[Yunqi Insights] Models Are Getting Stronger, So Why Is Context Becoming More Valuable—Yunqi Conference 02

At this year’s Yunqi Conference, Qoder made a point along these lines:

Model power is a commodity. Context is the asset.

It’s worth noting that this isn’t Qoder’s official slogan—it’s a directional insight shared during their session (vendor summary; original wording subject to the on-site presentation, so don’t read too much into it). But it captures an increasingly widespread trend: as models grow more powerful and accessing them becomes easier, the genuinely scarce element of an AI product keeps shifting upward. For enterprises and complex applications, that layer looks more and more like Context.

The previous Yunqi Insights piece (Yunqi Conference 01), Section 3, already touched on the directional logic of this shift. Qoder turns code repositories into Wikis, Memory, and Knowledge Cards; QwenWork emphasizes Enterprise Context; OpenSearch highlights long-term memory and context compression. All three vendors at Yunqi are converging on the same direction. This article isolates Context for a closer look—examining how it evolves from a one-off Prompt attachment into a long-term asset for AI systems, and what new engineering, governance, and organizational challenges it brings along.

1. Models Know the World, But Not “How We Actually Do Things Here”

General-purpose models have mastered vast amounts of public knowledge and can perform increasingly complex reasoning. Yet what enterprises actually want these models to handle is often heavily dependent on local information.

A Coding Agent needs to understand the current codebase’s architecture, conventions, historical bugs, module relationships, and deployment processes. An enterprise Agent needs to know the organizational structure, permissions, SOPs, project status, customer information, internal documentation, and business rules. An e-commerce content Agent needs to know Brand Guidelines, SKU details, product authenticity constraints, target market, historical ad performance, and platform policies. A Research Agent needs to know what has been searched before, which sources are trustworthy, which judgments have been overturned, and what the current evidence standards are for the research task at hand.

This information does not automatically fill in just because the model is upgraded.

Therefore, a large number of Agent products have started focusing on “how to build a continuously available Context.” Context is no longer an attachment to a one-off prompt, but a system that requires long-term maintenance.

The most common failure pattern we’ve seen in enterprise AI pilots confirms exactly this: the model is clever every single time, but the entire system is笨 (clumsy/incoherent). Files need re-uploading, brand guidelines need re-explaining, project context needs restating, and historical decisions need recounting. Remove this friction, and the model’s capabilities finally start delivering real value.

2. Qoder’s Context Engineering: How the Knowledge Engine Is Redefining the “Codebase”

Traditionally, a codebase was understood primarily as files and directories. In the AI Coding era, a codebase increasingly needs an additional layer of machine-readable semantics.

Several components that Qoder showcased on-site each take on different Context Engineering responsibilities (vendor-summarized; refer to vendor documentation for original descriptions): Repo Wiki forms project structure and module documentation, Knowledge Graph expresses dependencies and contracts between modules, Memory preserves cross-session constraints, preferences, and history, and Knowledge Cards organizes task-relevant information into Context packages that can be directly fed to an Agent.

More worth migrating is the judgment behind these four considerations—once Context is engineered, every new task begins with a high signal-to-noise Context package, rather than starting from a source code repository that gets read over and over again.

If an Agent has to rescan all the code, re-read all documentation, and re-guess the architecture every time it picks up a task, then even the most capable model will waste massive computation, and the conclusions will be wildly inconsistent. A more sensible structure is:

Raw Data → Structured Knowledge → Task-specific Context → Agent

Task-specific Context extracts only what the current task actually needs, complete with provenance, version, and constraints.

After the AutoNavi (Gaode) team turned their domain knowledge from millions of lines of code into recallable assets, their one-time task pass rate jumped from 37.3% to 61.5%. This is the engineering evidence for Context-as-Asset (data from vendor case study, measured results of Qoder Knowledge Engine at this client; the same argument appeared in Section 6 of “Cloud Computing Observation 01” under organizational-level judgment).

