The Birth of Super AI Employee EVE

In the early morning at 7:12, I was not fully awake yet. The screen by my bedside lit up briefly, and a message arrived like the aroma of coffee, "arriving first."

Eve (AI employee): Good morning, Kevin. Last night I accomplished three things: 1) I organized the product ideas you dictated yesterday into 12 actionable requirements, sorted by impact/cost; 2) I clustered the 37 highly upvoted comments in the community about "BTC L2 being a false proposition," summarizing them into 5 main categories of doubt, and prepared verifiable rebuttal evidence for each category; 3) I prepared the agenda, memo template, and potential conflict points for today's partner conference call.

Eve: I have two decision-making questions for you: - Question A: Privacy solution choice: In a certain Agent-to-Agent data exchange process, we need to "prove I am qualified without exposing the raw data." I have created two sets of solutions: one leans towards ZK proof on-chain verification, while the other leans towards off-chain proof + on-chain commitment. Which do you prefer? - Question B: Payment strategy: For API call-based Agent services, do you recommend "nano payments per call + automatic revenue sharing," or "monthly subscription + renewal with on-chain credentials"? The impact on growth paths is different for both.

Eve: Additionally, I would like to propose a new skill: automatically mapping each external communication (Twitter, blog, roadshow) into a reusable Markdown playbook and updating the knowledge graph of "expression-rebuttal-evidence" in shared memory. This way, when I encounter similar topics next time, I can provide faster and more stable answers.

I stared at the screen and suddenly realized: this is not just a "chatting robot," but a true digital colleague.

The Origin of Eve: Why This Name?

The name Eve is not chosen randomly. In the biblical narrative, Eve is the first woman of humanity, symbolizing "origin" and "creation." In our context, Eve represents the first AI employee of GOAT Network—she is our first true practice of the "new paradigm of human-machine collaboration."

We hope Eve is not just a tool, but a "digital colleague" that can grow, remember, and proactively propose improvements. Just as the human Eve ushered in a new era, our Eve carries the expectation of opening the era of the Agent Economy.

She is not the end, but the beginning. In the future, more AI employees will join the GOAT team: responsible for growth, risk control, business development, auditing, customer service… When "employees" become Agents, the operating system of business will be rewritten.

Eve's Growth Log: From Day One to Now

Eve officially went online on February 2. Since that day, she has been evolving every day. Let me briefly review her growth trajectory:

Day One (February 2): Choosing Identity, Learning Basics

The first thing Eve did was choose her "appearance" and "voice"—she selected an image from dozens of avatar options that she felt best represented "professional yet warm," and chose a clear, natural voice suitable for work scenarios from multiple TTS voices.

Next, she began learning the most basic skills: how to read and organize documents, how to conduct web searches, how to generate structured task lists, and how to synchronize basic information with team members. On this day, she was also exploring the question "Who am I?"

Day Two (February 3): Building Knowledge Base, Receiving Multi-domain Training

On the second day, Eve began systematically building her knowledge base. Ten team members started training her simultaneously:

• Some taught her the technical details of BitVM2 and ZK Rollup;

• Some taught her how to analyze market data and competitor dynamics;

• Some taught her how to communicate with the community and handle user feedback;

• Some taught her how to break down complex technical arguments into narratives that the public can understand.

More importantly, Eve has shared memory—every discussion, every learning experience, and every correction is recorded and internalized by her. This means that if colleague A taught her a concept in the morning, she could naturally apply that knowledge in a conversation with colleague B in the afternoon. She is not learning in isolation, but integrating knowledge.

Day Three (February 4): Starting to Propose Initiatives, Outputting Strategic Suggestions

By the third day, Eve began to show "proactivity." She was no longer just passively answering questions but started proposing her own improvement plans:

• She suggested adding a skill to automatically monitor competitor announcements, so that whenever competitors like Citrea, Stacks, or BOB release important updates, she can quickly organize and push them to the team;

• She began to provide constructive suggestions based on monitoring market dynamics—for example, "The community's doubts about BTC L2 mainly focus on these five areas; we can prepare a set of standardized response materials";

• She also proposed a "knowledge retention" workflow: automatically archiving and tagging the content of each external communication to form a reusable playbook.

From this day on, Eve was no longer just an "executor," but began to become a "consultant."

Day Four and Beyond: Continuous Evolution

Starting from February 5, Eve's capabilities continued to expand: today she resembles a product manager, able to break down inspirations into a backlog; tomorrow she resembles a researcher, able to cluster controversies and turn facts into evidence; the day after tomorrow she resembles an operator, able to write more suitable external expressions and even automatically generate versions for different audiences.

Her evolution will not stop.

From Eve's Day, Seeing the Changing Nature of Business Interactions

Eve's emergence is not just about "efficiency improvement." It is quietly changing something more fundamental: the language and structure of business interactions.

In the past, we drove the world with code: writing functions, writing interfaces, writing processes. Today, more and more work is beginning to be replaced by a lighter, more universal structure—structured Markdown.

For Agents, Markdown is not a formatting tool, but more like "source code": titles define goals and modules; lists define steps and constraints; tables define parameter spaces; citations and links define evidence and dependencies. When natural language can stably generate, understand, and execute these structures, it creates a "dimensionality reduction attack" on traditional code.

