As AI Agents rapidly proliferate, tools like Codex, Claude Code, Cursor, and Windsurf are reshaping how we develop software. But have you encountered these pain points:
Configuration fragmentation: Team members use different tools such as Claude and Cursor, each with incompatible configuration formats, making organization-wide distribution and governance extremely difficult.
Scattered high-quality resources: Useful Skills are spread across countless GitHub repositories, driving up the cost of discovery, evaluation, and maintenance for enterprises.
Security and compliance risks: Community-provided Skills are rarely audited. Do they contain malicious code? Will they exfiltrate sensitive enterprise data such as API keys? Introducing them poses serious security risks.
Restricted intranet environments: Many enterprises operate in air-gapped or tightly controlled networks, unable to access external resources, severely limiting agent capabilities.
If million-token context windows in large models are “temporary memory,” then an agent’s memory system is the “persistent hard drive.”
We are cheering for AI’s rapidly improving ability to remember.
But few realize that we are burying invisible landmines.
Recently, industry analysts issued a blunt warning:
“AI memory is just another database problem.”
(AI memory is, at its core, a database problem.)
This is not a minor technical bug.
It is the “last-mile” crisis for enterprise AI adoption in 2026—a life-or-death battle over data.
When enterprises try to bring agents into core business workflows, they often discover—much to their surprise—that they are not building an assistant at all, but a compliance-breaking data-processing black hole.
As Large Language Models (LLMs) compress the marginal cost of code generation to a negligible level relative to human labor, the underlying logic of the software industry is undergoing a fundamental shift. This article analyzes this transformation from an economic perspective, revealing how competitive barriers are shifting from "coding capability" to "data assets," and proactively explores the profound impact of this transition on industries such as finance, law, and healthcare.
As AI Agents rapidly proliferate, tools like Codex, Claude Code, Cursor, and Windsurf are reshaping how we develop software. But have you encountered these pain points:
Configuration fragmentation: Team members use different tools such as Claude and Cursor, each with incompatible configuration formats, making organization-wide distribution and governance extremely difficult.
Scattered high-quality resources: Useful Skills are spread across countless GitHub repositories, driving up the cost of discovery, evaluation, and maintenance for enterprises.
Security and compliance risks: Community-provided Skills are rarely audited. Do they contain malicious code? Will they exfiltrate sensitive enterprise data such as API keys? Introducing them poses serious security risks.
Restricted intranet environments: Many enterprises operate in air-gapped or tightly controlled networks, unable to access external resources, severely limiting agent capabilities.
Today, ASK (https://github.com/yeasy/ask) officially arrives—and all of these problems are solved.
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