State of Agentic AI in 2026

Where the agentic AI market stands in Q2 2026

The agentic AI market has shifted from experimental pilots to production deployments faster than most analysts predicted. The data across multiple independent research firms tells a consistent story: enterprises are not evaluating AI agents — they are operating them.

IBM’s 2025 CEO Study found 61% of CEOs were actively adopting AI agents and preparing for scale implementation. By early 2026, that figure has moved further. PwC’s 2025 AI Agent Survey reported 79% of executives confirmed agent adoption was already underway in their organizations, with 66% of adopters reporting measurable productivity gains.

Deloitte’s 2026 enterprise AI report reinforced this trajectory, identifying agentic AI as the highest-priority category across financial services, manufacturing, healthcare, and public sector verticals. The common thread across these industries: high-value proprietary data, repeatable workflows, and regulatory environments that demand auditability.

What changed between 2025 and now is not enthusiasm — it is operational commitment. Agents are no longer proofs of concept sitting in innovation labs. They are running customer support workflows, processing compliance documents, and managing supply chain decisions.

The investment landscape

Enterprise budgets have followed the adoption curve. KPMG’s January 2026 AI Pulse Survey reported organizations are projected to deploy $124 million in AI spending over the coming year, with 67% of business leaders stating they would maintain AI spending even in a recession scenario. This recession-resistance signal is significant — it separates agentic AI from discretionary technology spending.

PwC found 88% of executives increasing AI-related budgets specifically because of agentic AI capabilities, with 59% expecting measurable ROI within the budget cycle. These are not aspirational commitments. They are line items in approved budgets.

On the venture side, infrastructure and middleware layers are attracting disproportionate attention. The model layer is consolidating around a small number of foundation model providers. The application layer is fragmenting across thousands of vertical solutions. The infrastructure layer — the connective tissue between agents and the data they need — remains underbuilt and is drawing capital accordingly.

Gartner’s August 2025 analysis projected agentic AI could drive approximately 30% of enterprise application software revenue by 2035, exceeding $450 billion. Even discounting these projections conservatively, the investment thesis is clear: the market is large, the adoption is real, and the spending is durable.

The shift from single agents to multi-agent systems

The most significant architectural shift in 2026 is the move from single-agent deployments to multi-agent orchestration. Early enterprise implementations typically involved one agent handling one task — a support chatbot, a document summarizer, a code reviewer. That model is giving way to coordinated agent workflows where multiple specialized agents collaborate on complex processes.

A financial services compliance workflow, for example, might involve one agent extracting relevant clauses from regulatory filings, a second agent cross-referencing those clauses against internal policies, a third agent flagging discrepancies, and a fourth agent drafting remediation recommendations. Each agent is specialized. The value comes from orchestration.

This shift has profound infrastructure implications. Single agents can operate with simple retrieval pipelines. Multi-agent systems need shared context, consistent permissions across agent boundaries, traceable handoffs, and unified audit trails. The infrastructure requirements multiply with each agent added to the workflow.

Protocols like MCP (Model Context Protocol) and emerging tool-use standards have made multi-agent communication technically feasible. What remains missing is the data layer underneath — the infrastructure that ensures every agent in a workflow can access the right data, with the right permissions, at the right cost, with full provenance tracking.

The infrastructure gap: data access as the primary bottleneck

Model capability is no longer the constraint. The major foundation models can reason, plan, and execute with sufficient quality for most enterprise workflows. The constraint has shifted decisively to data access.

IBM’s CEO Study found 72% of executives view proprietary data as key to unlocking generative AI value, while 50% said rapid AI investment had left them with disconnected technology. Enterprises built agent capabilities without building the data infrastructure to support them.

The gaps are specific and measurable:

• Private data retrieval. Agents cannot safely access enterprise proprietary data — internal knowledge bases, legal repositories, financial models, operational runbooks. The data exists but is locked behind systems that were never designed for programmatic agent access.
• Economic infrastructure. There is no standard mechanism for pricing and monetizing agent data access. Organizations that want to make their data available to agents — whether internal or external — lack the transactional layer to do so.
• Trust and provenance. When an agent returns a result, there is no consistent way to verify where the underlying data came from, whether it is current, or how it should be cited. This is a hard blocker for regulated industries.
• Cross-organizational retrieval. Multi-agent workflows increasingly need data from multiple organizations. The infrastructure for secure, permissioned, priced cross-boundary data retrieval does not exist at scale.

