A guide to the leading no-code, low-code, and enterprise platforms for building, deploying, and orchestrating AI agents across your team's workflows.
According to the 2026 Microsoft Cyber Pulse report, “More than 80% of Fortune 500 companies today use AI active agents built with low-code/no-code tools.” This shift marks a move from generative AI, which produces content on request, toward agentic AI, which can plan and execute multi-step work on its own. Today, the best AI agent builders enable non-technical teams to quickly build agentic workflows without having to wait for IT support, and with the assurance that the proper governance is in place.
Key takeaways
- The best AI agent builder depends on your team's technical level, existing tool stack, and whether you need enterprise governance or just want to quickly automate a few workflows
- No-code platforms have closed most of the capability gap with developer frameworks enabling non-technical teams to build production-grade agents without writing code
- Airtable's structured data model makes it a strong foundation for AI agents that need to read from, write to, and act on business data across teams
What is an AI agent builder?
An AI agent builder is a platform for creating autonomous agents that can take in information, decide what to do with it, and carry out actions across the tools your team uses, without someone directing every step.
Agent builders are very different from basic automation tools, which follow pre-scripted, fixed workflows: when X happens, do Y — every time, the same way. By contrast, agent builders handle open-ended tasks and branching logic, meaning that agents are built to handle situations where the next step isn't fixed. They can look at the same task twice and make a different decision depending on what they find, ask a clarifying question, or hand off part of the job to another agent, all without a human anticipating and scripting each possible branch in advance. Many teams may continue using basic automation to handle processes that never change, but teams are increasingly taking advantage of agentic systems to apply agents to workflows where judgment is required.
What to look for when evaluating AI agent builders
As you evaluate your options, consider the following criteria:
- Ease of use and time-to-first-agent: Can someone on your team, not just a developer, get a working agent running in a single afternoon?
- Integration depth: Does the platform connect to the tools you already use, reliably enough that agents can act on complete, current data?
- Flexible output and model choice: Can an agent's output land somewhere your team or downstream agents can use, and can you pick the model — whether it’s OpenAI GPT models, Anthropic Claude, Google Gemini, DeepSeek, or others — to fit the task?
- Human-in-the-loop controls: Can you add approval steps so a person signs off before an agent takes action?
- Security and compliance: Does the platform offer certifications like SOC 2, HIPAA, and GDPR? — critical if agents will touch sensitive data.
Best AI agent builders
Here's a breakdown by type of agent builders to help you find the platform that best fits your needs.
No-code AI agent builders
These leading no-code AI agent builders allow non-technical, operational teams to create AI-powered workflows without relying on engineering support.
- Relay.app is a no-code AI automation platform where teams build AI agents through a chat-based, drag-and-drop interface, selecting their AI models, apps, and building in human-in-the-loop approval steps. Relay.app helps teams build agents that can handle repeatable workflows and streamline common operational processes where high volumes of data slow things down.
- Gumloop is a no-code platform with a visual builder for creating AI agents and multi-step workflows, with support for multiple AI models and hundreds of app integrations. It helps teams needing to process large volumes of documents or data through a repeatable agent workflow.
- Zapier is a workflow automation tool, generally considered middleware, that has layered an AI Agent Builder on top of its existing library of thousands of app integrations, from Webflow to Google Workspace. It helps teams connect a wide range of niche or legacy tools alongside AI-driven steps so that they can build automated workflows.
- Airtable provides no-code app building where operational teams can put Airtable AI Agents to work analyzing, researching, and creating content across your operational data. They can also use Airtable Hyperagent to describe a workflow in natural language and design a fleet of custom agents. Airtable connects to outside LLM models, like Claude, through Model Context Protocol (MCP), so that any agent you build, whether natively or in another system, can read from and write to the same structured data. Since workflows and the data live within the same operational surface, teams can quickly build agentic workflows with confidence that every agent action remains visible.
Enterprise AI agent builders
These leading enterprise AI agent platforms provide the agent security, compliance, and agent observability that enterprise teams require.
- StackAI allows IT teams to orchestrate agents with a visual workflow canvas, on-premises and VPC deployment options, and SOC 2, HIPAA, and GDPR compliance. It serves enterprise teams that need agents to operate inside strict compliance boundaries, often within regulated industries like finance, healthcare, and legal.
- Relevance AI allows you to build and deploy AI agents across business functions, with tools for connecting data sources and assembling custom multi-agent workflows. It's designed for teams that want pre-built agent templates alongside room to customize agent behavior.
- Tray.ai is an integration and orchestration platform with an agent builder and MCP governance that sits on top of its existing automation engine, connecting to hundreds of apps. It helps enterprises that want to manage agents, integrations, and governance from within a single platform.
- Salesforce Agentforce is an agent-building platform native to the Salesforce ecosystem, using its reasoning engine to ground agent actions in CRM data and enforce governance through a dedicated trust layer. It's built for organizations that want agents acting directly on Salesforce data across sales, service, and marketing.
