Original · Unique Research · 2026-07-15
Editor's note: The interview and its judgments belong to the original Chinese author and the interviewee, Sophie Yang, co-founder and CEO of Eureka.AI. This English rendition translates the full article in source order, including the opening essay, four analysis sections, all twenty Q&A items, and the respondent biography. All company and person names are preserved as source attributions. Product claims and predictions are speaker claims, not independently verified findings.
AI Industry Observation
AI FDE Takes the Stage: Eureka.AI Turns a Boss's One-Sentence Brief Into a Running Agent
"Clients perceive AI value not because they understand the underlying architecture, but because they've received a genuinely usable business result."
It can be a report, a proposal, a PPT, a spreadsheet, or a repeatable workflow.
Someone used AI software to finish a sales proposal for a client in ten-plus minutes; someone else spent three weeks still tweaking prompts in a chat box. This really isn't a technology gap — it's a working-method gap.
On social media, Agent videos showing flight bookings in seconds or ten-thousand-word novels in minutes are everywhere.
But walk into a real office, and what you see is that many bosses and employees still need to shuttle materials, fill in context, and revise results between different AI tools and business software.
There's a gap in between.
When Eureka.AI founder Sophie Yang (杨婷) talked about this, she said clients perceive AI value not because they understand the underlying architecture, but because they've received a genuinely usable business result.
"Stop talking about universal employees — first help the boss produce a proposal."
As an explorer of Agent Harness architecture, Eureka.AI tries to enter one step earlier: first use AI FDE to turn business requirements into executable tasks, then hand them to S.Harness to organize and run.
Is AI FDE a False Proposition? From a Boss's One Sentence to a Runable Scenario
Bosses actually care about AI and have some budget, but they're stuck at an extremely awkward stage.
They know AI is useful, but don't know which scenario in their business is most worth doing first, what materials to prepare, or what results to expect.
Throwing a pile of blank input boxes at them is equivalent to throwing the hardest requirement decomposition back at the user — it won't work.
Sophie told me that Eureka.AI is building AI FDE as a product entry point for SMBs, specifically handling business clarification, scenario judgment, and task definition.
This sounds a bit like pre-sales, and some question: if the boss can't even articulate the pain point clearly, how can AI diagnose it — isn't this a false proposition?
Actually, AI FDE doesn't try to replace complex strategic consulting. It does one step closer to execution: based on business goals, existing materials, and expected outcomes, it reverse-defines a runnable task structure.
The boss only needs to answer the most specific things, like what's your biggest headache finding clients right now, and what materials do you already have.
One sentence is just the starting point; AI FDE continues to clarify business goals, input materials, execution steps, deliverable format, acceptance criteria, and human confirmation points, until the task is ready to run.
Reverse-design the intermediate workflow from the final document you want and the materials you have; if you don't even have materials, don't force AI.
S.Harness's Fight for Survival: Between Large Models and Vertical SaaS, Where Does Value Lie?
Eureka.AI named its Agent Harness engineering engine S.Harness. It organizes runtime, context management, tool calling, human confirmation, permission governance, quality evaluation, and Artifact delivery together.
But there's a sharp challenge here: large model vendors are giving away orchestration tools for free, vertical SaaS holds the core data — why can Eureka.AI survive squeezed in the middle?
Large model vendors mainly provide base intelligence and general tools, but when clients put Agents into specific business, they still need to define material boundaries, confirmation processes, evaluation standards, and responsibility control points themselves.
Vertical SaaS is usually better at professional processes within its own system, but real knowledge work often requires cross-software, cross-material, and cross-role collaboration.
Real work crosses software boundaries — writing a competitive analysis means you go to the CRM for customer data, browse news in the browser, then compute a spreadsheet in Excel.
No single vertical software can compile all the context.
The real defense is the work assets clients deposit here.
The company's unique deliverable format, the evaluation set for judging good from bad, the legal review red lines — these are digital standard workflows.
"Once these rules run smoothly in S.Harness, the underlying model can be continuously upgraded and switched, but the processes, evaluation standards, and work assets clients have deposited don't disappear."
SMB or Professional Team? How AI FDE and Skills Interlock Both Ends
On the business path, many feel SMB demand is too scattered and AOV too low, while professional teams like lawyers and auditors have too high a threshold.
Eureka.AI is touching both sides, which sounds like self-consumption.
I thought about it, and the logic behind is actually a two-sided interlock.
SMB high-frequency use continuously generates real demand, revision feedback, and evaluation results — an important source for validating scenario Agents and Skills.