III. QwenWork: Scaling Context from Codebase to Enterprise

QwenWork’s showcase at the Yunqi Conference demonstrates how context extends beyond codebases into broader enterprise operations.

Legal Document Fill Out and Marketing Content Generation appear straightforward on the surface. The real differentiator lies in how the Agent accesses a company’s own rules and resources.

Without knowledge of enterprise templates, approval workflows, contract fields, and access controls, a legal document Agent can only produce something that looks plausible. Similarly, a Marketing Agent lacking brand assets, historical campaigns, target markets, brand voice, and product details will constantly need users to re-explain the context.

This friction represents the most noticeable pain point with most AI tools today: users repeatedly provide the same context across sessions. (Industry observation from the Yunqi Conference press release.)

An AI Workspace that delivers long-term value should progressively transform these repeated explanations into persistent assets. Files shouldn’t require re-uploading every time. Brand requirements shouldn’t need restating. Project backgrounds shouldn’t need re-explaining. Historical decisions shouldn’t need recounting. Models can be swapped, but systems shouldn’t reset to zero each time.

IV. The Value of Context Comes from “Continuous Accumulation,” Not “Fitting More In”

A common pitfall when discussing Context is assuming that larger context windows are inherently better and that the solution is simply to fit all available information inside.

In real systems, more Context doesn’t automatically translate to better outcomes. Excessive irrelevant information inflates Token costs and dilutes attention density. When multiple versions of a document coexist, models may struggle to determine which version’s rules remain authoritative.

So the real challenge in Context Engineering boils down to two questions: what to preserve and what to discard.

What to preserve — chat history doesn’t automatically constitute long-term memory. What deserves preservation are decisions, constraints, evidence, failure causes, stable preferences, and reusable methodologies. A payment system modification doesn’t require the entire marketing knowledge base, and SEO research certainly doesn’t need every server log.

What to Forget — Context must have version, timestamp, source, and status. If a deprecated architectural decision continues to be invoked by an Agent, long-term memory will amplify errors rather than mitigate them. Long-running tasks require continuous summarization and reorganization of context, preserving critical state while discarding details that have become stale.

This is precisely why the Yunqi OpenSearch Agentic Search Forum at the Cloud Town (Yunqi) conference emphasized Task Memory, Long-term Memory, and Context Compression. The “Retrieval-Action-Memory-Knowledge” self-reinforcing loop framework that Alibaba Cloud OpenSearch presented on-site (vendor’s summary, based on the official Yunqi press release) is essentially answering the same question: Which Memories should be kept for extended use, which should be compressed when a task concludes, and which have expired and must be actively forgotten.

5. Memory Is Far More Than “Remembering What Users Said”

A December 2025 survey titled Memory in the Age of AI Agents—co-authored by 46 researchers from Tsinghua University, the National University of Singapore, Fudan University, and other institutions—proposes a more rigorous framework. Memory, the paper argues, should no longer be crudely divided into short-term and long-term. Instead, it must be characterized along three joint dimensions: Forms (Token-level, Parametric, or Latent), Functions (Factual, Experiential, or Working), and Dynamics (Formation, Evolution, or Retrieval). This represents the latest scholarly articulation of the Context-as-Asset paradigm.

Many AI products treat Memory as user preference storage—remembering language choices, names, or preferred formats. While useful, this falls far short for Agents.

True compound-value Memory operates closer to what we call Task Memory.

After a complex task wraps up, the system should know: how the task was ultimately decomposed; which search paths proved effective; which tools failed; which sources are trustworthy; which result the user accepted and why; which steps could be abstracted into a Skill; and which errors to avoid going forward.

If you’re just embedding the entire chat history and pulling it up in the next round, Memory tends to degenerate into a massive archive of historical text. The “relevant snippets” retrieved often aren’t actually relevant, which increases the likelihood of the model getting sidetracked.

What Memory really needs is distillation, evaluation, and structuring. Otherwise, as Context accumulates, the next decision becomes less—not more—certain.

