智能体是个伪市场｜爱分析访谈

The agent market is currently experiencing a hype boom, yet it suffers from overgeneralization and disorder: many vendors simply slap an "agent" label on existing products that have just been connected to a large language model. This interview clarifies the core nature and practical deployment of enterprise agents, with key takeaways as follows:

1. What enterprise customers truly need from agents is not a new conversational portal, but a "digital employee" that can understand corporate knowledge, decompose complex tasks, call tools and coordinate execution. Agent products divorced from foundational model capabilities deliver no real value.

2. Two core capabilities are required for successful enterprise agent deployment: first, building an enterprise-level memory mechanism that converts a company’s various materials, processes and experience into a dynamic corporate knowledge network; second, possessing long-task planning capabilities to support the execution of complex business operations.

3. AI will restructure corporate organizational forms. For technology companies, core competitiveness will shift from experience accumulation to the ability to learn and adapt quickly, and corresponding job roles will also transform accordingly.

This interview reveals the latest trends in AI enterprise deployment and provides clear guidance for brands planning AI-driven digital transformation, with key insights as follows:

1. Brands must screen carefully through market hype when purchasing agent products. Currently, most vendors claiming to offer agents lack core model capabilities and are essentially still traditional system integrators unable to meet enterprises’ complex business needs. Buyers should prioritize evaluating a vendor’s model training capabilities.

2. When introducing agents, focus on two core deployment capabilities: first, whether the agent can generate a dynamic knowledge network based on the brand’s own business materials, user data and operational experience to form exclusive enterprise-level memory; second, whether it can support long-task decomposition and multi-skill coordination to adapt to the brand’s real business scenarios.

3. AI digital employees can replace a large volume of repetitive low- to mid-skilled work, such as pre-sales proposal generation, data governance and project management, to significantly boost efficiency. Brands can start preparing for internal organizational transformation to adapt to this new working model.

The booming enterprise agent track brings new opportunities for B2B service sellers, but also hides risks. Key takeaways are summarized below:

1. Risk warning: The agent concept is currently overgeneralized, with many players simply jumping on the bandwagon and slapping on labels without core model capabilities. These players are unlikely to survive the upcoming industry shakeout. Sellers should not blindly follow the hype and neglect building core capabilities.

2. Opportunity direction: In the long run, two high-value tracks stand out. The first is exclusive large models for enterprises, which focus on enterprise ontology modeling and dynamic knowledge network generation to replace the traditional manual work of sorting business data. The second is enterprise agent collaboration platforms, which solve the problem of coordinated scheduling across multiple models, multiple skills and multiple AI employees.

3. Recommended practice path: DeeperTechnics’ (DiPU Tech) dual-track layout is worth learning from. The company builds core capabilities with an enterprise large model on one track, and delivers scenario-based deployment with an agent collaboration platform on the other, while adjusting its internal organization to improve learning and adaptability around the new strategic direction.

The development of enterprise agents brings new insights and directions for factories advancing digital transformation. Key insights are summarized below:

1. Enterprise agents can improve efficiency for many complex scenarios in factory production and operations. For example, for complex problems such as equipment failure and maintenance, agents can build an exclusive knowledge network based on the factory’s accumulated historical failure data and equipment operation logic, and solve complex practical problems far better than traditional manual handbook-based Q&A.

2. When selecting AI service providers for digital transformation, factories should prioritize evaluating two core capabilities: first, whether the provider can help the factory convert its own production processes, process experience and failure data into a dynamically updatable enterprise knowledge network to build enterprise-level memory; second, whether it can support long-task planning and multi-tool coordination to meet the actual needs of the factory’s complex business.

3. Enterprise agents can replace a large amount of manual work in data and process sorting, greatly reducing the labor cost of digital transformation and improving deployment efficiency. Factories should pay close attention to deployment progress in this field.

This interview clarifies development trends, core customer pain points and feasible solution directions for the enterprise agent track, offering high reference value for AI service providers. Key insights are as follows:

1. Industry development trend: Agents will not become an independent market. In the long run, industry competition will return to underlying capabilities. The real core value lies in enterprise large models and agent collaboration platforms. Service providers without proprietary model capabilities remain essentially traditional integrators, and are unlikely to survive long-term.

2. Core customer pain point: Most agent products on the current market remain just conceptual, lacking enterprise-level memory and long-task planning capabilities—they can only handle simple Q&A and fail to meet enterprises’ real needs for processing complex business, leading to widespread supply-demand mismatch.

3. Solution reference: Service providers can draw on DeeperTechnics’ (DiPU Tech) dual-track product strategy. On one hand, build exclusive enterprise large models to deliver core capabilities including enterprise ontology modeling, knowledge generation and storage, and reasoning planning. On the other hand, build an agent collaboration platform responsible for coordinated scheduling of multiple models and multiple skills. Together, the two modules meet the needs of full-scenario enterprise deployment.

The deployment and development of enterprise agents puts forward new requirements for AI platforms, and also clarifies development directions and risks for platform providers to avoid. Key takeaways are summarized below:

1. New market demand: Currently, enterprises’ core demand for agent platforms has shifted from simple tool calling to deep collaboration across multiple models and multiple skills. Traditional platforms that rely on manually preset workflows cannot meet enterprises’ needs for handling complex tasks, so platforms need to adjust their capability focus.

2. Platform development direction: Agent platforms need to transform into a collaborative decision layer. Backed by an enterprise large model that provides memory and planning capabilities, the platform is responsible for automatically scheduling various models and skills and generating execution paths based on business task goals, replacing the traditional manual preset workflow model.

3. Risk warning: The agent concept is currently overgeneralized. Platforms should not blindly jump on the hype, and should instead focus on polishing underlying capabilities and aligning with real enterprise needs. Platforms without core model capability support are unlikely to survive future competition, and long-term competition will ultimately center on underlying capabilities such as enterprise semantics, memory and planning.

This interview puts forward new problems, new trends and new business model directions for the current development of the enterprise agent industry, offering high value for research on AI industry deployment. Key insights are summarized below:

1. New industry problems: The current agent industry is plagued by conceptual hype and overgeneralization. A large number of vendors simply complete simple system integration and slap on an agent label, making them essentially no different from traditional integrators. This interview clarifies the core boundary of agents, points out that agents cannot exist independently of model capabilities, and exposes the current industry disorder.

2. New industry trends: The mature deployment path for enterprise agents is gradually clearing up. A complete deployed system consists of two core modules. The first is an enterprise large model with enterprise-level memory and long-task planning capabilities, which is responsible for generating the enterprise’s dynamic knowledge network. The second is an agent platform responsible for multi-agent coordinated scheduling. Only the combination of the two can support full-scenario deployment.

3. New directions for business models and organizational design: Unlike the pure token-selling model, the future core value will lie in increasing the business value added per token, by providing an enterprise-level semantic foundation to improve the production efficiency of all connected models. At the same time, AI will restructure enterprise organizations, shifting core competitiveness to the ability of rapid learning and adaptation.