How enterprises are building AI agents in 2026

AI agents have moved from experimental to an essential part of an organization’s tech stack. But how are enterprises actually using them?

In partnership with research firm Material, we surveyed over 500 technical leaders across industries and company sizes to understand how organizations are deploying agents today and where they see opportunity ahead.

The findings show a clear pattern: organizations are shifting from simple task automation to complex, multi-step workflows that span teams and business functions.

What the data shows

More than half of organizations (57%) now deploy agents for multi-stage workflows, with 16% running cross-functional processes across multiple teams. In 2026, 81% plan to tackle more complex use cases, including 39% developing agents for multi-step processes and 29% deploying them for cross-functional projects.

Coding leads adoption. Nearly 90% of organizations use AI to assist with development, and 86% deploy agents for production code. Organizations report time savings across the entire development lifecycle: planning and ideation (58%), code generation (59%), documentation (59%), and code review and testing (59%).

But the impact extends beyond engineering. Data analysis and report generation (60%) and internal process automation (48%) rank among the highest-impact use cases. Looking ahead, 56% plan to implement agents for research and reporting over the next year.

Perhaps most notably, 80% of organizations report their AI agent investments are already delivering measurable economic returns.

What this looks like in practice

The organizations seeing results are treating agents as a core part of their infrastrucuture, not experiments.

Thomson Reuters uses Claude to power CoCounsel, their AI legal platform. Lawyers who once spent hours manually searching through documents can now access 150 years of case law and 3,000 domain experts in minutes.

Cybersecurity company eSentire compressed expert threat analysis from 5 hours to 7 minutes, with AI-driven analysis aligning with their senior security experts 95% of the time. In healthcare, Doctolib rolled out Claude Code across their entire engineering team, replacing legacy testing infrastructure in hours instead of weeks and shipping features 40% faster.

The retail sector is seeing similar gains. L'Oréal achieved 99.9% accuracy on conversational analytics, enabling 44,000 monthly users to query data directly instead of waiting for custom dashboards.

The path forward

The question for leaders in 2026 isn't whether to adopt AI agents but how to scale them strategically. The data points to three primary challenges: integration with existing systems (46%), data access and quality (42%), and change management needs (39%).

Nine in 10 leaders report that agents are shifting how their teams work, with employees spending more time on strategic activities, relationship building, and skill development rather than routine execution.

This transition requires purpose-built infrastructure: models optimized for coding and enterprise workflows, frameworks like the Agent SDK, and tools like Claude Code that help teams move from prototype to production faster.

We're also finding that while coding has been the proving ground for AI agents,  it's just the beginning. As agents expand into research, customer service, financial planning, and supply chain operations, the organizations that build expertise now will capture disproportionate value as the technology matures.

Read the full 2026 State of AI Agents Report, here.