They collide with all kinds of needs on the front lines every day; which skills truly work and which templates are efficient get filtered out in massive collisions.
Once these skills mature, they converge into industry scenario packs.
Teams with higher requirements for permissions, data boundaries, auditing, and professional standards will form clear demand for commercial versions, industry Skills, and deployment capabilities.
"SMB brings high-frequency scenarios and real feedback; professional teams push governance, industry capabilities, and commercial versions deeper; the two paths jointly deposit Skills and work assets."
Next 12 Months: From Demo Hype to Artifact Reliability
Sophie predicts that a year from now, many AI products that can only do one cool demo but can't be stably reused will struggle to retain ongoing client usage and payment.
The industry watch in the coming year is product reliability.
From demo capability to production-level responsibility — this is a hard threshold.
The north-star metric isn't one-time call volume, but how many high-frequency tasks are stably completed, how many Artifacts are recognized by clients, and how many processes are deposited as reusable Skills.
She shared a seven-step checklist you can use right away to test whether AI can actually do work:
Step one, pick a high-frequency task you do every week.
Step two, take 5 to 10 historical real materials as input.
Step three, lock the final document format.
Step four, write clear quality standards and red lines.
Step five, block key confirmation points — don't let AI make unilateral decisions.
Step six, run it three times in a row and record issues.
Step seven, deposit reliable steps as templates.
"If after three runs human modifications keep decreasing, this work has truly landed."
Selected Interview Q&A
1. Many SMB bosses know AI might be useful but don't know where to start. How does Eureka.AI help them find the first worthwhile business scenario?
Sophie: Many SMB bosses aren't uninterested in AI or lacking budget, but they're stuck at an earlier stage: they don't know which scenario in their business is most worth doing first, what materials to prepare, what results to achieve, and how to judge whether AI did it well.
Eureka.AI is building AI FDE as the core product entry for SMBs. It breaks down vague business requirements into specific scenarios, input materials, execution steps, Artifacts, and acceptance standards.
For example, if a client says "I want to use AI to improve sales efficiency," AI FDE continues to clarify: do you want customer lead research, sales scripts, or pre-visit briefings? What materials do you have? Do you want a spreadsheet, report, PPT, or a reusable workflow?
This process is essentially translating "AI ideas" into "AI-executable workflows." What SMBs truly lack isn't more complex AI concepts, but a low-barrier, professional, timely entry point that guides them to take the first step.
2. AI FDE sounds like a pre-sales consultant — what's the biggest difference from traditional pre-sales or consulting?
Sophie: Traditional pre-sales or consulting relies on human experience, with high service costs, making it hard to cover scattered SMB clients at scale.
AI FDE's final result isn't a recommendation that stays on paper, but a task plan that directly connects scenario Agents, Skills, and S.Harness.
AI FDE's value is productizing and Agent-ifying high-frequency requirement clarification, scenario diagnosis, material preparation, proposal recommendations, and deliverable definition. It doesn't replace all human consultants, but first takes clients from "completely not knowing how to start" to "ready to run the first real task."
Traditional pre-sales usually asks: "Do you want to buy this product?"
AI FDE should ask: "What's your business goal? Where are the materials? What Artifact do you expect AI to deliver? What result counts as valuable to you? Which steps need human confirmation? What information can't be touched?"
We don't want clients to use AI just for AI's sake; we want them to start from a real, high-frequency, verifiable task. Only then can AI enter the business process instead of staying as a one-time capability display.
3. SMB AOV isn't high, but demand is very scattered. How does Eureka.AI balance standardized products and personalized needs?
Sophie: If everything is standardized, clients feel "it doesn't fit my business"; if everything is project-based customization, service costs are very high and hard to scale.
Eureka.AI's approach isn't choosing between standardization and customization, but first covering high-frequency scenarios with standardized capabilities, then adapting client differences through industry-specific, scenario-specific, and lightweight configuration.
AI FDE plays the role of a "requirement translation layer." Clients say "I want AI to help with operations" or "I want to improve customer conversion"; AI FDE translates this business language into executable task structures: which scenario, which inputs needed, which Agents or Skills to call, what Artifact to generate, and how the client accepts it.
This way, Eureka.AI doesn't need to develop from scratch for each client, nor force clients into completely fixed templates. Standardized Agents and Skills serve as the starting point, then combine the client's industry, materials, terminology, output format, and feedback to form a dedicated workflow.