Six、Context Can Create New Lock-in — The Anti-lock-in Five-Question Selection Checklist

The more critical Context becomes, the more you need to guard against new forms of platform lock-in.

When all of an enterprise’s historical decisions, Workflows, Agent Memory, Skills, and user feedback accumulate within a closed platform, migrating models may be straightforward—but migrating Context is hard. This represents a subtler, long-term cost that goes beyond model switching.

When we help clients with technology selection, we always ask one pointed question: “If you had to replace this platform three years from now, could you take your Context assets with you?” Any solution that can’t answer that question is worth approaching with caution.

Here is the English translation:

Five Questions to Determine Whether an AI Platform Will Lock You In

To assess whether an AI platform will trap your organization, start by asking these five questions:

Question 1: Can you export?
Can your Task History, Decision Log, Knowledge Base, Skill Definition, Evaluation Result, Tool Configuration, and Permission Mapping be exported in standard, interoperable formats? This determines whether switching platforms means migrating your data or rebuilding from scratch.

Question 2: Is there version control?
Does the exported Context include version numbers, timestamps, provenance, and status information? A Knowledge Card without timestamps will be meaningless three years down the road—you’ll have no idea why it was created in the first place.

Question 3: Is it model-agnostic?
Can your Context be consumed by different models? If a Context can only be interpreted by a specific model, it’s still effectively tied to a single vendor.

Question 4: What about data egress and compliance?
If your Context is stored on servers outside your jurisdiction, does this trigger data cross-border transfer approval requirements, personal information protection obligations (under regulations such as GDPR, CCPA, or PIPL), or data localization mandates for heavily regulated industries? If you fail this check, the other four points become moot.

Five Questions on Governance Responsibility. Who is accountable for Context quality? Who has the authority to modify it? Who decides when to retire stale content? Without clear governance, the accumulation of Context becomes an organizational liability rather than an asset.

The core principle underlying these five questions is straightforward: Models can be swapped out, Runtimes can be replaced, but Context assets must remain under your own control. This may well emerge as the new architectural boundary for AI-native enterprise software.

7. Context Manifests Differently Across Four Industries

The discussion above focused on general patterns. Now let’s apply these principles to specific industry contexts.

Telecommunications — Requirements like plan changes, enterprise dedicated lines, and cross-region billing must traverse four or five domains: BSS, OSS, CRM, and compliance auditing. While AI might double the speed of writing application-layer code, middleware adaptation, reconciliation logic, and compliance approvals remain untouched. Here, the focus of Context accumulation isn’t code repositories, but historical billing anomalies, compliance definitions, and reconciliation rules — the kind of Context that barely exists in public datasets and represents a genuine competitive moat for enterprises.

Financial Banking — Core systems, risk control, anti-money laundering (AML), and explainable auditing. The defining characteristic of this pipeline is that every change must be explainable, auditable, and traceable. AI can draft a risk control rule in seconds, but getting it into the rule engine requires passing model validation, explainability testing, regulatory alignment, and internal approval. The Context repository here must satisfy cross-border data transfer compliance (Standard Contractual Clauses for personal information, Personal Information Protection Law assessment) and local data center requirements. Without meeting these two prerequisites, all preceding Context assets become unusable.

Manufacturing — MES, ERP, QMS, and reporting systems. The previous section highlighted how AI Coding in manufacturing most commonly fails at “works in demo, breaks in integration.” The same principle applies to Context: workshop knowledge, equipment parameters, collection standards, PLC interfaces, vision system versions — most of this Context resides in the heads of veteran engineers, aging PDFs, or partially updated spreadsheets. If no team takes ownership of “domain knowledge repository quality,” the Context available to AI will quickly become stale or contradictory. This is where the five-question checklist from the previous section finds its most concrete application in manufacturing.

