Emerging AI software infrastructure proxy market

From SaaS to agentic AI Digital and Analytics/ArticleMarch 10, 2026 Between the late 1990s and the mid-2010s, the rise of software-as-a-service (SaaS) fundamentallydismantled the on-premises application market. Early SaaS pioneers such as Salesforce (founded in 1999)demonstrated that multi-tenant, subscription-based delivery could outperform licensed, install-and-maintain software on cost, speed, and scalability. By the late 2000s, SaaS adoption accelerated rapidly as Much like the industry’s transition from on-premises software to SaaS, agentic AI will also usher in a newsoftware delivery model, leading to very different underlying software infrastructure (see figure 1). Asoftware infrastructure model is beginning to take shape for agentic AI, providing the foundational The evolving agentic AI software infrastructure The agentic AI software infrastructure market remains nascent and rapidly evolving. While definitive,mature platforms akin to today’s cloud databases or container orchestration layers do not yet exist, clearpatterns of capabilities are crystalizing. These early patterns provide a valuable lens into how investors, At its core, agentic AI infrastructure must support a set of emerging capabilities that extend well beyondtraditional AI platforms. First, solutions must be capable of running in secure, standalone environmentsthat meet enterprise compliance and data governance requirements. Second, they must support dynamicdiscovery and integration with external services and data sources, enabling agents to act on up-to-datebusiness context. Closely related is the ability for agents to discover and collaborate with one another,sharing work and knowledge rather than operating as isolated silos. Persistent memory, both short-term The emerging landscape of agentic AI infrastructure can be logically decomposed into a series offunctional layers (see figure 2): •Agentic platforms, including anthat provides the core execution layer whereagentic AI runtimereasoning, goal management, dependency resolution, short-term memory, and behavioral logic reside.This is the locus of autonomous decision-making. Complementing this is anagentic AI orchestration •MCP (Model Context Protocol) runtime and servers,which allow agents to interact with external services and standardized APIs, acting as gateways and registries for MCP servers that agents access.1•Agentic context stores, including context and long-term memory infrastructure that persistsknowledge and evolving agent state across interactions and operational cycles, in addition toknowledge •Agentic market capabilities, including anto make agents readily discoverable,agent registryagentto allow users to browse and manage available agents, andthat acceleratecatalogsthird-party agents •Operations, governance, and security managementtooling that provides observability, healthmonitoring, life cycle controls, oversight, and security management for production-ready agents.•Financial and commercial managementlayers that enable cost allocation, showback/chargeback, Each of these can be deployed onto either an underlying cloud or robotics physical infrastructure.Together, these elements represent the architectural primitives required for any organization making a From a market perspective, the broader agentic AI segment is forecast to experience rapid growth.Several industry forecasts project the global agentic AI market to expand from the mid-single-digit billionstoday to the tens or even hundreds of billions by the early 2030s, with compound annual growth rates frequently above 40 percent depending on the scope of included capabilities. This growth is underpinned In the sections that follow, we drill into each major architectural layer and capability area. For each, wedescribe current market state, highlight representative vendors and open-source efforts, and assess how Financial and commercial management Financial and commercial management represents one of the most critical, and least mature, layers of theemerging agentic AI software infrastructure stack. At a foundational level, many of the requiredcapabilities are not new. Enterprises still need to maintain product catalogs, configure offerings, generate However, the systems that deliver these capabilities are themselves undergoing a profoundtransformation as AI—and increasingly agentic AI—becomes embedded directly into commercialworkflows. Established enterprise platforms are beginning to infuse autonomous intelligence intotraditionally manual or rules-based processes. For example, Salesforce has introduced autonomous AI In parallel, a new class of agentic-AI-native entrants is emerging, designed from the ground up for modernAI-centric business models. Alguna exemplifies this shift with a no-code, AI-first CPQ platform built tosupport complex usage-based, hybrid, and bundled pricing structures—capabilities that are increasingly transparency side, Vantage has emerged as a leading AI cost visibility