professional task execution, the opposite is often true.

Why “No Memory” Can Be Better

When handling professional tasks, having no memory can actually yield better results. This sounds counter-intuitive, but the logic lies in the limitations of the model’s context window.

If too much irrelevant “memory” is loaded, it crowds out the reasoning space required for the core task. This causes the model’s attention to scatter, leading to a degradation in output quality—a phenomenon often referred to as “lost in the middle.”

Code as Memory: The TikTok Handover Case

So, how do we retrieve necessary context without cluttering the window? A highly instructive case study involves a server handover:

• The Scenario: A TikTok Southeast Asia server handover required processing documentation exceeding 10MB.
• The Problem: No current model can一次性 ingest such a massive context without significant quality loss or crashing.
• The Solution: “Code is Memory.”

Instead of trying to shove a 10MB document into the Prompt, the solution leverages the inherent structure of the code repository. A codebase is not just a collection of characters; it embodies business logic, architectural design, and historical evolution. By enabling the AI to index the codebase and understand structure and dependencies, it can precisely locate relevant logic when needed, rather than searching for a needle in a haystack of documentation.

Operational Takeaway:
For engineers, your code repository is the best knowledge base. Do not try to convert all documentation into plain text for the AI. Instead, establish a mechanism where the AI can “consult” the code as it would a memory. This avoids context length limits while ensuring information accuracy and timeliness.

The Skills System: Building Reusable Capability Atoms

Core Question: How do we precipitate an Agent’s capabilities to achieve long-term compound interest?

Skills are the atomic units of an Agent’s ability to execute specific tasks. A well-designed Skill allows you to achieve “one-click execution” when facing repetitive work.

Case Study: The AI Writing Workflow

The operation of a specific公众号 was cited as a prime example. All articles are generated by AI, yet they possess a unique human touch. The secret lies in a complete Writing Skill Workflow:

1. Style Feeding: The AI is fed the author’s past blog posts to deeply learn their unique writing style.
2. Fragment Recording: Using open-source tools (like Memos), the author records 10-20 sentences of fragmented inspiration daily.
3. Workflow Execution: The AI reads these fragments, combines them with a preset Writing Skill, and executes a complete workflow to output an article draft and generate images.

The essence of this process is that the human is only responsible for “inspiration input” and “final review.” The tedious intermediate steps of polishing, formatting, and image generation are automated by the Skill.

Eat Skill vs. Shit Skill: The Metabolism of Capability

To maintain an efficient Agent workspace, we can borrow from biological metabolism mechanisms and establish two core types of Skills:

• Eat Skill (Absorption): Used to absorb and precipitate knowledge. For example, reading a long document to extract a summary, or learning a new API document to store it in a knowledge base.
• Shit Skill (Excretion/Cleanup): Used for the metabolism and cleanup of the workspace. As an Agent runs, its context becomes bloated. The Shit Skill clears useless caches and compresses expired information, ensuring the Agent’s “brain” remains clear.

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Author’s Insight:
This anthropomorphic Skill design solves the problem of “entropy increase” in AI systems. Many Agent projects start strong but become increasingly “dumb” over time because they lack a “metabolic mechanism.” Regularly cleaning and reorganizing the workspace is a key operational task to maintain the Agent’s long-term viability. As an “orchestrator,” you need to care about your Agent’s context health just as you care about your own physical health.

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Mindset Leap: From Executor to Orchestrator

Core Question: In the Agent era, where will human core competitiveness shift?

AI won’t replace humans, but “people who use AI” will replace “people who don’t.” The future work mode will undergo a fundamental reversal: Previously, we were the workers; in the future, our role is the “Boss.”

How to Be a Good “Boss” to Your AI?

You can observe your current managers or mimic the principles of high-level executives. The core of leadership lies in “setting goals, providing resources, and reviewing results,” not in “writing PPTs, typing code, or making reports.”

• Set Goals: Clearly define task boundaries.
• Provide Resources: Offer necessary context and tool permissions.
• Review Results: Audit output and provide feedback for correction.

