The Stack, Stated: A Canonical Taxonomy of AI Agent Infrastructure

Worldwide spending on artificial intelligence will reach $2.59 trillion in 2026, a 47% increase from $1.76 trillion in 2025, according to a Gartner forecast released May 19, 2026, and $1.43 trillion of that sum, 55% of the total, goes to infrastructure. John-David Lovelock, the Gartner analyst behind the forecast, called 2026 the inflection year. What is AI agent infrastructure, and how much of that $2.59 trillion belongs to it? The answer depends on the definition. Deloitte counts a standalone agentic AI market of $8.5 billion for 2026; Gartner counts $201.9 billion once agentic capability embedded in other software is included. That 24× spread is a measurement problem, and resolving it requires a taxonomy. This article proposes one: six layers, one instrumentation plane and one substrate, each dated and each mapped to the evidence.

Origins of the Agent Stack: From Madrona’s Six Themes to O’Reilly’s Six Layers

Jon Turow of Madrona Venture Group published “The Rise of AI Agent Infrastructure” on June 5, 2024, the earliest widely cited use of the phrase as a category; the essay grouped the emerging vendors into six themes: developer tools, agents-as-a-service, browser infrastructure, personalized memory, agent authentication and a “Vercel for agents” hosting layer. Madrona revisited the map on Feb. 28, 2025, compressing it into three defining layers: tools, data and orchestration. Competing taxonomies multiplied through 2025 and 2026, with published guides counting three, six, seven or nine layers. The most rigorous of the recent attempts, Paolo Perrone’s “The AI Agents Stack (2026 Edition)” on O’Reilly Radar, June 8, 2026, settled on six layers and anchored each with data, including 97 million monthly downloads of the Model Context Protocol SDKs and a 37-point gap between the teams that trace their agents (89%) and the teams that evaluate them (52%). Principle matters more than layer count. A layer earns its place when it has its own standards, its own vendors and its own risk profile.

Defining AI Agent Infrastructure

AI agent infrastructure is the set of shared systems beneath an agent application that let a model act in the world: reason within a cost and latency budget, reach tools and data through a common protocol, run code in an isolated environment, persist state across sessions, prove its identity and stay inside a mandate, pay and get paid, and leave a trace that a human or an evaluator can audit. Two exclusions follow by design. The model itself is an input purchased from the layer above the substrate, priced by tier and judged by accuracy per dollar; the application, the agent that books travel or triages tickets, belongs to whoever owns the workflow. Infrastructure is what both of them share, and the test for membership is substitution. A component qualifies when an application could swap it for a rival of equivalent function, as it can swap one MCP server for another or one sandbox vendor for another.

Six Layers, One Plane, One Substrate: The Canonical Taxonomy

Two further elements sit outside the six layers. Observability and evaluation form an instrumentation plane that cuts across all of them: traces, cost attribution and evals are properties of the whole system, and Perrone’s 89% versus 52% gap shows that instrumentation has been bought far faster than it has been used. Compute is the substrate. Gartner’s May 19 forecast assigns $1.43 trillion of 2026 AI spending to infrastructure, and the same release projects AI software at $453 billion (+60%) and AI services at $586 billion (+34%), which puts the substrate at roughly three times the size of the software that runs on it. Platforms and interfaces, the Agentforces and Copilot Studios and coding agents, sit above the six layers as packaged bundles of them; they are covered in this journal as a category of their own because buyers purchase them as units.

Reconciling the Agentic AI Market Size: $8.5 Billion or $201.9 Billion

Deloitte’s TMT Predictions 2026, published Nov. 18, 2025, sized the standalone agentic AI market at $8.5 billion for 2026, growing to $35 billion by 2030 in the base case or $45 billion if orchestration improves, and predicted that as many as 75% of companies may invest in agentic AI by the end of 2026. Gartner’s fourth-quarter 2025 forecast, as reported by Software Strategies Blog on Feb. 16, 2026 from a paywalled Gartner document, put agentic AI spending at $201.9 billion in 2026, up 141%, rising to $752.7 billion in 2029, with agentic spend overtaking chatbot spend in 2027. The two numbers describe different objects. Deloitte counts software sold as agents; Gartner counts agentic capability embedded across software categories, which folds a share of every CRM, ERP and productivity-suite contract into the total. Menlo Ventures offers a third lens in its Dec. 9, 2025 report: enterprise generative AI spending of $37 billion in 2025, tripling from $11.5 billion, of which agent platforms accounted for roughly $750 million.

Reconciliation follows from the taxonomy. Standalone estimates measure the top of the stack, the application layer plus the orchestration tooling sold as a product; embedded estimates measure agentic function wherever it appears, including inside incumbents’ suites; and both omit most of the substrate, because the $1.43 trillion of infrastructure spending is booked as compute. A reader who wants the size of agent infrastructure as this taxonomy defines it needs a fourth number that all three houses have yet to publish, and this journal will keep the three side by side until one does.

Adoption Evidence: 16% True Agents and a 40% Cancellation Rate

Menlo’s Dec. 9, 2025 report found that 16% of enterprise deployments qualify as true agents, with the remainder running as fixed-sequence workflows. Gartner’s “Hype Cycle for Agentic AI, 2026,” published April 2, 2026 and explained in an April 15 article, reports that 17% of organizations have deployed agents and that more than 60% expect to within two years, the most aggressive adoption curve Gartner measures. McKinsey’s “The State of AI: Global Survey 2026,” fielded May 4 to June 8, 2026 among 1,719 respondents and published Aug. 25, found 40% of organizations with revenue above $1 billion scaling agents, against 22% of smaller organizations, while 37% of all respondents attribute any EBIT impact to AI and 6% qualify as high performers. Gartner said in a June 25, 2025 press release that more than 40% of agentic AI projects will be canceled by the end of 2027, citing cost, business value that resists measurement and weak risk controls, and estimated that of thousands of vendors marketing agents, about 130 sell the real thing.

Read together, the figures describe a market whose infrastructure is being built ahead of the agents that will run on it. The 97 million monthly MCP downloads and the 150-plus organizations running A2A exceed, by orders of magnitude, the count of deployments that Menlo would classify as true agents; the protocols are being adopted by workflows first. That sequencing is normal for infrastructure, and it is also the origin of the cancellation rate. A project built on a fixed workflow and marketed as an agent inherits agent-grade costs with workflow-grade returns.

Mapping the Agent Infrastructure Layers to This Journal’s Coverage

Each article carries a last-verified date, and each figure in it traces to a listed source. Where two sources conflict, both appear, and vendor-published benchmarks are labeled as such.

What to Watch

Three developments will test the taxonomy over the next year. First, the Agentic AI Foundation, which took MCP, goose and AGENTS.md under Linux Foundation governance on Dec. 9, 2025 with AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI as platinum founders, will show whether the protocol layer consolidates under one governance body or fragments across payment and identity standards owned by card networks and cloud providers. Second, Gartner’s 2027 cancellation deadline arrives with its 40% figure attached, and the share of deployments that the next Menlo survey classifies as true agents will indicate whether returns caught up with infrastructure. Third, the market-size spread itself: when Deloitte’s standalone figure and Gartner’s embedded figure begin converging, the category will have matured from an infrastructure story into a software story. This taxonomy carries a date for that reason. It will be revised as the layers move.