E-commerce—peak season preparation, inventory consistency, fraud prevention, cross-border reconciliation. In this sector, the Context that AI receives consists of brand guidelines, historical assets, platform rules, and campaign post-mortems. This type of Context has the shortest shelf life—logic from a hit material three months ago may be completely obsolete by the next major sale. Therefore, the focus of Context governance in e-commerce isn’t “accumulation,” but rather “retirement cadence.”

The four industry types exhibit different Context patterns, yet they share a common conviction: the organizational governance of Context assets is more foundational than tool selection.

VIII. What Truly Deserves Accumulation Is Information That Improves Future Judgment and Execution

If we push the statement “Context is an asset” further, we arrive at a stricter criterion: more preservation doesn’t automatically mean more assets.

Only information that can reduce uncertainty in the next task, minimize repetitive exploration, enhance result stability, and improve decision quality truly constitutes an asset.

So when we design AI products going forward, we often ask additional questions during architecture reviews with clients:

What does the system leave behind after completing each task? Is it merely results, or also reusable methodologies and failure records?

Can it be reused directly next time? If every instance requires re-explanation, reuse remains nothing more than rhetoric.

Which conclusions have been validated? Experience that hasn’t been verified tends to make AI repeat the same mistakes.

Which failures have been systematically recorded? A Context without failure records is incomplete.

If you switch to a different model, does the value you’ve accumulated carry over? This extends the Anti-lock-in framework from the previous section: only when Context survives a model switch does it count as a true asset.

Models will keep improving, API costs will keep dropping, and today’s impressive capabilities may soon become commoditized infrastructure. The truly compounding value often lies outside the model itself: your organization’s own Context, validated Workflows, and the judgment and feedback you’ve built up over time.

Implications for Decision Makers: 3 Things for Next Quarter

For the CFO—Shift the conversation from “how many hours did AI save?” to “how much reusable Context did each completed task generate?” For the same type of task, Context reuse rates can vary by 3–5× depending on which platform you use. This metric gets closer to true ROI than raw call counts, and it points more directly to long-term asset building.

For CIOs and CDOs — This quarter, swap out “model benchmarks and token pricing” as your AI platform selection criteria and replace them with an Anti-lock-in Five-Question Checklist (export, versioning, model-agnostic, compliance, and governance accountability). Roll in one or two of these criteria, and within a quarter or two, your organization will naturally start demanding controllable Context. Skip the criteria swap, and three years down the line, the biggest cost won’t be your model bill — it’ll be the engineering hours spent migrating Context.

For business leaders — Assign one person or a team to own “domain knowledge preservation quality.” The real takeaway from the Gaode case in the previous section isn’t that a tool got deployed; it’s that someone took responsibility for the quality of Context preservation. Hand a tool to a team and nobody owns the Context quality, and you’re probably looking at half the expected impact.

Common Questions

Q1: Context assets sound great, but how are small and mid-sized companies supposed to have the bandwidth for active preservation?

It’s not about running a dedicated operation — it’s about weaving preservation into existing workflows. Every time an issue gets closed, every requirements review, every post-mortem — add a couple of sentences on why you went that route and what pitfalls you hit. Over a year, that’s hundreds of thousands of words of institutional memory. The real barrier isn’t hours in the day; it’s whether you’re willing to treat these as formal deliverables rather than “documentation perfectionism.”

Q2: Agent platforms are all pushing Memory and Knowledge Cards—are these just old wine in new bottles?

Some of it is repackaged old stuff, but some directions are genuinely new. Task Memory turns execution history into a retrievable asset, and Knowledge Cards structure domain knowledge—things that Prompt Library never solved. The test is whether it can answer: “Who used this Memory last, in which task, and why was it accepted or rejected?” If it can’t, it’s probably just the same old wine.

Q3: In the Anti-lock-in Five Questions, isn’t “model-agnostic” too idealistic? In reality, different models vary wildly in capability, and switching always degrades quality.

Yes, quality will drop in the short term. But the question isn’t “Can you switch at zero cost?” It’s “Is the switching cost locked in by a single vendor?” If you can export, convert formats, and preserve versions, switching becomes a calculable engineering problem. If you can’t export, switching becomes an uncontrollable business risk. These are two completely different things.

