As companies struggle to deploy AI beyond pilot projects, automation platform Make is betting that visual orchestration — not complex code — will bridge the gap between AI’s promise and practical business value.

The company unveiled an expansion of its automation and AI platform at its annual customer event, Waves ’25, including upgraded agentic AI offerings and enhanced testing capabilities on its AI platform.

The Prague-based company, which serves more than 350,000 organizations globally, made the announcements during its two-day conference on Oct. 15-16 in Munich. The timing is notable: the automation industry is experiencing a crucial inflection point as AI agents move from experimental features to production-ready tools, with Microsoft, Salesforce, and OpenAI all pushing their own agent frameworks in recent months.

The capabilities focus on what the company calls “visual orchestration” on its platform that gives users control over automated and AI-driven processes.

Build, accelerate, scale

As the initial AI hype cycle matures, platforms are competing not on AI capabilities alone but on their ability to make those capabilities accessible to non-technical business users who understand processes but can’t write code.

Anton Danilov, vice president of product at Make, said the company’s latest features align with three pillars: build, accelerate, and scale.

“AI and automation are making new things possible,” he told attendees. “And when and if the hype will be over, the value of highly automated business operations will be truly massive.”

Building the foundation

Under the first pillar — “build” — Make is focusing on making it easier for users to create their initial automations without technical expertise.

At the conference, the company showcased improvements to its no-code Scenario Builder, including new “If-Else” and “Merge” modules (launching early 2026) that bring programming logic to visual workflows. While conditional logic isn’t new to automation platforms, the visual implementation could lower the barrier for business users who currently rely on IT departments for anything beyond simple linear workflows.

Additional building tools include “Scenario Run Replay” for testing and debugging, and “Subscenarios” that let teams create reusable, modular workflow components. A forthcoming “Scenario Sharing” feature will allow teams to distribute automations securely with a single link.

Make also previewed Maia, an AI-powered automation builder that allows users to create workflows through natural-language conversations.

Danilov described Maia as “an intuitive experience” offering both speed and transparency, giving users the ability to co-create with AI.

Accelerating with intelligence

The second pillar — “accelerate” — focuses on improving intelligence within workflows.

Make introduced new AI Agents, which it says will allow users to build and monitor autonomous agents directly in the visual builder. Unlike traditional automations that follow fixed rules, agents can understand goals and adapt their actions based on changing conditions.

The company also announced a forthcoming Library of Make AI Agents for pre-built, customizable solutions, and one-click Agent Sharing for teams.

Technical users gain additional acceleration tools through “Make Code,” which enables users to write Python or JavaScript within workflows, and “Make AI Provider,” which integrates large language models without external API management.

Scaling the operation

Finally, the “scale” pillar tackles what happens when organizations move from dozens to hundreds or thousands of automated workflows.

Make Grid, now available to all customers, provides a real-time map of an organization’s entire automation landscape. The Grid interface includes tools for cost optimization (tracking which automations consume the most resources), performance monitoring (identifying bottlenecks) and collaboration features (showing which teams own which workflows).

Whether Make’s visual orchestration strategy will differentiate it sufficiently in an increasingly crowded market remains to be seen. But with enterprises still struggling to operationalize AI nearly two years after ChatGPT’s launch, the company’s focus on accessibility over raw capability offers yet another sign of a rapidly maturing market.

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