This is the problem space where platforms like ipto.ai are building — creating the private data infrastructure layer that connects agents to proprietary data with the permissions, pricing, provenance, and audit capabilities enterprises require. The focus is not on building better models or better agents, but on solving the infrastructure gap between them.

Governance and compliance as table stakes

Governance has moved from “nice to have” to “deployment blocker” in 2026. Multiple forces are driving this shift.

Regulatory pressure is increasing globally. The EU AI Act’s provisions around high-risk AI systems apply directly to autonomous agent workflows in finance, healthcare, and public sector. US sector-specific regulators are issuing guidance on AI agent oversight. Enterprises operating in multiple jurisdictions face a patchwork of requirements that demand robust governance infrastructure.

KPMG’s AI governance framework for the agentic era calls for traceable inter-agent handoffs, explainability, confidence thresholds, guardrails, and human oversight alongside strict access controls and privacy protections. These are not theoretical recommendations — they reflect requirements that enterprise procurement teams are writing into RFPs.

PwC’s findings that companies need to orchestrate and integrate multiple agents across applications and workflows underscore the governance challenge. Every agent-to-agent handoff is a potential audit point. Every data retrieval is a potential compliance event. Without infrastructure-level governance, enterprises cannot scale agent deployments past pilot stage.

The practical implication: any infrastructure connecting agents to data must treat governance as a core architectural principle. Audit trails, access controls, usage tracking, and compliance reporting need to be built into the retrieval layer, not bolted on after the fact.

What comes next

Based on current trajectories and the data from IBM, PwC, Gartner, KPMG, and Deloitte, several developments are likely over the next 12 to 18 months:

Data infrastructure becomes the competitive differentiator. Organizations that solve the private data access problem will unlock agent value that others cannot. The competitive advantage shifts from “which model do you use” to “what data can your agents access.”

Agent-to-agent commerce emerges. As multi-agent systems mature, agents will need to transact with each other — purchasing data access, paying for specialized capabilities, settling usage-based fees. The economic layer for this does not yet exist but is being actively built by infrastructure platforms.

Governance standardization accelerates. Industry-specific governance standards for agent workflows will emerge, likely led by financial services and healthcare. These standards will define minimum requirements for auditability, explainability, and human oversight.

The data supply side opens up. Organizations sitting on valuable proprietary data will increasingly recognize the revenue opportunity in making that data available to agents — on their terms, with their pricing, under their control. This mirrors the API economy evolution of the 2010s but with richer economic and governance models.

Vertical agent platforms consolidate. The current fragmentation of agent solutions across verticals will consolidate around platforms that solve the full stack: orchestration, data access, governance, and economics. Horizontal infrastructure layers will be critical enablers.

For ongoing analysis of these trends and deeper dives into the infrastructure layer, the ipto.ai Substack covers market developments as they unfold.

Key takeaways

• Enterprise agentic AI adoption has moved past pilot stage: 79% of executives report active agent deployments (PwC), 61% of CEOs are scaling implementations (IBM)
• Investment is substantial and recession-resistant: $124M projected per organization (KPMG), 88% of executives increasing AI budgets for agentic AI (PwC)
• Multi-agent orchestration is replacing single-agent deployments, multiplying infrastructure requirements for shared context, permissions, and audit
• The primary bottleneck is data access, not model capability: 72% of CEOs identify proprietary data as key to AI value, while 50% report disconnected technology (IBM)
• Governance is now a deployment requirement, not a feature — driven by regulatory pressure (EU AI Act, sector-specific guidance) and enterprise security demands
• Infrastructure connecting agents to private data — with pricing, provenance, permissions, and audit — is the critical unsolved layer and the fastest-growing investment sub-segment
• The market opportunity for agentic AI infrastructure participates in a projected $450B+ enterprise software revenue pool by 2035 (Gartner)