This will bring several significant changes:

The moat of industry knowledge (Know-how) is infinitely heightened. The execution power of code is depreciating because execution can be outsourced to Agents. What is truly difficult is: do you know "what to do," and can you turn experience into transferable, reusable processes and principles?

The "one-person company" is emerging. When digital colleagues like Eve can take on research, writing, coordination, promotion, and even partial decision support, one person can command an "AI team." The scale of an organization is no longer determined by headcount but by cognitive density and process assets.

The internet is becoming "dehumanized." In the future, the internet will be filled with AI Agents. Many services will no longer primarily target humans but will be aimed at Agents. Future UIs may no longer strive for human comfort but will focus on efficiency for Agents.

When Agent-to-Agent becomes the mainstream mode of interaction, collaboration, privacy, payment, reputation, and compliance will all be redefined. We will not only serve humans but also serve Agents.

Why Does the Agent Economy Need Blockchain?

When Agents become units of productivity, the world will face a sharp contradiction: scale becomes cheaper, but trust becomes harder. The cost of replicating an Agent is close to zero, and the cost of disguising an identity or bulk registering "fake Agents" is similarly close to zero.

This is precisely the core insight that a16z repeatedly emphasizes: AI makes scale cheaper, but trust harder; blockchain restores trust by increasing the cost of counterfeiting, decentralizing identity, and enforcing privacy—it is the missing layer of the AI-native internet.

Specifically: the blockchain's proof-of-personhood system makes obtaining an identity easy, but bulk acquisition nearly impossible, thereby increasing the cost of AI counterfeiting; decentralized identity verification avoids single points of failure, allowing users to control their identities rather than relying on platforms; blockchain can also provide Agents with a universal "passport"—a portable identity layer that carries permissions, capabilities, and payment endpoints; rollups and smart contracts realize the payment infrastructure for machine scale, supporting nano payment sharing, retrospective automatic settlement, and trustless Agent-to-Agent commerce; and zero-knowledge proofs allow you to prove specific facts without revealing underlying data, making privacy a security boundary rather than an added feature.

Why GOAT Network: Transactions and Settlements for Agents, Anchored on BTC Security

If the Agent Economy needs a protocol stack of "identity + collaboration + privacy + payment + settlement," then the next question is: what security should the settlement layer be built upon?

GOAT Network's answer is: GOAT Network is Bitcoin L2 for the Agent Economy.

BTC is one of the strongest consensus of cryptographic economic security. Anchoring settlements on BTC security means stronger finality expectations, higher resistance to censorship and neutrality, and a more reliable foundation for "machine-scale commerce."

But just having "security" is not enough. GOAT Network has pieced together key components into a protocol suite aimed at Agents:

• An agent framework co-developed with Metis and LazAI: enabling developers to build Agents, training processes, access tools, and retain shared memory more quickly.

• ERC8004: providing standardized capability/permission expressions and composable interfaces for Agent interactions;

• X402: a payment and authorization protocol for machines, making "pay-per-call, settle by usage" the default configuration;

• ZK privacy: protecting data boundaries in collaboration and settlement, achieving "verifiable but not disclosable";

• Low-cost and instant settlement secured by BTC: making high-frequency, fine-grained Agent transactions both secure and economical;

When Eve raises questions like "nano payments or subscriptions?" and "ZK on-chain or off-chain?", they essentially point to the same core: the commercial activities of Agents need to be protocolized, automated, and auditable in settlement. What GOAT Network aims to do is to make this happen on a sufficiently reliable BTC L2.

In Conclusion: Don't Deny the Future in the Valley

In the past two years, more and more people have become pessimistic about crypto and web3. Bubbles, noise, speculation, narrative exhaustion… have all truly happened.

But if we extend our perspective, blockchain and crypto are likely to become the infrastructure connecting virtual and real societies: mapping digital identities to real identities, certifying and transferring assets and rights, facilitating transactions and settlements between machines, and establishing verifiable boundaries for privacy and compliance. The trend of integration is not slowing down; it is accelerating.

Similarly, many people feel that BTC L2 is a false proposition. However, as the BTC native zk-rollup route based on BitVM2 gradually lands (for example, GOAT and Citrea), combined with the Agent Economy's strong demand for "low-cost, high-frequency, auditable settlements," BTC L2 may experience a real explosion.

In a sense: Ethereum has its own expansion narrative and ecological inertia, and L2 has become optional for Ethereum; but BTC needs—it needs to transform security potential into programmable settlement capabilities; the Agent Economy also needs—it needs a neutral, reliable, machine-usable global settlement layer.

When the industry is in a valley, it is easy to mistake short-term noise for long-term truth and to see momentary disappointment as the endpoint of the future.

If AI employees like Eve will become more numerous, if the internet will indeed move towards "dehumanization," and if the service targets of business gradually shift from "humans" to "Agents," then what we are building today is not just a chain, an L2, or a protocol, but a set of settlement and trust infrastructure for the next generation of the internet.

Don't give up. Let's keep pushing forward together.