Mindset Shift Checklist:

1. AI First Principle: Before doing anything, ask: “Can an Agent do this?”
2. Capability Boundary: My capability boundary = The sum of the Agent’s capability pool.
3. Compound Interest: Today, I package a workflow into a Skill; tomorrow, I solve the same problem in seconds. This is a long-term compound interest investment.

Platonic Questioning: Exploring Unknown Fields

When facing a completely unfamiliar field, humans are often “ignorant.” The most effective way to use AI is not through direct commands, but through Platonic Questioning.

• Wrong Approach: “Write a blockchain quantitative strategy for me.” (You lack the details to provide context.)
• Right Approach: “I am entering the blockchain field but know nothing about it. What core concepts do I need to understand? Please list a learning path.”

Let the AI tell you “what you need to know” first, then search for knowledge. This “reverse” learning method allows you to quickly build a cognitive framework in陌生 fields. Remember, in this era, the strongest teacher within your cognitive range is a top-tier model like Opus 4.6 or GPT 5.4.

Real-World Case: HappyClaw and the Singularity of Automation

Core Question: To what extent can an ordinary person without coding skills leverage an Agent?

The HappyClaw project serves as a highly persuasive case. This is a complex system with nearly 100,000 lines of code.

The Shocking Fact: The project’s core builder stated they had not written a single line of TypeScript code and hadn’t even read the frontend code in detail. They were only responsible for architectural design and Review. All code implementation was completed by the Agent.

Application Scenarios Demonstrated:

1. McDonald’s Ordering Automation: Simulating user operations to automatically complete complex ordering flows.
2. Ctrip Tour Comparison: Automatically crawling information on multiple tour groups and generating comparison tables to aid decision-making.
3. Blockchain Account Analysis: Inputting an address to automatically track on-chain transactions and analyze fund flows.

The realization of these functions no longer depends on the developer mastering a specific language, but on the ability to describe the process. As long as you can decompose the process into executable steps (Skills), the Agent can turn it into reality.

Technical Detail:
The infrastructure of Skills lies in browser automation. Anything that can be operated in a browser can theoretically be encapsulated into a Skill. This breakthrough at the “visual interaction” level allows Agents to transcend API limitations and possess broader operational space.

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An Investment Perspective: Leveraging 10x Capability for $200

Core Question: Is buying an expensive AI subscription a consumption or an investment?

Many hesitate at a $200/month subscription fee for tools like Claude Max. However, from another angle, this is an investment with an extremely high risk-reward ratio.

Investment Logic Analysis:

• Leverage Target: You are longing your own capabilities.
• Limited Downside: The worst-case scenario is losing the subscription fee; your inherent capability does not shrink.
• Unlimited Upside: This is an “asymmetric bet.” If the AI helps you conquer a technical hurdle or optimize a workflow, the returns can be exponential.
• Time Cost: For a professional engineer, one day’s salary often exceeds the monthly fee of a top-tier plan. Trading money for the most efficient tools is the most cost-effective time arbitrage.

The Information Gap of 2026:
There is a massive information gap in the AI field. Those who adopt the strongest models first and learn to use them deeply are eating the Alpha (excess returns). Latecomers only get the Beta (average returns), and some miss even that because they rely on tools that only provide average-level intelligence.

Gary’s Insights: Domain Differences in AI Acceptance

Core Question: Is the impact of AI the same across different industries?

Gary, the creator of Feishu (Lark), outlined an interesting Acceptance Pyramid in his share:

1. Programming (Highest Acceptance): Programmers are most eager to embrace AI, viewing it as a productivity multiplier.
2. Writing: Assisted writing is becoming normal; acceptance is high.
3. Painting/Art: There is noticeable resistance. Professional artists have high requirements for detail and artistic intent. Current AI painting still has flaws in high-quality delivery, often triggering criticism of the “AI look.”
4. Gaming (Lowest Acceptance): Most serious gamers would never want AI to play the game for them. The essence of gaming is the experience; AI playing deprives the process of meaning.

The Irreplaceability of Human Experience:
In the modern stage, human experience remains a core asset. Whether writing articles or building products, what you need is not just the result, but the ability to materialize “experience” into text and convey it to the AI. This perception is something AI cannot simulate.

