Original · Unique Research · 2026-07-09
Editor's note: This is the original Chinese author's interview with Cen Runzhe and its framing. This English rendition retains the full text in source order, including all 15 Q&A items. The interviewee's product claims, performance figures, and market views are his self-reports, attributed to him and not independently verified. Person, company, and product names are preserved as source attributions.
AI Industry Observation
The awakening of the "human agent": from "whatever is fastest" to daring to cut back.
Cen Runzhe (岑润哲) spent five years in quant operations at a top internet company, serving enterprise after enterprise, each requiring a full-chain digital operations platform built from scratch. By the fifth client, he suddenly realized an absurd fact: he was essentially a "human agent." Every day he pulled data from various systems, did attribution, wrote conclusions, and pushed actions — the same methodology rewritten five times in SQL, reconciled five times in metric definitions, and used to train five batches of analysts.
"What's truly scarce isn't analytical ability, but analytical ability that can be reused at scale."
This is the underlying logic of his joining Shushi Technology (数势科技) and productizing the methodology into SwiftAgent. But the hardest step from operations thinking to product thinking wasn't technology — it was "restraint."
The "Human Agent" Awakens: From "Whatever Is Fastest" to Daring to Cut Back
In operations, facing a business problem, Cen Runzhe could write a dedicated logic for that client directly, whatever was fastest. But as product owner, every requirement first passes through a sieve: is this one client's special case, or a class of clients' commonality?
"If we hard-code a special case into the mainline just to close one deal, half a year later the product becomes spaghetti code stacked from patches." This "delayed gratification" restraint took him quite a while to adapt to.
But zero customization isn't realistic either. He gave a vivid example: metric-definition alignment workflows, anomaly-attribution analysis paths, and report-template structure are already almost fully productized into SwiftAgent's semantic layer and Agent workflows — new clients only "map" rather than "rewrite." But the "rectification tracking" step in store-visit supervision almost always changes per client. Retail chains and financial branches have completely different responsibility levels, reporting chains, assessment definitions, and SOP knowledge bases. Even for "supervisor finds a problem then pushes the store to rectify," the granularity AI may recommend reaching differs. Rather than keep hard-coding, they were forced into a configurable "process orchestration layer" — letting clients drag-and-drop to define responsibility chains themselves, instead of the team changing code each time.
As product owner, Cen Runzhe measures his work with three indirect metrics: client conversion from POC to scaled renewal, the ARPU growth curve per client (new modules / new department penetration), and whether delivery marginal cost is falling (is the onboarding cycle for the same new client shorter than a year ago?). "Taken together, the three metrics essentially answer 'is productization high enough?'"
Investors' most common question isn't how flashy the features are, but a sharper line — "If you replace the few people who know the business best, does the product still run?" Behind it is: is the moat in the system, or in people's heads?
Multi-Agent Is Flashy, but Clients Pay for "It Knows What It Doesn't Know"
In May 2024 SwiftAgent 2.0 launched, emphasizing the "unified semantic layer" and "Human in the Loop." In April 2025 it launched 3.0, upgrading to a Multi-Agent architecture on a DeepSeek R1/V3 core. But after 3.0, the team stopped announcing big version numbers externally — after integrating DeepSeek, underlying model capability rises a step every six months; if they still ran on "annual major versions," the product would always be half a beat slow.
The current core upgrade extends the Multi-Agent architecture validated in 3.0 from "analysis-attribution-report" to a fuller "analysis-decision-action" closed loop. In several financial and retail flagship clients, the Agent can already turn attribution conclusions directly into executable action recommendations and connect to the client's own ticket/approval systems, forming a traceable loop.
But the most counterintuitive finding? What clients value most isn't "multi-agent" or "chain-of-thought" — things that sound flashy — but the "clarifying-question mechanism" based on Human-in-the-Loop: when a user asks vaguely or the semantic layer doesn't cover it, the Agent proactively asks back to clarify, rather than confidently giving a wrong answer.