If a process has reuse value, it can be further deposited as a new Skill, template, or industry scenario pack, gradually converting client demand into platform capability.
Client usage feedback, human revisions, and evaluation results continue to deposit as configurations, Skills, and evaluation cases, making the same type of task run more stably next time. This continuous evolution is controlled, traceable product iteration, not Agents modifying business rules without supervision.
4. How do you avoid Eureka.AI eventually becoming a traditional software company's project-based delivery, dragged down by custom requirements?
Sophie: This is something we're very alert to. Eureka.AI's principle is: client requirements must be deposited as much as possible into configurable, reusable, evaluable platform capabilities, avoiding uncopyable human projects.
AI FDE helps us judge which are truly unique client business rules and which are just industry-common processes; which can be solved through templates, parameters, input/output formats, and human confirmation points, and which require deeper product capability building.
We want to keep customization within "configurable, reusable, evaluable" boundaries. Industry terminology, report formats, sales processes, common materials, and output styles can be deposited as configurations and Skills; but if every client requires a completely different system development, that's not Eureka.AI's priority direction.
So AI FDE isn't leading Eureka.AI toward heavy delivery, but helping us identify, categorize, and converge requirements earlier, so truly valuable requirements feed back into the product.
5. From a commercialization perspective, is AI FDE more of a customer acquisition tool, customer success tool, or part of the product?
Sophie: Essentially, AI FDE is first part of the Eureka.AI product; on the commercial chain, it simultaneously lowers the first-use barrier and improves first-time success rate.
Many AI products require users to think of prompts themselves, upload materials, judge results, and repeatedly revise. This works for AI-savvy users, but for most SMB bosses and professional teams, the barrier is still high.
AI FDE solves this breakpoint: turning a vague requirement into an executable task, turning a trial into an acceptable Artifact, and depositing a high-frequency task as a reusable process.
From a customer acquisition angle, it lowers the barrier for clients to try AI for the first time; from customer success, it improves the probability of clients getting usable results on the first try; from a product angle, it deposits real requirements back into Eureka.AI's Agent, Skills, Artifact, and industry pack system.
AI FDE's goal isn't to make clients feel "Eureka.AI is very technical," but to make them feel: "It truly understands my business and can help me take the first step."
6. In Eureka.AI's S.Harness, which layer best reflects your core engineering value?
Sophie: If I could only choose one layer, I'd choose the Agent runtime.
Real enterprise work requires decomposing tasks, retrieving materials, calling tools, recording state, having humans confirm key actions, and ultimately delivering a result that can enter the business process.
General models provide base intelligence; S.Harness is responsible for organizing task decomposition, materials, tools, state, human confirmation, and Artifact delivery into a complete execution process.
For example, a business owner wants AI to understand client materials, organize industry background, generate a client visit briefing, output sales strategy, form an editable document, and have key conclusions sourced and the process reviewable. This leap from one-time capability invocation to complete task execution is the biggest difference.
7. Why does Eureka.AI treat the "evaluation and optimization loop" as a core component of S.Harness rather than hiding it in the backend?
Sophie: The industry already has RAG, workflow orchestration, tool calling, Agent Runtime, permission governance, and evaluation systems. What's different about Eureka.AI is reorganizing these capabilities around knowledge work: from material intake, context management, task execution, and human confirmation, to quality evaluation, formal Artifact delivery, and reuse as Skills.
We treat the "evaluation and optimization loop" as a core component of S.Harness because Agent products can't just pursue "generating what looks right" — they must pursue "are results reliable, is the process traceable, and can it be done better next time."
If evaluation is just a backend metric, it easily becomes something only the engineering team looks at; but when evaluation becomes a product layer, it directly affects user trust, team collaboration, permission governance, and result delivery.
After AI enters real workflows, quality, evidence, and reviewability aren't附属 features but core capabilities.
8. What exactly is the Artifact Eureka.AI talks about? How is it different from one-time generated results?
Sophie: An Artifact is not a one-time generated result, but a formal deliverable that can enter real business processes.
It can be a research report, project proposal, PPT, spreadsheet, webpage, code, operations record, meeting minutes, client briefing, sales material, or a fixed-format document type reused long-term within the enterprise.
What clients ultimately receive isn't a read-only result, but a work product they can continue editing, trace, and reuse. We hope Artifacts carry three layers of value simultaneously:
First, the visible formal document the user sees;
Second, the sources, citations, versions, and process records behind it;
Third, the methods, templates, and evaluation standards deposited from this task.