Self-Reflection

Don’t romanticize this piece to the point of fiction. Three things need to be honest:

First, this article overlaps significantly in direction with the previous “CloudScene Insight 01”—Qoder Knowledge Engine, QwenWork Enterprise Context, OpenSearch Task Memory, Gaode team’s pass rate improvement from 37.3% to 61.5%, and the concept of context assetization all appear in both pieces. This article has been restructured (with the five anti-lock-in questions moved forward to Section 6, governance responsibility and compliance dimensions added, and four-industry perspectives included), but readers going through both articles consecutively may find them somewhat familiar. I’ll avoid this overlap when writing CloudScene Insight 03.

Second, the article relies heavily on vendor case studies. The three key arguments—Gaode, QwenWork Legal Document Fill Out, and OpenSearch Agentic Search—all come from on-site vendor summaries or vendor documentation at CloudScene, giving the piece a vendor-leaning perspective. I’ve annotated each instance in the citations section and have cross-validated the arguments against third-party academic reviews (such as Memory in the Age of AI Agents, arXiv:2512.13564) wherever possible.

Third, the Anti-lock-in Five Questions are currently in design state—not yet a validated metric system. To put them into practice, you’ll still need to add: specific measurements for each question, minimum industry thresholds, and precise compliance references. What this article provides is a directional framework for assessment, not a compliance checklist. We’ll flesh these out further when co-creating with clients next time.

Citations (Source + Evidence Level + Position)

#	Statement in the Text	Source	Date	Attribution	Evidence Level	Position
1	“Model power is a commodity. Context is the asset.” (general meaning)	Qoder live presentation (vendor summary; original wording subject to on-site confirmation)	2026-09-24	Qoder Team	Vendor Claim	Vendor Position
2	Qoder Knowledge Engine includes Repo Wiki / Knowledge Graph / Memory / Knowledge Cards	Qoder Knowledge Engine introduction page + cross-verified with the third section of “Cloud Intelligence Observation 01”	2025-2026	Qoder Team	Vendor Claim	Vendor Position

| 5 | OpenSearch Agentic Search “Retrieval—Action—Memory—Knowledge” Self-Loop + Task Memory / Long-term Memory / Context Compression | https://xie.infoq.cn/article/163b700cba024c8adb326ec5c（InfoQ Cloud Town 2026 Coverage）+ https://docs.opensearch.org/3.6/vector-search/ai-search/agentic-search/agentic-memory + https://opensearch.org/blog/unpacked-at-open-source-summit-na-2026-inside-opensearchs-massive-leap-into-the-agentic-era/ | 2026-09 | Alibaba Cloud OpenSearch Team / OpenSearch Project | Verified Facts (Vendor Release + Third-party Coverage) | Joint Vendor / Third-party |

| 9 | Data Export / PIPL / Localized Data Center Requirements for Heavily Regulated Industries | PIPL Articles 38–39 + Measures for Security Assessment of Data Export + Financial Sector’s Stringent Regulatory Requirements (public regulations) | 2021–2026 | Cyberspace Administration of China (CAC) / People’s Bank of China (PBOC) / National Financial Regulatory Administration (NFRA) | Verified facts (regulations) | Regulatory stance |
| 10 | Context Pattern Variations Across Four Industries (Telecom / Finance / Manufacturing / E-commerce) | Industry observations in this article (based on anonymized client cases + public vendor cases) | 2026 | Article author + synthesis | Industry observations (anonymized) | — |
| 11 | “If you switch platforms three years from now, can you take your Context assets with you?” | Judgment-based question in this article (based on cross-industry AI platform migration experience) | 2026 | Article author | Author’s analysis | — |
| 12 | Manufacturing Sector Phenomenon: “Works On-site, Fails During Integration” | Section 6 of the previous “Cloud Town Observations 01” + Haier / Wens case studies (public vendor cases) | 2025–2026 | Qoder / Alibaba Cloud Lingma / Haier / Wens | Verified facts (vendor cases, anonymized extensions) | Vendor / customer joint |