"Clients trust 'knowing what it doesn't know' far more than 'looking smart.'"
What leaves them cold is instead the "upgrades" on the visualization layer — flashier chart interactions, more anthropomorphic conversational style. What business users really care about is whether the conclusion is right and whether they can quickly locate the problem; past the passing line, interface beauty has low marginal value. This is why the team later shifted resources from "experience polish" to "attribution accuracy and action closure."
SwiftAgent's core differentiation is NL2Semantics (not traditional NL2SQL), achieving "100% elimination of large-model hallucination" on a self-built metric semantic layer. Cen Runzhe explains: the principle isn't letting the model "guess SQL more cleverly," but letting it choose and combine within a metric semantic space already validated by business people with clear definitions — the model doesn't generate things it isn't authorized to understand, so it naturally doesn't fabricate.
But "100%" has a boundary condition: the semantic layer's coverage. As client business complexity rises, new metrics, dimensions, and business definitions keep emerging; if the semantic layer doesn't add definitions in time, the model can indeed drift outside the boundary. The handling is pragmatic: "ask first, then fall back" — for questions not pre-defined in the semantic layer, the Agent first prompts "this metric/dimension isn't currently defined in the semantic layer; should I try a temporary combination from existing data?" Only after user confirmation does it take an NL2SQL fallback path labeled "not semantic-layer validated," clearly marking confidence and definition source in the result. "We never mix the two kinds of results without distinguishing them."
Is the L4 Loop Closed? Behind 5,000 Stores, What Sticks Isn't Technology
SwiftAgent divides data analysis into four levels: L1 data-report extraction, L2 intelligent insight attribution, L3 industry report generation, L4 high-order action drive. Cen Runzhe says clients' core demand sits at L3 and L4. A year on, is L4 closed? In certain industries and scenarios, yes.
A tea-drinking chain case: the Agent monitored that same-store growth in a district was below the mean for two straight weeks; attribution found newly opened competitor stores nearby plus an expiring membership campaign; the system auto-generated a "member reactivation + regional joint marketing" rectification recommendation, pushed it to a regional supervisor for approval, and dispatched it to relevant stores in one click, then auto-recovered data a week later to verify effect. The full chain: detect anomaly → attribute cause → generate rectification recommendation → push to responsible person → track effect. This is L4.
But the sticking point is almost never on the technology side. Cen Runzhe is blunt: "The enterprise's decision authority hasn't reserved a fast lane for 'machine-initiated recommendations'; no matter how accurate the Agent is, it can only stop at 'recommendation.'" This is essentially an organizational problem, not a technology problem.
The depth difference between finance and retail is clear: financial clients currently stay more at L2, L3 — high-order actions involving funds, compliance, and risk control are naturally treated more prudently by regulators and internal risk control, so acceptance of the Agent directly triggering action is low. Retail chains move faster on L4 — store-operation actions have controllable trial-and-error cost, and effect is quickly validated by same-store data.
"Real-time attribution for 5,000+ store anomalies, doubling supervisor per-capita efficiency" — the quantified definition is "the number of store problems a supervisor can effectively handle per unit time." It's two parts stacked: first, the time to find and attribute problems compresses from manual store visits + manual data checks to system auto-push, greatly shortening per-store diagnosis; second, supervisors can cover more stores per person, no longer checking store by store but prioritizing system-flagged anomaly stores.
Someone may ask: AI attribution is accurate, but what if store execution falls short? "What AI can do is push the right problem and right recommendation to the right person at the fastest speed. But whether the store manager seriously executes is an organizational-execution problem; AI can't solve it now, nor should it try." What it can do is make "rectification tracking" part of the loop — the system continuously tracks whether the rectification action is actually executed and how effective it is, feeding execution rate and effect back to regional managers, so that "not executed" is seen and managed.