Enterprise clients don't lack a report that "looks okay"; what they truly need is deliverables that meet internal templates, approval habits, brand standards, and delivery criteria. Format compatibility, terminology, layout, and approval processes aren't minor issues — they're the key threshold for AI going from demo to production.
9. Why does Eureka.AI emphasize that Skills are not just prompts? What stage is the Skills ecosystem at now?
Sophie: A Skill shouldn't just be a prompt; it should include task boundaries, input requirements, work steps, tool calling, output format, quality evaluation, and human confirmation points. Only then can it become a truly reusable, deliverable, commercializable capability module.
At this stage, Eureka.AI doesn't use Skills Marketplace quantity as core promotion; what matters more is first polishing a few high-frequency, essential, verifiable official Skills to be genuinely good — not making a "prompt store" that looks lively but users leave after one use.
At this stage, we'll prioritize building official Skills around high-frequency knowledge work scenarios: research and reporting, document intelligence, sales and marketing, data reports, and project operations.
In the future, industry experts, AI consultants, implementation partners, and client teams can all participate in creating Skills. Business experts don't need to write code, but complex system connections, APIs, MCP, private knowledge bases, or specific deployment environments require development capability or implementation partner support.
10. During Agent execution, which actions must be confirmed by humans? Does human confirmation hurt efficiency?
Sophie: Human confirmation points can't simply say "the fewer the better" or "the more the safer" — the key is risk grading.
For low-risk actions like organizing materials, summarizing points, and adjusting format, the system should handle them automatically as much as possible; but for high-risk actions like sending externally, citing sensitive information, forming important judgments, modifying key data, calling external tools, incurring costs, or touching permission boundaries, user confirmation should be required.
Different clients have different acceptance levels. Legal, audit, investment research, and professional service scenarios usually prefer more confirmation points because responsibility boundaries are critical; marketing, operations, and internal knowledge organization prefer smoother flows.
In the future, confirmation thresholds can be set by team role, task type, risk level, data sensitivity, and client preference.
Eureka.AI's goal isn't to let AI constantly disturb people, nor let AI make unilateral decisions, but to find appropriate control points in human-machine collaboration.
11. What's the boundary between Pro/Team and Commercial Edition? When does an AI tool become a production system?
Sophie: Once AI moves from an individual efficiency tool into team-level business processes, Commercial Edition value begins.
Pro/Team is more suited for lightweight teams, professional users, and SMB daily work — reports, documents, sales materials, project operations, and business analysis.
If a client needs more complex role permissions, private knowledge management, audit records, enterprise connectors, industry packs, security policies, cost control, or wants the system to run within specific data boundaries, Commercial Edition is more appropriate.
The trigger for the commercial edition isn't company size, but whether the task enters the formal business responsibility chain.
When clients start asking: "Who can see? Who can edit? Where's the evidence? Can results be audited? Can data leave the domain? How to control model costs? Can industry templates be reused?" — then it's no longer a simple AI tool, but production system building.
12. How does Eureka.AI reduce sensitive information exposure through "minimum necessary context" and controllable data boundaries?
Sophie: This doesn't mean all tasks are processed locally, nor packaging it as federated learning concepts. Our core principle is: minimum necessary context and optional data boundaries.
Some tasks can first be completed based on anonymized summaries, structured inputs, fields, directories, templates, and user confirmation, without uploading complete raw materials from the start; some tasks can be handled through permission connectors, client-controlled environments, browser environments, local environments, or commercial edition capabilities; for clients with stronger data boundary requirements, lightweight private deployment can be considered.
What lawyers, auditors, consultants, investment researchers, and compliance teams care most about is often not whether the model is strong enough, but whether client materials will leak, whether results can be traced to evidence, and who is responsible when AI makes errors.
So this isn't marketing language, but a product design principle. Whether professional users adopt AI often depends first on "can it be safely put into the workflow" before "how well it generates."
13. If large companies make Agent orchestration, governance, and evaluation all free, why would Eureka.AI clients stay?
Sophie: Large model companies provide increasingly strong base intelligence, and open-source frameworks provide good development components, but what clients truly need to solve is: where are my materials, how do processes run, how are results verified, how does the team collaborate, and how are products delivered?
Eureka.AI's position isn't replacing models or all frameworks, but through S.Harness, organizing models, knowledge, tools, workflows, governance, and Artifact delivery into a running system for knowledge work.
What clients need isn't a single feature, but a set of work assets that fit their business — including industry templates, the client's own knowledge, internal processes, permission policies, evaluation cases, deliverable formats, team usage habits, and reusable Skills.
