Monday, December 22, 2025
SoftwareOne has announced the launch of a new GenAI multi-agent cost optimization system designed to help enterprises and partners effectively measure, manage, and optimize the cost, accuracy, and performance of AI agents. As organizations accelerate their adoption of generative AI, the company aims to address one of the biggest challenges in enterprise AI deployments: achieving measurable business value without losing control over costs and operational efficiency.
As AI agents increasingly become embedded across business functions, many organizations struggle to balance innovation with financial accountability. SoftwareOne’s newly introduced system directly addresses this gap by enabling companies to deploy the right AI agents for the right tasks, while continuously monitoring performance and spend. This approach helps ensure that AI investments scale responsibly rather than becoming fragmented or inefficient.
Importantly, the new solution builds on SoftwareOne’s long-standing expertise in both artificial intelligence and software asset management. With decades of experience helping organizations optimize software portfolios, the company brings a unique economic lens to AI adoption. This combination allows SoftwareOne to move beyond experimentation and help enterprises operationalize AI in a sustainable, value-driven way.
“With a decade of AI experience and more than 25 years in software asset management, we understand both the innovation and the economics required to scale responsibly,” said SoftwareOne Co-CEO Melissa Mulholland. “Our multi-agent cost optimization system helps clients deploy the right agents for the right jobs to ensure measurable business outcomes.”
At the core of the system is AI observability, which plays a critical role in enabling transparency and continuous improvement. Through managed services and integrated monitoring capabilities, organizations gain deep visibility into how AI agents behave, how resources are consumed, and how outcomes align with business objectives. This observability-driven approach allows teams to analyze agent performance in real time, identify inefficiencies, and refine deployments as business needs evolve.
By embedding observability directly into the AI solution stack, SoftwareOne ensures that enterprises can move from static AI implementations to adaptive, continuously optimized environments. As a result, organizations are better positioned to improve accuracy, enhance performance, and maintain governance across increasingly complex AI ecosystems.
Furthermore, the launch of this multi-agent optimization system closely follows SoftwareOne’s recent recognition by Microsoft. The company became one of the first partners to earn Microsoft’s Frontier Partner Badge, a designation that highlights organizations that place AI at the core of how they operate and deliver value. This recognition reinforces SoftwareOne’s commitment to helping customers adopt AI technologies in a structured, scalable, and business-aligned manner.
While SoftwareOne also develops AI agents, the company emphasizes its broader role as an enabler, facilitator, and operator of AI ecosystems. Rather than promoting a one-size-fits-all approach, SoftwareOne focuses on helping organizations assess cost, accuracy, and performance across different GenAI models and agents. This vendor-agnostic perspective allows customers to make informed decisions about which models best align with their specific use cases and strategic goals.
By leveraging the new GenAI multi-agent cost optimization system, organizations gain a clearer understanding of their AI landscape. They can determine which models deliver the best outcomes, where costs can be reduced, and how AI agents can be continuously optimized as workloads scale. Ultimately, this enables enterprises to shift from experimental AI usage to enterprise-grade, outcome-driven AI deployments.
Overall, SoftwareOne’s latest announcement reflects a growing industry focus on responsible AI scaling, where governance, economics, and performance are treated as equally important pillars. As AI adoption matures, solutions that combine technical innovation with financial discipline are expected to play a critical role in helping organizations unlock long-term value from generative AI.
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