Analysts' work is also changing structurally. Repetitive data pulls and report-building drop sharply; more shifts to "defining metric definitions," "designing attribution logic," and "judging whether the Agent's recommendation fits business common sense." AI isn't replacing analysts; it's turning analysts from "data movers" into "semantic-layer architects."
Clients who truly moved from "trial" to "deep dependence" (core business-decision meetings default to opening SwiftAgent rather than traditional reports) are about 30% among flagship clients. This ratio rises steadily, but is far from "most." The adoption cycle for data intelligence is longer than many expect.
BI Is a Dashboard; SwiftAgent Is a Copilot
The long adoption cycle isn't because enterprises don't want to use it, but because everyone asks the same question: how does this relate to my existing BI system? The answer may disappoint some investors — short-term, it's complementary, not replacement.
Cen Runzhe drew me a picture: traditional BI keeps being the "dashboard" — fixed reports, underlying data governance, jobs it does well; SwiftAgent is "the person in the copilot seat" — you point at the dashboard and ask "what does this red light mean," "where should I turn next," and it interprets and attributes. The dividing line is clear: natural-language query + intelligent attribution + action recommendation belong to SwiftAgent; fixed report display + underlying data governance continue with BI. I asked whether any client has dropped BI for SwiftAgent alone; he said yes, but only where the client feels fixed-report value has dropped low enough to no longer need — "this critical mass hasn't arrived at scale." Plainly, today's state is each doing its job, not one eliminating the other. Clients don't face an either/or choice; that's the real enterprise procurement scene. Behind this is an easily misunderstood business logic: SwiftAgent's opponent isn't BI vendors but "people" — the analysts woken up at midnight by the CEO to run numbers, the department heads haggling over five different metric definitions. What technology replaces is people's repetitive labor, not existing technical systems.
The Entry Fight: Don't Fight the IM Users Open Every Day
Another very real competitive dimension is the entry. DingTalk, Feishu, and WeCom all push their own AI assistants and data-analysis capabilities. How does SwiftAgent respond? Cen Runzhe is candid: "If the entry fight is between us and national-level office platforms, the outcome is no contest." So the strategy isn't confrontation but becoming a "capability backend." Business people would rather ask in DingTalk directly "why did this month's sales drop" than open another standalone app — this user behavior is nearly irreversible. SwiftAgent's choice is to become an Agent engine callable inside DingTalk and Feishu; the entry users perceive is still the IM they use daily, but behind it, the attribution analysis and recommendation generation are really done by SwiftAgent's semantic layer. This path lowers the usage threshold and actually helps scaled rollout. Plainly, being a backend isn't embarrassing; fighting user habit is.
DeepSeek Forced Us to Rewrite a Module
I asked a more technical question: after DeepSeek R1/V3 came out, what changed in your product architecture? The answer was direct — forced to rewrite a module. Before R1's reasoning chain launched, the team designed a "multi-step manual decomposition guidance" Prompt engineering to compensate for weak model reasoning. The model couldn't chain-of-thought on its own, so humans broke steps and guided it step by step. This engineering took months to write and ran stably in production. After R1, the model can do better chain reasoning itself. That manual guidance was not only useless but became a constraint — over-design dragging it back. "This is a typical refactor forced by model progress." More of this will happen in the AI application layer: what you think is an engineering moat may become technical debt the moment model capability upgrades. So AI application teams need a realization — the code you write may be "covered" by model capability half a year later; think ahead about what true sustainable differentiation is.
The Stronger the Model, the Heavier the Semantic Layer — How to Read This Counterintuitive Call
Many people intuit that "the stronger the model, the lighter the product" — more the model can do, the less the upper-layer app must do. Cen Runzhe's judgment is the opposite: the stronger the model, the more important the semantic layer. Why? Model capability solves "understanding and generation," but enterprise decisions need "trustworthy, explainable, traceable." The smarter the model, the more enterprises fear it "speaking loosely" — especially in finance, any investment advice or risk alert SwiftAgent gives must pass the client's compliance team's manual review before formally reaching the client. The semantic layer's job is to draw the model's boundary, telling it "within what range your output is trusted."