The closer these assets are to clients' real work, the less likely they are to be completely replaced by a generic free feature.
Many teams can quickly build Agent demos with open-source frameworks, but turning it into a system the team uses daily, clients are willing to pay for, and results can be traced requires substantial productization capability. What Eureka.AI wants to carry is precisely the middle layer from "can build an Agent demo" to "can stably deliver a knowledge work system."
14. What is the "aha moment" when SMB clients first clearly feel Eureka.AI's value?
Sophie: We don't educate SMBs with many Agent concepts. What SMB bosses truly care about is whether they can get client proposals, weekly business reports, sales scripts, competitive analyses, and project plans faster, and whether they can reduce the time spent on repeated communication and revisions.
The "aha moment" usually happens when clients discover the system not only completed the current task but also turned scattered materials into structured, editable, reusable formal deliverables, and can reuse the same process and Skill for the next similar task.
For example, a client inputs a set of anonymized materials, selects a scenario Agent, and the system generates a client visit briefing, weekly business report, sales proposal, or project retrospective based on industry, goals, preferences, and output requirements. The deliverable is structured, sourced, editable, and reusable.
15. What kind of work is unsuitable for Eureka.AI? Is this a product maturity issue or a hard boundary of Agents themselves?
Sophie: Eureka.AI is more suited for knowledge-intensive work with clear materials, decomposable processes, and judgeable results.
Conversely, if task goals are very vague, input materials are missing, judgment standards don't exist, and there's no stable process, it's unsuitable to hand to Eureka.AI from the start. Things like "help me fully AI-ify the company," "build a universal AI employee," or "automatically make all operating decisions" sound big but aren't suitable as a first step.
For final judgments on legal opinions, audit conclusions, investment decisions, medical advice, external sending of important content, and actions involving permissions and funds, we also insist on human confirmation.
Some of these boundaries are product maturity issues. As models, tool calling, evaluation, permission governance, and context engineering improve, many tasks will gradually be covered.
But as long as a task involves responsibility, value judgment, and real-world consequences, final responsibility shouldn't be fully handed to AI. In these scenarios, AI is better at organizing evidence, generating drafts, flagging risks, simulating options, and tracking the process; final judgments should still be made by human professionals.
So human-machine collaboration isn't a transitional design, but a long-term requirement for production-grade Agent systems.
16. What is Eureka.AI's real competing alternative? Is it Copilot, Notion AI, or vertical SaaS?
Sophie: Eureka.AI's competitors aren't a single type.
We overlap with augmented tools like Microsoft Copilot and Notion AI, and also have substitution relationships with vertical industry SaaS, Agent frameworks, AI consulting services, and even users' own approach of using ChatGPT plus document tools.
But from a client budget perspective, what we truly replace is usually not one software, but the low-efficiency working style composed of "manually organizing materials, multi-round communication, repeated revisions, scattered prompts."
Previously it might be ChatGPT generating fragments, Feishu or Notion recording, Excel organizing data, PPT delivering, with humans constantly supplementing context and modifying formats. What Eureka.AI wants to replace is the large amount of repetitive, broken, unreusable work in this chain.
If a client just asks a question occasionally, using a large model directly is enough; if a client wants a type of knowledge work completed repeatedly, stably, and traceably, the value of AI FDE and S.Harness becomes more obvious.
17. If you could only go all-in on one direction, would Eureka.AI choose infrastructure, S.Harness, or vertical industry Skills?
Sophie: If I could only go all-in, I'd choose S.Harness plus Artifact generation, while selectively developing vertical Skills upward.
Clients ultimately don't pay for "orchestration frameworks" or "model routing," but for a result that can enter the workflow, be delivered, be reused, and be governed.
Artifact-first is a very important judgment for Eureka.AI. Only when users receive formal deliverables like reports, spreadsheets, proposals, PPTs, code, or webpages do they truly perceive the business value of Agents.
If S.Harness is just a task orchestration layer, it's easily integrated and the ceiling may not be high. But the S.Harness Eureka.AI talks about also connects client knowledge, context, processes, permissions, evaluation cases, product templates, team habits, and Skills.
The real barrier isn't "I have an orchestrator," but clients depositing industry templates, historical cases, delivery standards, evaluation rules, team permissions, and reusable processes here.