Practical Summary & Action Checklist

To help you implement these concepts quickly, here is a distilled guide:

1. Configure Your Super Agent

• Tool Choice: Prioritize Claude Code or Openclaw.
• Model Choice: Always use the SOTA (State-of-the-Art) models (e.g., Opus 4.6 / GPT 5.4).
• Cost Mindset: Do not skimp on subscription fees; view them as an investment in your own capabilities.

2. Steps to Build Skills

• Define Process: Decompose business or life processes into Step 1-2-3. Ensure every step is “executable” (Don’t write “Make $1M”; write “Execute Strategy X”).
• Write Skill.MD: Use a template (Name + Description + Guide). The best reference is the official open-source skills-creator repository.
• Loading Strategy: Skills should only load name and description when not in use to avoid occupying Context.

3. Daily Workflow Restructuring

• Input: Cultivate the habit of recording fragments (e.g., using Memos) as raw material for AI writing.
• Execution: When encountering repetitive tasks, first think “Can this be encapsulated into a Skill?”, then execute.
• Output: Periodically execute a “Shit Skill” to clean the workspace and keep Context clean.

4. Learning & Growth

• Info Source: Scroll X (Twitter) for 4 hours daily. Frontier AI information often ferments here first.
• Learning Method: In new fields, ask the AI “What questions should I ask?” first to build a cognitive framework.
• Mindset: Shift from “How do I write code” to “How do I describe requirements clearly.”

One-Page Summary

• Core Formula: Agent = Model + Framework + Context. Context is the user’s moat.
• Selection Advice: Claude Code + Opus 4.6 is the current optimal solution.
• Memory Management: Code is memory. Avoid lengthy docs; use structured codebases.
• Capability Precipitation: Precipitate workflows through Skills. Achieve a closed loop of Eat (Absorption) and Shit (Metabolism).
• Mindset Shift: Change from an executor to an orchestrator. Treat yourself as the Agent’s boss.
• ROI: Buying a top-tier AI account is a 10x long position on your own capability.

Frequently Asked Questions (FAQ)

Q1: Why do you recommend using the most expensive models? Can’t cheaper models work?
A: We recommend SOTA (State-of-the-Art) models because your time is more valuable. Cheaper models often struggle with complex logic and long contexts, leading to errors and repeated debugging. The time wasted costs far more than the subscription difference.

Q2: I don’t know code. Can I use Claude Code?
A: Yes. As the HappyClaw case shows, you don’t need to write code; you need to clearly describe processes and logic. Claude Code is responsible for translating your logic into code.

Q3: What is “Context Overflow” and how to avoid it?
A: Context Overflow happens when you feed the AI too much information at once (e.g., a 10MB doc), causing the model to become “dumb” or forget instructions. Avoid this by using the “Code is Memory” principle or loading information on-demand via Skills, rather than full-text ingestion.

Q4: What is the difference between Skills and standard Prompts?
A: A Prompt is usually a one-time instruction. A Skill is a reusable, standardized workflow encapsulation. Skills are like toolboxes you can call anytime, possessing specific context environments, making them more stable and powerful than bare Prompts.

Q5: How do I start building my first Skill?
A: Find a repetitive task you do often (e.g., summarizing articles). Find a Skill template, fill in the Name and Description, write down the specific steps (e.g., 1. Read, 2. Extract keywords, 3. Output Markdown), and test it with your Agent.

Q6: How should I act as a “Boss” to the AI?
A: The core is “Defining Goals” and “Reviewing Results.” Do not intervene in execution details. You need to learn to set clear backgrounds, goals, and constraints for the AI, just like assigning tasks to a subordinate, then trust its output and review it.

Q7: Why is “scrolling Twitter” considered important for learning AI?
A: AI technology iterates extremely fast. Academic papers and news sites often lag. X (Twitter) is where AI experts and the open-source community are most active; the newest concepts (like Vibe Coding) and tools are often released here first.

Q8: Will AI make me lose my job?
A: AI won’t directly make you redundant, but “people who use AI” will replace “people who only execute manually.” Future core competitiveness lies in “Judgment” and “Orchestration,” not mere execution. Establishing your own AI workflow early is the best strategy for the future.