Deeper: the semantic layer's real moat has never been "translation ability" — mapping natural language to SQL something models increasingly do, and zero-shot SQL accuracy over 90% is only a matter of time. The semantic layer's moat is "business consensus": what a metric is named, how its definition is set, which dimension combinations are meaningful in business, which are spurious correlations — these are years of internal bargaining sedimented in the enterprise, something a large model can't grow just by seeing more data. So Shushi's internal strategy isn't "hold the semantic layer" but actively shift the center of gravity from "how to query data" toward "how to define business consensus, how to sediment analysis SOPs, how to manage decision authority" — going heavy toward "decision governance," the direction least replaceable by models. This move looks defensive; it's actually offensive.
Show-Off Will Cool; Infrastructure Will Win
After the competitive landscape, I asked for a prediction: a year on, which hot industry concepts will disappear? Cen Runzhe says what cools is narratives that purely emphasize "number of multi-agents" and "Agent orchestration complexity" — engineering show-off — "clients ultimately don't care how many Agents you collaborate; they only care whether results are right and land." What will be proven right instead are unsexy capabilities like "decision governance" and "permission tiering." "Not flashy but necessary" — these five words are his definition of next-generation Data Agent infrastructure.
I asked whether he still stands by a year-old line that "in 2025 China's AI acceleration will comprehensively surpass other regions." He revised it: at the enterprise-app layer it's partly validated — DeepSeek made "affordable" real, and scenarios once limited by model cost can now land. But on original breakthroughs in foundation models, the China-US gap remains. "China's lead is in application-layer landing speed, not absolute lead in underlying technology. This distinction must be clear; otherwise it's irresponsible optimism."
Three Signals: If Two Are Missing, Don't Rush to the System
Near the end, I asked him: if you could give enterprise CIOs, data analysts, and AI entrepreneurs only one landing piece of advice, what? He said: don't start by "getting an Agent product"; start by "sorting out your metric semantic consensus." Many enterprises fail not in model selection but in not aligning even basic business definitions internally — the same "sales" has three calculations across three departments. Any intelligent tool in this environment only amplifies chaos, not solves it.
He gave three concrete self-check signals: First, do core business metrics have unified, written, cross-departmentally recognized definitions? If the same "sales" has three calculations, don't get an Agent yet. Second, is there a clear data owner and governance process? When data breaks, you know who to find and how to fix it, not an unowned black hole. Third, is management willing to reserve a decision fast lane for "AI-recommendation-driven actions"? If every AI recommendation goes through a full traditional approval flow, the Agent's efficiency advantage can't show. If two of the three signals are unmet, fix the basics first; don't rush to the system.
"Data Agent is an amplifier of consensus, not its maker."
When inside an enterprise even "what counts as an effective customer" has three sayings, getting an Agent isn't solving a problem — it's propagating chaos to more people faster. Technology can accelerate right decisions, and wrong ones. The difference is — before pressing the accelerator, have you confirmed this team actually has consensus on what "right" means.
Selected Interview Q&A
Q1: Without an official intro, how would you explain to an enterprise client who doesn't know SwiftAgent what you do?
Cen Runzhe: In one sentence, SwiftAgent wants to solve the enterprise problem of "lots of data, but little data actually usable for decisions." In the past, a business team wanting to look at a problem often had to find data, reconcile definitions, write SQL, attribute, and report, relying on lots of manual experience. SwiftAgent doesn't simply add a chatbox to BI; it productizes metric definitions, analysis methods, and business SOPs the enterprise has already accumulated, letting AI help business people complete analysis, attribution, and reports in a trusted semantic space, even push action. So I'd rather understand it as an enterprise Data Agent: it doesn't "guess answers" for you, but based on the enterprise's own business consensus, helps you truly turn data into decisions.
