Dewu Technology has identified three multi-agent architectures: central dispatch, task boards, and advisor collaboration. The conclusion is clear: AI systems are moving away from “one model doing everything” toward specialized division of labor, but organizational complexity has not disappeared—it simply shifted to a different bearer.
What this is
Multi-agent architecture means having multiple AI programs with different responsibilities work together on long-chain tasks. The real challenge is not “simulating a professional team,” but drawing two boundaries: context boundaries determine what each Agent can see, while model boundaries determine which steps can use cheaper small models and which require stronger large models.
The article categorizes three arrangements for allocating control. In Master Agent mode, a central node decomposes tasks, assigns work, and accepts results—well suited to processes with fixed steps and clear permission requirements. In task board mode, Agents claim tasks from a shared board, providing greater parallelism and stronger fault recovery. In Advisor mode, execution Agents move work forward proactively and seek a second opinion only for high-risk decisions, specialized blind spots, or disagreements over proposed approaches.
Industry view
What the article gets right is reducing the industry’s “Agent teams” concept to engineering problems such as scheduling, state management, and result acceptance. The value of multiple Agents goes beyond stacking capabilities: small models can handle batch filtering and structured extraction, while large models handle complex reasoning and final decisions, allowing systems to balance cost, speed, and quality.
The risks are equally clear. Task board mode must handle lock contention, task reclamation, dependency release, and duplicate execution. Advisor mode depends on execution Agents correctly identifying when review is needed. As the number of Agents increases, communication costs, latency, error propagation, and troubleshooting difficulty all rise. If the underlying business process is unstable, adding more Agents will only amplify the existing chaos. So-called “autonomous collaboration” cannot substitute for clear data permissions, output protocols, and result audits.
Impact on regular people
For enterprise IT: As enterprises deploy AI, the focus will shift from choosing a model to decomposing workflows, controlling permissions, tracing logs, and building failure recovery. Multi-agent systems are not the default option; for stable processes, a single Agent or a fixed workflow is usually more cost-effective.
For individual careers: The most valuable workplace skills will increasingly involve defining complex work clearly, setting acceptance criteria, and checking outputs. AI can handle parallel information gathering and preliminary processing, but business judgment and final accountability still require clear ownership.
For the consumer market: Consumer AI products will gradually become backstage operations: users will still see one assistant, but multiple roles may be collaborating behind the scenes. Reliability will increasingly depend on whether errors can be detected—not merely on whether responses sound fluent.