2026年代理型AI应用状况报告
2026 State ofAgentic AI AnonymizedInsights from500+Evo by Snyk AI Introduction Enterprise AI has crossed a structuralthreshold. What began as experimentationwith prompts, chatbots, and copilots israpidly evolving into agentic systems: AIsoftware capable of reasoning, calling At the same time, enterprise visibility andgovernance have not evolved at the samepace. Model-centric views dramaticallyunderrepresent the true AI footprint.System-level analysis shows that eachmodel is typically supported by two to This report provides an empirical view ofthat transition, based on anonymized AI Billof Materials (AI-BOM) telemetry dataacross 500+ scans of customers' AI This gap is no longer theoretical. As AIsystems gain access to tools and internalservices, risk compounds expanding fromdata exposure to unintended orunauthorized actions. Independent threat The findings reveal a clear inflection point.Agentic architectures are already inproduction, with roughly one in fourorganizations deploying autonomous agentframeworks or Model Context Protocol The implication is clear: governing AI atscale now requires system-level visibilityinto agentic software, not just models orinfrastructure. Organizations that treat AIas isolated components risk falling behind This gap is no longer theoretical. As AI systems gain access to toolsand internal services, risk compounds expanding from data exposure At a glance: Top metrics fromthe report Agentic adoption has arrived. •28.4% of organizations use agentic architectures (either Agents orMCP Servers)•20.4% of organizations use agentic frameworks (Agents). Takeaway:Agentic AI is no longer experimental, it is actively entering production. •0.24 models per repository (model-only view).•0.68 total AI components per repository (system-level view). Takeaway:Models are just the visible tip of a much larger AI supply chain. Note: Density metrics are calculated across the total volume of scanned repositories, including legacyapplications and 'maintenance-mode' codebases. A density of 0.24 in a brownfield environment signalssignificant penetration of new development initiatives. Depth, not volume, defines AI maturity. 3 •Healthcare, financial services, and specialized industries average ~50AI components per account. Takeaway:Regulated and high-stakes industries are embedding AI most deeply. 4 •82.4% of AI tools come from third-party packages.•Respondents report using roughly 5 external tools for every 1 custom Takeaway:AI innovation is accelerating — but so is supply chain exposure. 5 Takeaway:Most deployed models lack clear data provenance, limitinggovernance and compliance readiness. Chapter 1: From experimentation to agenticsystems What began as experimentation with prompts and chat interfaces is now maturing into agentic AI systems This shift is already visible in the data. Across the analyzed organizations, 21.8% have deployed agenticframeworks, signaling autonomous behavior in real systems, while 19.7 % have deployed Model Context Protocol(MCP) servers, indicating investment in the infrastructure required to connect AI to enterprise tools, services, Binary indicators of AI presence, however, understate the true depth of adoption. When measured solely bymodels, AI usage appears limited. When the full AI supply chain is considered — including models, tools,packages, datasets, and orchestration layers — the footprint expands dramatically. On average, each model is This chapter examines how AI adoption progresses from experimentation to platform-level integration. Byanalyzing adoption intensity, system composition, and industry paerns, it distinguishes exploratory usage from To assess the early adoption of agentic and platform-level AI architectures, we analyzed customer-level usageof agentic frameworks (Agents) and Model Context Protocol (MCP) servers. This metric reflects adoption rate, 20.4% of active organizationshave adoptedagentic frameworks, such as autonomousagent frameworks (e.g., LangChain, AutoGen),indicating active experimentation or IMPLICATIONS Agentic AI adoption is no longer niche.More than 1 in 4 organizations has alreadymoved beyond prompt-based AI toward 18.2% of active organizationshave deployedMCP servers, reflecting early investment ininfrastructure that connects AI systems tointernal tools, data, and services. Thisrepresents the cohort that has crossed the Platform infrastructure is emergingalongside autonomy.MCP servers act as aconnective layer between AI models and Risk scales with adoption.As agents gainaccess to tools and internal services,operational and security risks shift from Furthermore, the significant overlap(10.1% ofrespondents use both)indicates thatorganizations experimenting with autonomousAI are simultaneously investing in theinfrastructure required to operationalize thosesystems. Rather than treating agents as This paern marks a transition from AI usage focused on content generation and decision support towardsyste