Q2: You were a senior quant-operations lead at a top internet company; why did you decide to join Shushi Technology to productize this methodology?
Cen Runzhe: The turning point wasn't "I want to make products" but discovering the same problem recurred across clients. In quant operations I was essentially a "human agent" for business teams: every day pulling data scattered across systems, doing attribution, writing conclusions, pushing actions. This methodology reused across many enterprises, but every landing rewrote SQL, re-reconciled metric definitions, retrained analysts. I realized what's scarce isn't point analytical ability but analytical ability reusable at scale. Shushi does exactly this: turn the metric semantic layer and analysis SOPs into a productized base, instead of starting from zero each project. The hardest to adapt to is "restraint." In operations, a client need lets me write dedicated logic directly, whatever's fastest. But as product, every requirement first asks: is this one client's special case, or a class's commonality? If you hard-code a special case into mainline to close one deal, half a year later the product is spaghetti code. This shift from operations to product thinking is the hardest gate.
Q3: From 2.0 to 3.0 to today, what's SwiftAgent's core upgrade?
Cen Runzhe: After 3.0 we didn't announce a bigger version number; we moved to higher-frequency capability iteration. The reason is underlying model capability changes too fast. After DeepSeek, including GLM and Qwen, a series of domestic models also progress fast. If we still producted by "annual major version," the rhythm would always be half a beat slow. Now SwiftAgent's core change extends the Multi-Agent architecture validated in 3.0 from "analysis-attribution-report" to an "analysis-decision-action" loop. Especially in some financial and retail flagship clients, the Agent no longer just reports; it turns attribution conclusions into action recommendations and connects to the client's own ticket and approval systems, forming a traceable business loop.
Q4: Which upgrade point do clients value most? Any feature you think is great but clients feel nothing?
Cen Runzhe: What's valued most isn't the flashy "multi-agent" or "chain-of-thought" but the "clarifying-question mechanism" based on Human in the Loop. When a user's question is unclear or the semantic layer doesn't cover the metric, the Agent proactively asks back rather than confidently giving a wrong answer. Clients trust "knowing what it doesn't know" far more than "looking smart." What leaves them cold is many visualization upgrades — flashier chart interactions, more anthropomorphic dialogue. Business users really care whether conclusions are right and whether they can quickly locate the problem. Past the passing line on interface beauty, marginal value is low. So we shifted more resources to attribution accuracy and action closure rather than polishing surface experience.
Q5: You mentioned L4 "high-order action" — the Agent directly driving business execution. Is this truly closed now?
Cen Runzhe: In certain industries and scenarios, yes. Take retail-chain store visits: the Agent completes the whole chain of "find anomalous stores → attribute to a specific cause → generate rectification recommendations → auto-push to the corresponding supervisor and store manager → track whether rectification is done and effect recovers." For a tea-chain client, the Agent monitored same-store growth in a district below the mean two weeks straight; further attribution found newly opened competitor stores plus an expiring membership campaign. The system then auto-generated a "member reactivation + regional joint marketing" recommendation, pushed it to the regional supervisor for approval, dispatched it to relevant stores, and auto-recovered data a week later to verify effect. But the sticking point is often not technology but organizational process. Joint marketing needs the marketing department to cooperate; many enterprises haven't reserved a fast approval lane for "machine-initiated recommendations." No matter how accurate the Agent is, it can only stop at "recommendation." One step further enters organizational authority and responsibility-boundary questions.
Q6: You once emphasized NL2Semantics can "100% eliminate large-model hallucination." Does this hold now?
