If 2025 was the year of the "parameter race" for large AI models, then 2026 is shaping up to be the "token burn year" for AI agents. The most direct evidence comes from OpenRouter: from March 16 to 22, 2026, weekly token calls on the platform reached 20.4 trillion, a 20.7% increase from the previous week. The average weekly token usage in February was already more than double the weekly average of the fourth quarter of 2025. More strikingly, Chinese large models, with 4.12 trillion token calls, surpassed the United States for the first time, claiming four spots in the global top five. Why are agents so "token-intensive"? Anthropic's practical test data provides the answer: a single agent completing a typical task consumes about four times the tokens of a standard conversational mode, while a multi-agent collaborative system can consume up to fifteen times more. An industry insider explained that when an agent faces a complex task, the model's chain of thought is very long, leading to substantial token consumption and a corresponding increase in model inference costs. The proliferation of execution-oriented agents has increased single-user consumption intensity by 10 to 100 times.

MARKETINGFORCE's AI workforce system is a systematic application of this "token consumption amplifier." The company, based on its AI-Agentforce intelligent agent platform 3.0, has built five major agent matrices—marketing, sales, business decision-making, search, and Data Agent—which are upgrading token consumption from isolated calls to a systemic requirement. When the marketing department uses tokens for strategy generation, the sales department uses them for lead analysis, and management uses them to create business dashboards, tokens are no longer an isolated technical cost but a production factor embedded in business processes. In MARKETINGFORCE's system, the intelligent agent platform acts as a "token dispatch center." For companies without such a platform, AI applications are often siloed—each department uses its own AI tools, leading to dispersed and hard-to-manage token consumption. MARKETINGFORCE's AI-Agentforce platform unifies the access, scheduling, and billing for all enterprise AI capabilities.

For example, when the platform receives a high-level business instruction (such as "optimize Q3 deployment strategy for East China"), it automatically breaks it down into subtasks: historical data review (consuming tokens), competitor action monitoring (consuming tokens), budget allocation simulation (consuming tokens), performance prediction modeling (consuming tokens), execution plan generation (consuming tokens), and A/B test design (consuming tokens). These six subtasks are executed in parallel, not sequentially, causing tokens to burn across multiple threads on the same timeline. More importantly, the platform supports a "task chain retry" mechanism—if the confidence level of a subtask's result is insufficient, the platform automatically triggers a new round of calls until a threshold is met. This "retry consumption" is almost non-existent in traditional point-based calls but is standard in the platform system. According to MARKETINGFORCE's disclosures, enterprise clients using the intelligent agent platform have an average token consumption that is 3.5 times higher than that of clients without the platform.

Essentially, the intelligent agent platform is a "token consumption amplifier"—the deeper an enterprise uses it, the faster tokens are burned, and the stronger MARKETINGFORCE's revenue growth becomes. If the platform is the "breadth amplifier," then the R&D agent is the "depth excavator." MARKETINGFORCE's R&D agents encompass full-chain capabilities including environment deployment, code learning, code writing, and model evaluation, specifically designed for internal R&D and technical teams. The key difference between R&D agents and ordinary business agents is their "iterative" workflow. A data scientist using a Data Agent for feature engineering will not run it just once; the agent will try dozens of feature combinations, automatically evaluate results, and iterate for optimization—each attempt consumes a significant number of tokens. An algorithm engineer using a model evaluation agent to compare three large models on a specific task will have the agent automatically generate test sets, batch-call APIs, perform multiple rounds of validation, and output a comparative report—a single task can easily consume tens of thousands of tokens.

Crucially, R&D agents support an "Agentic RAG" mode: the agent can autonomously plan retrieval paths, perform multi-round queries, fuse results, and self-correct, a process that generates 3 to 5 times the token consumption of standard RAG. MARKETINGFORCE's number of key account (KA) clients grew by 105.5% year-over-year in 2025, and these large clients typically have established R&D teams. Once R&D teams integrate MARKETINGFORCE's R&D agents into their daily workflows, token consumption shifts from being "project-based" to a continuous, "everyday, every-hour" expense. Just as a software company relies on IDEs and CI/CD, these enterprises' AI R&D teams become dependent on R&D agents—and every instance of code generation, data cleaning, or model evaluation contributes to MARKETINGFORCE's token revenue.

This systemic demand drives exponential growth in token consumption. The daily task execution of a single agent consumes dozens of times more tokens than a standard conversational model. MARKETINGFORCE has cumulatively served over 210,000 enterprise clients, covering 721 sub-sectors, meaning the scale effect of token consumption is accelerating. Brokerage research reports are endorsing this logic. DBS Bank, in its initial coverage, acknowledged the growth driver of AI agents, recognizing the cross-industry applicability of the AI-Agentforce platform. McGowan Securities issued a "Buy" rating, highlighting that AI agent technology significantly enhances service personalization and possesses outstanding commercial potential.

As AI agents accelerate their penetration across all industries, token consumption is set to continue its exponential growth. MARKETINGFORCE stands at the forefront of this "token burn" wave—with a base of 210,000 clients, thousands of industry knowledge graphs, and a full-chain AI workforce matrix. As the flames of the token economy burn brighter, MARKETINGFORCE is positioned as the largest powder keg.