- Airtable operates within both the no-code and enterprise agent platform space, pairing pre-built Field Agents with a custom agent builder. Enterprise teams can build directly within Airtable or connect LLM models to Airtable via MCP to create a data layer that allows agents and humans to work across the same operational surface. Airtable also provides enterprise-grade security and agent governance — SSO, SCIM, granular permissions, SOC 2 Type II, and HIPAA support — with full visibility into what agents are doing and why. Audit logs and governance are built right into cross-functional workflows.
AI agent builders for developers
These leading AI agent builders are purpose-built for technical teams who want more control over their agentic workflows.
- n8n is an open-source workflow automation platform with a visual canvas that lets developers combine no-code nodes with custom JavaScript or Python, with the option to be self-hosted. It allows technical teams to see and and control their automation infrastructure and retain code-level control.
- LangGraph, from LangChain, models agent workflows as a graph of nodes and conditional edges, giving developers explicit control over state, branching, and error recovery. It's built for teams building production-grade, stateful multi-agent systems that need audit trails and rollback points.
- CrewAI is an open-source framework that organizes AI agents into role-based “crews,” where each agent has a defined role, goal, and set of tools. This may appeal to teams that want to prototype multi-agent systems quickly around specialist roles, then optimize, rather than build an explicit state graph.
- AutoGen is Microsoft Research's open-source framework for building multi-agent systems where agents collaborate through conversation. It works well for research-oriented or offline workflows where agents need to debate and refine an answer rather than run in real time.
- Airtable connects to developer tools through Model Context Protocol (MCP) and its SDK, letting agents build in Claude Code or other AI agent frameworks, and read from and write to Airtable data using the same structured schema that powers Airtable agents. That gives developer teams a shared, well-documented data layer to build on.
How to choose the right AI agent builder for your team
These four questions create a decision framework to help you narrow options down to a shortlist of the best AI agent tools for your use case and needs.
- How technical is your team? Teams that are mostly reliant on developers to adopt new technology should consider no-code platforms that offer native integrations to the tools they use daily, template libraries, and visual interfaces that support natural language agent-building. Teams with dedicated engineering support or that need fine-grained control are better served by enterprise platforms or developer frameworks like LangGraph or CrewAI.
- What tools do you need to connect? Any solution’s feature list may look great, but it won’t work for you (or your agents) if it doesn’t integrate well with your existing tech stack. Agents need to read and/or write to various systems across teams, so be sure to vet any solution for cross-functional success.
- What are your compliance requirements? For teams in regulated industries, begin by confirming that vendors offer the specific certifications they need before comparing anything else. Do you require traditional role-based access control (RBAC) permissions or will a low- or no-code tool satisfy your needs?
- Are you automating a repeatable workflow or building an open-ended agent? If you’re looking to automate workflows for specific use cases (e.g., creating weekly pipeline reports, activating employee onboarding flows, analyzing customer feedback, etc.), these can often be served by no-code platforms. If you’re looking to build multi-agent systems that tackle more complex, high-volume workflows that may also require human judgment, then you may need to isolate your search to enterprise agent builder platforms or those that are developer-focused.
Build AI agents on your data with Airtable
Whatever builder you choose, agents are only as good as the data they act on. Airtable provides a structured data layer to build on, where your teams — and your agents — can act on your most up-to-date business data.
The flexible AI agent builder built for all teams
Frequently asked questions
Workflow automation tools follow fixed, pre-scripted steps and can break when the tool encounters something unexpected. They are generally faster and more reliable when handling predictable and repeatable processes. For open-ended tasks that require judgment — like research, drafting, or triage — agents are the better choice as they can consider context and quickly pull insights from different tools, and most companies now use both.
Coding skills are no longer required to build AI agents. Many platforms, including Airtable, Relay.app, Gumloop, Lindy, and Zapier, are designed for non-technical users so that you can quickly automate operational workflows and build agents through visual interfaces or conversational descriptions.
Security and compliance varies by platform, so you must vet these aspects according to your data and industry needs. Regulated industries like healthcare, finance, or defense, for example, may want to look at StackAI or Tray.ai, given that they offer both compliance certifications and on-premises deployment options. For those looking primarily at cloud-based solutions, Airtable offers enterprise controls that include SSO, SCIM, HIPAA, SOC 2, and Secret management. Most major platforms are SOC 2 Type II compliant at minimum, but it’s always important to verify certifications and data residency options before deploying agents to handle sensitive data.
Yes. In fact, agents need to connect to your tools to do their job well. Most platforms offer between 200 to thousands of pre-built integrations, along with the ability to connect to custom tools via API or MCP servers. For agents that need to read from and write to structured business data, pairing your agent builder with an agent system of record like Airtable gives agents a real-time data foundation to act on.