Cen Runzhe: It holds within semantic-layer coverage, but with a boundary condition. The principle isn't making the model guess SQL more cleverly, but letting it choose and combine within a metric semantic space already validated by business people with clear definitions. The model doesn't generate what it isn't authorized to understand, so it naturally doesn't fabricate. But "100%'" boundary is the semantic layer's coverage. As client business complexity rises, new metrics, dimensions, and definitions emerge; if the semantic layer doesn't add definitions in time, the model can drift outside. But this differs from traditional NL2SQL "making up SQL" directly against table structure. It's more like "can't answer because not learned," rather than fabricating a seemingly right answer.
Q7: If a client asks an ad-hoc question not pre-defined in the semantic layer, how does SwiftAgent handle it?
Cen Runzhe: Our logic is "ask first, then fall back." If the question involves a metric or dimension not pre-defined in the semantic layer, SwiftAgent first prompts the user: this metric/dimension isn't currently defined in the semantic layer; should we try a temporary combination from existing data? Only after user confirmation does the system take an NL2SQL fallback path labeled "not semantic-layer validated," clearly marking confidence and definition source in the result. We don't mix semantic-layer-validated results with temporary fallback results. In enterprise decisions, the answer matters, but the answer's source and trusted boundary matter too.
Q8: Models get stronger, and direct raw-data understanding improves. Will the semantic layer's value dilute?
Cen Runzhe: My judgment: the semantic layer thickens, but its form changes. Model capability does compress the "translation layer" value in the semantic layer. Mapping natural language to SQL grammar is increasingly a native model ability, and we admit this value is declining. But the semantic layer's real moat was never translation ability; it's business consensus. What a metric is named, how its definition is set, which dimension combinations are meaningful, which are spurious correlations — these are consensus sedimented over years of enterprise bargaining and management practice, not something a general model grows automatically by seeing more data. So the semantic layer's center of gravity shifts from "how to query data" to "how to define business consensus, sediment analysis SOPs, and manage decision authority." The stronger the model, the more enterprises need a trusted, auditable framework to hold it; otherwise decision-black-box risk actually grows.
Q9: From "trial" to "deep dependence," how far has client conversion gone?
Cen Runzhe: Clients who truly moved from "trial" to "deep dependence" — where core business-decision meetings default to opening SwiftAgent rather than traditional reports — are about 30% among flagship clients. This ratio rises steadily but is far from "most." Data intelligence's adoption cycle is longer than many expect. The clear change is that enterprises haven't directly cut data-analyst hiring because they use SwiftAgent, but analysts' work is changing structurally. Repetitive data pulls and reports drop; analysts shift more to defining metric definitions, designing attribution logic, and judging whether the Agent's recommendation fits business common sense. This is what we want: AI doesn't replace analysts but turns them from "data movers" into "semantic-layer architects."
Q10: How does usage depth differ between finance and retail clients? Who pays more for L4?
Cen Runzhe: Financial clients currently stay more at L2, L3 — intelligent attribution and report generation. Once funds, compliance, and risk control enter, acceptance of the Agent directly triggering action is naturally lower; regulators and internal risk control are more prudent. Retail chains move faster on L4 and pay more willingly. The reason is direct: store-operation actions have relatively controllable trial-and-error cost — adjusting promotions, reallocating inventory, dispatching rectification tasks — and effect is quickly validated by same-store data. Management sees ROI in a short cycle, so it naturally pays more for the "action" layer. Finance values trustworthiness, compliance, and explainability; retail values efficiency, execution, and result recovery. The two accept Agents differently.
Q11: You mentioned "real-time attribution for 5,000+ store anomalies, doubling supervisor efficiency." How is this 2x quantified?
Cen Runzhe: The definition is "the number of store problems a supervisor can effectively handle per unit time." It mainly comes from two changes: first, problem-finding and attribution time compresses. Before it took manual store visits and manual data checks; now the system auto-detects anomalies and pushes causes. Second, supervisors cover more stores per person; they no longer check store by store but prioritize system-flagged anomaly stores. But there's a "last mile" problem. AI can push the right problem and right recommendation to the right person fastest, but whether the store manager seriously executes is organizational execution. AI can't solve it now, nor should it replace management itself. What we can do is include rectification tracking in the loop: the system continuously tracks whether rectification actions are executed and whether effect recovers, feeding execution rate and effect back to regional managers. Then "not executed" is seen and managed, rather than pretending AI can replace human management responsibility.
