Rethinking Collaboration: Insights on Building AI‑Native Teams

Recent AI projects have proliferated across both technical and business sides, yet overall efficiency remains low because collaboration is missing; the author identifies two common fragmentation patterns: business functions split across platforms and R&D processes split across stages.

To break this "single‑point improvement, global inefficiency" cycle, the author advocates reconstructing collaboration by turning the connector from a human into an Agent that operates on a unified knowledge base . The resulting AI‑Native team forms a closed loop of "knowledge base + Agent + human", where humans set goals and make judgments, while Agents handle execution and orchestration.

The article distinguishes AI assistance (adding an AI function to an existing human‑centric workflow) from AI native (replacing the human orchestrator entirely and redesigning the workflow around the Agent). It argues that software is essentially "solidified knowledge" and that the real bottleneck is not the collaboration tool but the construction of an autonomous, up‑to‑date knowledge base.

Three‑layer model:

Knowledge base : stores business rules, processes, and data (TBox and ABox).

Agents : specialized for operations, product, development, QA, etc., each pulling knowledge from the base and performing tasks such as data retrieval, system calls, or issue diagnosis.

Human : defines objectives, makes high‑level decisions, and handles exceptions that Agents cannot resolve.

Example workflow: a business/product Agent drafts MRD/PRD using the knowledge base, a development Agent generates technical solutions, an operations Agent auto‑diagnoses alerts, and a QA Agent answers user queries, all while new decisions are fed back into the knowledge base.

The author notes that existing internal tools (e.g., Devix, Qoder series, digital‑employee platforms) are early prototypes and that the real difficulty lies in building the knowledge base, especially for legacy businesses where knowledge is scattered in code, configuration, and human memory.

Key challenges for legacy systems include:

Defining a repeatable process to construct the knowledge base from fragmented, historical architectures.

Extracting tacit knowledge hidden in experts' minds and inconsistent documentation.

Ensuring the knowledge base can self‑maintain and stay in sync with evolving business logic.

Finally, the article concludes that while collaboration tools will eventually become commoditized infrastructure, the decisive factor for AI‑driven efficiency is the ability to build and sustain robust knowledge bases that enable Agents to replace humans as the primary orchestrators.

Why split agents instead of a monolithic one? Different agents require distinct knowledge scopes, permissions, and safety requirements; isolating them allows independent evaluation, optimization, and iteration.