On vertical Skills, Eureka.AI doesn't need to compete head-on with vertical SaaS in every industry; it's more suitable to let industry experts, AI consultants, implementation partners, and client teams create Skills, while the platform handles underlying runtime, governance, evaluation, distribution, and commercialization.
18. A year from now, which Agent products will disappear? Which currently "unsexy" capabilities will become important?
Sophie: A year from now, many "demos that look like Agents" will disappear.
Agents that can only do one cool operation, without evaluation, permissions, formal product, or reusability, will gradually be phased out by clients. The generic "universal Agent" narrative will also return to more specific scenarios and workflows.
Conversely, capabilities that seem unsexy now may prove very important: evaluation, governance, human confirmation, context management, knowledge compilation, product reliability, version management, auditing, and cost control.
These may not be the most attractive in demo videos, but they directly determine whether clients keep paying. Enterprise clients don't pay for demos; they pay for stable, trustworthy, reusable business results.
The same goes for Skills Marketplace. If Skills are just a pile of prompts, it easily becomes a lively but low-value plugin market; only when Skills are bound to real tasks, industry knowledge, evaluation standards, client payment, and continuous reuse can it become a new professional capability distribution network.
19. For Agent products going from "can do" to "clients willing to pay continuously," what's the most critical leap? What is Eureka.AI's north-star metric?
Sophie: The most critical leap is from demo capability to production responsibility.
Many Agent products can demonstrate "can do" in PoC, but for clients to keep paying, they need to see something more stable: can tasks be repeated, can results be accepted, can the process be traced, can risks be controlled, can teams collaborate, can ROI be calculated clearly.
Eureka.AI's current focus is productization and payment validation for high-frequency scenarios, caring more about whether the product direction is repeatedly used by real clients.
Our north-star metric isn't one-time call volume or token consumption, but: how many high-frequency tasks are stably completed as client-recognized Artifacts, and how many of those processes are deposited as reusable Skills.
We also look at effective Artifact count, task completion rate, reuse rate, team retention, Skills creation and usage, paid conversion, and whether clients are willing to migrate more workflows in.
If a client just tries once, generates one piece of content, and leaves, that's not the value we want. What truly matters is clients continuously using Eureka.AI to complete a type of work, deliverables being accepted, process being reviewable, methods being reusable, and teams collaborating.
20. For enterprises that want Agents to truly enter workflows, what's your most specific piece of advice?
Sophie: Don't start by "buying an AI tool"; start by "choosing a repeatable real task."
Many enterprises fail at AI not because the model isn't strong enough, but because they start by wanting to transform all processes. A better approach is to choose a high-frequency task with clear materials and judgeable results — like a weekly business report, client visit briefing, competitive analysis, contract clause screening, sales proposal, or project retrospective.
You can start with a seven-step checklist:
Choose a task the team does at least once a week.
Prepare 5-10 real but anonymizable historical materials.
Define the final deliverable format — report, spreadsheet, PPT, proposal, or briefing.
Write clearly what a "good result" looks like, including structure, accuracy, citations, style, and forbidden zones.
Set at least one human confirmation point to prevent AI from overstepping on key judgments.
Run it three times consecutively and record what needs changing each time.
Deposit stabilized steps as a Skill instead of rewriting prompts every time.
This doesn't require a large budget upfront, but can quickly judge whether an Agent can truly enter the workflow.
If a task runs three times and quality gets more stable, modifications decrease, and the team starts actively reusing it, it has a chance of becoming a real AI workflow. The biggest breakthrough for future Agent products won't just be model capability, but Artifact reliability: whether it can be trusted, modified, cited, audited, and stably enter business processes.
About the Interviewee
Sophie Yang | Eureka.AI Co-founder & CEO
Sophie Yang is responsible for Eureka.AI's commercialization, strategic capital, and global ecosystem. She has over 10 years of enterprise digital transformation and AI commercialization experience, previously serving as SOIN AI CEO and Kunlun Wanwei Tiangong AI Commercialization VP, participating in and driving 0-to-1 commercialization of AI Search, SkyAgent, AI Music, and multiple enterprise-level Agentic AI products. She is also an inventor on several Agentic AI core technology patents.
ISC Hall of Fame annual inductee; Peking University North Venture Camp; Tsinghua PBCSF; 360 Group ISC.AI distinguished AI expert; Beijing Academy of Artificial Intelligence (BAAI) distinguished AI project expert advisor; 2025 APEC and KES invited representative; Singapore Google Cloud ecosystem event invited representative; OpenAI Singapore strategic ecosystem partner and invited expert; Global Women's Development Foundation.