Q12: Going-global companies have data scattered across Amazon, Shopify, TikTok Shop, DTC sites and more. How does SwiftAgent cover such multi-platform heterogeneous data?
Cen Runzhe: Going-global companies' data is more fragmented than domestic. One brand may run on Amazon, Shopify, TikTok Shop, and DTC sites simultaneously, but different platforms naturally define "conversion rate," "return rate," "valid order" differently. Our approach is first "platform-layer semantic mapping": map each e-commerce platform's native data fields and metric definitions uniformly into the enterprise's own core business semantics. For example, first uniformly define how a "valid order" is counted, then let each platform's data enter the same analysis framework. This is more complex than a single domestic business system's semantic layer, because mapping rules must be maintained as platform APIs change. Domestic clients usually care more about depth — can the same data be mined finely enough; going-global clients care more about breadth and real-time alignment. This also forces us to redesign the semantic layer's "onboarding speed" as a priority in going-global scenarios.
Q13: Any clients you'd actively reject? What kind of enterprise isn't suited to a Data Agent now?
Cen Runzhe: Yes. The most typical case isn't insufficient budget or overly complex needs, but too weak data-governance basics. For example, core business data scattered across unintegrated systems, basic metric definitions lacking internal consensus, even within one department three sayings on "what counts as an effective customer." Getting an Agent in this case only amplifies internal definition chaos faster to more eyes, rather than solving it. A Data Agent is an "amplifier of consensus," not a "maker." If an enterprise hasn't aligned even basic metric definitions, any intelligent analysis tool only accelerates spreading misinformation. We now add a "data-governance maturity diagnosis" in presales. If the diagnosis is too poor, we advise the client to do three to six months of basic governance first before considering an Agent. This isn't conservative; it's more responsible to both sides.
Q14: Traditional BI vendors are all going AI, and big platforms push intelligent analysis. Is SwiftAgent a replacement relation?
Cen Runzhe: Short-term complementary; mid-term converging. But convergence leadership depends on whose semantic layer is deeper and who upgrades "analysis" to "action" faster. Traditional BI vendors' AI-ification mostly still adds a natural-language Q&A entry to reports, essentially not reconstructing underlying semantic understanding and decision logic. Big platforms' intelligent analysis is more generic, lacking industry-depth metric semantics. Many clients already have traditional BI and then add SwiftAgent. Their coexistence usually is: original BI keeps fixed-report display and underlying data governance; SwiftAgent takes over natural-language query, intelligent attribution, and action recommendation. Think of BI as the dashboard and SwiftAgent as the copilot beside it, helping interpret and decide. They don't immediately replace but do their jobs.
Q15: If you could give enterprise CIOs, data analysts, and AI entrepreneurs one piece of advice to truly land a Data Agent, what?
Cen Runzhe: Don't start from "getting an Agent product"; start from "sorting out your metric semantic consensus." Many failed landings don't root in model or product selection but in the enterprise not aligning even basic business definitions internally. Any intelligent tool amplifies this chaos. Look at three signals: first, do core business metrics have unified, written, cross-departmentally recognized definitions? If the same "sales" has three calculations across departments, don't rush to an Agent. Second, is there a clear data owner and governance process? When data breaks, know who to find and how to fix it, not an unowned black hole. Third, is management willing to reserve a decision fast lane for "AI-recommendation-driven actions"? If every AI-generated recommendation must go through a full traditional approval flow, the Agent's efficiency advantage is hard to show. If two of these three signals are unmet, fix the basics first rather than rushing to the system.