GTHT has released a research report stating that FDE is responsible for deeply understanding business scenarios and accumulating industry experience, while Harness ensures stable agent execution and capability reuse. Their collaboration is expected to enable more efficient and replicable large-scale deployment of enterprise AI. GTHT believes that as enterprise AI moves from demo to production, the industry's value is likely to shift from pure model capabilities to a combination of "business understanding, engineering delivery, agent runtime, and platform reuse." It recommends focusing on vendors with deep industry-specific know-how, FDE delivery capabilities, and agent platform accumulation.
The key points from GTHT are as follows:
FDE is reshaping the delivery model of enterprise AI from product to production.
FDE (Forward Deployed Engineer) is not a traditional pre-sales or implementation role. Instead, it recombines capabilities such as business understanding, scenario identification, prototype validation, system integration, production deployment, and effect evaluation, connecting the client's business site with product development. As AI enters core enterprise workflows, it must adapt to differentiated data, systems, permissions, rules, and organizational processes, increasing the demand for deep customization. Meanwhile, AI coding and agent tools significantly reduce the cost of custom development, making the previously economically less viable "heavy delivery" model scalable again. The true scaling of FDE does not rely on continuously adding personnel, but on converting on-site experience into skills, connectors, industry templates, and platform capabilities, continuously reducing the per-client human investment.
Harness becomes key infrastructure for Agent capability evolution, with model competition shifting from point capabilities to system engineering.
Harness is essentially an Agent Runtime wrapped around the base model, responsible for context management, memory management, tool invocation, state maintenance, permission control, result validation, exception recovery, and long-term task management, organizing a single model call into a continuously running Agent. As Agents move from single-turn Q&A to multi-step, cross-system, and long-duration task execution, risks such as error accumulation, state drift, tool misuse, and security issues increase, making the importance of Harness grow. The focus of Harness competition is expected to shift from "how many components are integrated" to improving real task outcomes with reasonable runtime overhead. "Cost per Successful Task" is likely to become a more core metric for Agent productivity than single token price.
FDE × Harness creates an AI commercialization flywheel, moving enterprise AI from "human replication" to "capability replication."
FDE solves "business understanding and implementation," while Harness solves "stable execution and capability accumulation." Combined, they can continuously encode the tacit business knowledge, process rules, and engineering experience from the enterprise site into executable Agent capabilities. FDE enters the client site to acquire high-value business knowledge, and Harness converts it into skills, tools, workflows, ontologies, and operating rules. The production environment further generates real tasks, failure cases, and user feedback, which in turn feeds back into the iteration of FDE and Harness. As common experience accumulates, new client deployments shift from starting from scratch to configuration and reuse, ultimately forming a commercial flywheel where deployment speed increases, marginal delivery costs decrease, and revenue growth outpaces the growth in delivery personnel.
Risk warnings: Risks of slower-than-expected enterprise AI commercialization and large-scale deployment; risks of slower-than-expected downstream client AI budgets, scenario expansion, and ROI realization; and risks of intensified industry competition and rapid technological changes.