When OpenAI revealed its new AgentKit visual workflow tool at Dev Day 2025, it reopened a familiar debate. Do we really need another AI workflow builder? LangChain’s “Not Another Workflow Builder” makes a fair point. Visual builders promise to democratize AI creation, yet many fail to meet users where they are. They are often too simplistic for engineers and too complex for the business experts they target.
The appeal of no-code visual builders is easy to understand. They suggest a world where anyone, not just engineers, can assemble AI applications by dragging boxes and arrows on a canvas. For companies strapped for technical resources, it’s an attractive idea: let domain experts automate what they know best.
In theory, this approach levels the playing field. A marketing lead could build a chatbot; an analyst could prototype a report assistant. But theory and practice often diverge.
To understand where OpenAI’s workflow builder fits, it’s important to separate two related but distinct ideas: agents and workflows.
You trade control for creativity. Workflows are predictable but rigid. Agents are flexible but require stronger guardrails.
Most visual builders today fall squarely in the workflow camp. They make logic visible and testable, but they don’t enable true reasoning or self-direction. In contrast, agentic systems aim for something more: software that can plan and adapt dynamically rather than just follow a recipe.
The distinction between agents and workflows, however, is getting fuzzier. Modern workflow tools are no longer just static pipelines. Many now allow you to invoke agents or LLM-powered reasoning directly inside their branching logic.
For example, a decision node in a UiPath or n8n workflow can now ask an LLM to interpret an email, decide which department it should go to, or even choose which sub-flow to trigger next. Instead of fixed “if/else” statements, these systems can query a model for context before acting.
That makes the classic divide between “predictable workflows” and “autonomous agents” less clear. We’re entering a hybrid phase where agents live inside workflows, and workflows can call on AI for reasoning when the logic gets murky. It’s powerful, but it also raises questions about accountability, debugging, and governance. When a probabilistic model makes the choice, traditional guarantees of process reliability start to erode, and you must design for auditability and control.
Even with smarter branching, three fundamental challenges remain:
AgentKit lowers the barrier to building quickly. The simplicity helps early build, but enterprise power users will still want deeper control and extensibility.
LangChain points out that traditional no-code platforms are being squeezed from both ends.
This leaves visual builders stuck in the middle, too constrained for engineers and too complex for non-coders. As models grow more capable of translating natural language directly into either agentic behavior or executable code, that middle ground continues to shrink.
Overall, AgentKit, UiPath, and Zapier are built with different users in mind. When it comes to pricing and consumption cost, use RPA for stable, repetitive, high-throughput steps; add agents where ambiguity and reasoning create outsized value. Implement cost guardrails (caps, fallbacks to cheaper models, and observability) for any LLM-driven logic.
Goals: LLM-native agent builder for rapid, chat-centric automations that reason, call tools/APIs, and adapt. Great for intelligent triage, deep research, and conversational self-service; lighter on event/schedule-driven back-office flows.
Control, Governance & Compliance: Versioning, connector management, and evaluation/safety tooling for responsible use; cloud-only with lighter administrative controls, better suited to internal/low-risk data and clear guardrails set by the implementor.
Hosting: Usage-based (API/tokens). Powerful but variable; agent recursion/looping and long contexts drive costs—set max iterations, timeouts, and model mix to control spend.
Pricing: Usage-based (API/tokens). Powerful but variable; agent recursion/looping and long contexts drive costs—set max iterations, timeouts, and model mix to control spend.
Goals: Enterprise orchestration of RPA bots, APIs, AI, and human-in-the-loop with BPMN-style modeling. Built for regulated, long-running, multi-party processes where reliability, auditability, and scale matter.
Control, Governance & Compliance: Strongest enterprise guardrails: role-based access, environment separation (DEV/UAT/PROD), deployment approvals, detailed run/audit logs, and options to meet GDPR/industry controls via cloud or self-hosted setups.
Hosting: Flexible: UiPath cloud, private cloud, or fully on-prem (Automation Suite). Supports hybrid patterns and regional/data-residency needs.
Pricing: Deterministic licensing (robots/capacity + platform). Higher upfront, predictable run-costs—well suited for high-volume, repetitive work where utilization can be planned.
Goals: Fast, no-code app-to-app integrations with broad connector coverage. Ideal for departmental “glue” workflows and quick wins; less suited to complex branching, long-running state, or deep governance.
Control, Governance & Compliance: SSO/SCIM, RBAC, shared workspaces, change approvals, and audit logs for departmental governance; cloud-only and generally not used for PHI/strict data-residency use cases.
Hosting: Cloud-only (vendor-hosted). No self-hosting or regional private instances.
Pricing: Subscription tiers based on monthly task volume; economical for light/medium usage but costs scale with step count and frequency.
The next frontier is tools that abstract complexity and collapse the distance between human intent and machine execution. Agents will understand goals, generate code when needed, and orchestrate actions end to end with human oversight. OpenAI’s new workflow builder is a step toward that vision, but it does not in itself deliver true accessibility, or more importantly, meet the requirements of enterprise-wide scale (yet).