Localization Key Points (Multi-language Translation Reference, IAIUSE Multilingual Strategy · 2026-08-09 Guidelines)

When translating into 19 languages, replace the following content according to target language market localization; structure/visual formatting remains unchanged:

Original (Chinese)	English Version	Japanese Version	German Version	Arabic Version
Qoder / Alibaba Cloud products	Qoder / Alibaba Cloud (keep product names)	Qoder / アリババクラウド	Qoder / Alibaba Cloud	Qoder / علي بابا كلاود
Feishu / DingTalk	Slack / Teams	Slack / Teams / Lark	Slack / Teams	Microsoft Teams
China Telecom / China Mobile / China Unicom	AT&T / Verizon / T-Mobile	NTT / KDDI / SoftBank	Deutsche Telekom / Vodafone	STC / Etisalat

Note: Except for the localization items specified above, global products/concepts in the text (Repo Wiki, Knowledge Graph, Task Memory, Skill, Context Engineering, Anti-lock-in Five Questions) remain in their original English terms. The other 15 languages follow IAIUSE’s three-tier approach: the priority 5 languages (Chinese/English/German/Japanese/Arabic) are localized per the table above; the 9 supplementary languages (Spanish/French/Portuguese/Korean/Russian/Italian/Dutch/Polish/Turkish) retain original Qoder/QwenWork names while replacing representative enterprises with local equivalents; the optional 5 languages (Swedish/Thai/Vietnamese/Ukrainian/Indonesian) retain original names as placeholders.

About This Series

“Yunqi Observations” is an industry field series from IAIUSE,出发点是从 the 2026 Yunqi Conference (Alibaba Cloud’s flagship tech summit in Hangzhou). It uses a researcher’s lens to拆解 the real changes happening in the AI industry—avoiding hype, focusing only on where bets are being placed and the strength of evidence.

If you’re evaluating where enterprise AI should切入, which Context assets are worth prioritizing, and which prompts will be drowned out by model upgrades, feel free to reach out. We offer three services—Enterprise Training (AI-era R&D and operations team transformation, 2-3 day workshops where you walk away with Context asset inventories, Anti-lock-in’s five questions, and measurement frameworks), Specialized Consulting (from Context asset inventories and workflow redesign to measurement systems—helping you convert “model capabilities” into “organizational capabilities”), and Executive Briefings & Industry Talks (decision-maker perspectives on AI Context realities and Anti-lock-in selection). If you just want to explore directions over 90 minutes, book a lightweight conversation. Contact: [email protected].

Further Reading: The Seven-Step AI Transformation Framework, which systematically covers the complete path to enterprise AI implementation.

The series covers approximately 10 articles on system layers above models, Agent deployment, Context assets, enterprise AI organizational design, and the migration of AI product competitive units.

This series has accumulated over 200 publicly available research papers and industry cases. The evidence for this article draws from three levels: on-site vendor presentations (Qoder / QwenWork / OpenSearch), third-party independent research (Memory in the Age of AI Agents, arXiv:2512.13564, and other academic reviews), and anonymized client cases. Vendor cases are overrepresented; their positioning has been noted in the citation section.

I bring nearly 8 years of experience in large enterprise consulting and business analysis, having worked at IBM on telecom, financial, insurance, and manufacturing projects. Since then, I’ve remained on the front lines—in operator products, internet applications, and AI development—handling requirements analysis, product design, and cross-team implementation. This account is actually maintained by a small team: myself and one or two long-term collaborators, each responsible for different tracks: AI coding tool research, organizational governance case studies, and coaching dialogues. Most of the projects “we’ve guided companies through” were delivered jointly by our team.

The assessments in this series come from my on-the-ground observations and cross-industry validation. They reflect a clear author perspective and do not represent the views of any vendor.