During the 2026 World Artificial Intelligence Conference, DEEPEXI TECH held its user conference in Shanghai. The company officially opened the first public test of its DeepWorks enterprise agent platform, upgraded its core DeepexiOS product system, and completed strategic agreements with multiple partners and clients. In a subsequent small media exchange, the founder and chairman shared his views on the future competitive landscape of the Agent market.
He believes that while foundational models remain a significant variable, the gap between models may gradually narrow as the capabilities of open-source models continue to strengthen. For deep enterprise application scenarios, there are three truly critical competitive factors for Agents.
Essential Competitive Factors
The first is memory. Large enterprises often possess vast knowledge systems spanning from blueprints and processes to quality management. Whether an Agent can establish effective organizational-level memory, weaving dispersed corporate knowledge into a complete network, is fundamental to solving complex tasks.
The second is collaboration. Complex tasks within enterprises typically require invoking multiple Skills, accomplished by different "experts." The ability of multiple Agents to decompose and coordinate tasks based on a unified enterprise semantic foundation will also become a crucial competitive capability.
The third is auditability and traceability, essentially whether an Agent can be held "accountable." When Agents participate in serious business tasks, enterprises need not only to see the final result but also to know what data was called, which steps were taken, and on what basis judgments were made.
Market Dynamics and Long-Term Logic
In the short term, market competition may still focus on acquiring customers and traffic, but the logic of the To B market differs from that of the To C market. The enterprise market is not about first attracting users; it is about genuinely solving customer problems, getting the product used within one company, and then gradually replicating that success with more clients.
Looking further ahead, the competitive logic of the AI era is also changing. The true business model in the AI era might be forming "intelligent compound interest" in sufficiently complex domains. This refers to an Agent continuously accumulating data, knowledge, and experience while handling business processes, thereby forming a continuously enhanced intelligent capability in a specific field.
This is why DEEPEXI TECH is focusing on industrial scenarios. The industrial sector not only has high data modality barriers but also involves complex professional knowledge and business logic. In contrast, Skills for office tasks like PPT and Word are easier to standardize and have relatively lower entry barriers. Office agents may soon enter a fiercely competitive "red ocean."
The founder argues that only by entering sufficiently complex domains can a company's data and knowledge continuously accumulate through daily work and transform into ever-growing intelligent capabilities. Foundational models address the "IQ" problem; the enterprise semantic foundation and knowledge logic determine whether the system can truly understand the business. Continuous work accumulation drives the system's evolution, solving the core problem of work capability.
Evolving Enterprise Demand
Regarding current enterprise market demand, a company co-founder added that since large models gained widespread attention, top executives and managers at all levels have generally recognized the potential role of large models. However, over the past year or so, corporate understanding swung to an extreme, believing large models could seemingly do anything.
Currently, the development of enterprise-level Agents is gradually returning to rationality. Rather than discussing "which positions will be replaced," companies are now more concerned with "improving the efficiency of which positions" and precisely which core tasks an Agent can solve. This has become an important criterion for enterprises when deciding whether to adopt Agents.
Furthermore, accuracy and rigor remain the most core considerations for enterprises using AI. Companies need to confirm whether the results delivered by an Agent are trustworthy, whether the entire information source is transparent and can achieve a "white box" state, and ensure all outcomes are deliverable, auditable, traceable, and accountable.
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