What is low-code AI agent development? [+7 best platforms reviewed]
AI agents can analyze information, make decisions and take action across business workflows. Building those capabilities no longer requires teams to code every integration, orchestration layer, and workflow from scratch.
Low-code AI agent development uses visual tools, reusable components, and prebuilt integrations to accelerate that work. For enterprises, the bigger advantage is control: teams can build agents faster while maintaining the security, governance, and integration depth needed to move from experimentation into production.
What is a low-code AI agent builder?
A low-code AI agent builder is a development environment that lets teams visually create, connect, test, deploy, and manage AI agents with less hand coding. Instead of building every interaction among a large language model (LLM), enterprise data, APIs, and business logic from scratch, teams configure much of that behavior through visual workflows and reusable components.
This approach can also create a shared development environment for professional developers and citizen developers, such as product managers and business analysts. Business users can contribute process knowledge and requirements while developers retain control over architecture, integrations, security, and specialized code.
Key components of a low-code AI agent
Low-code AI agent platforms typically bring several capabilities together:
- Reasoning and models: Configure the LLM or other AI models an agent uses to interpret information and determine its next action
- Context and memory: Ground agents in relevant enterprise information using techniques such as retrieval-augmented generation (RAG), embeddings and vector databases
- Tools and integrations: Connect agents to APIs, applications, databases, and model context protocol (MCP) servers
- Orchestration: Design workflows, branching logic, human approval points, and multi-agent collaboration
- Governance and observability: Control access, monitor executions, debug problems, and maintain audit logs
Low-code vs. traditional AI agent development: What’s the difference?
Low-code doesn’t eliminate code. It reduces the amount of repetitive infrastructure and orchestration teams need to build manually while preserving code-based options for requirements that need deeper customization.
Benefits of low-code AI agent development
Low-code AI agent development can shorten the path between an idea and a working agent, but development speed is only part of the value.
- Accelerate development and iteration. Visual tools and reusable components reduce repetitive work, making it easier to test an agent, change a workflow, and respond to new requirements.
- Expand collaboration. A visual builder makes agent logic easier for technical and business teams to review together.
- Connect AI to real workflows. API integration and enterprise connectors allow agents to retrieve information and take approved actions across existing systems.
- Improve visibility. Built-in observability, debugging, error handling, and audit logs help teams understand how an agent behaves once it begins taking action.
- Apply governance from the start. Access controls, guardrails, and human checkpoints can become part of the agent architecture rather than an afterthought.
The 2025 State of Application Development report, based on a survey of nearly 1,700 IT professionals, found that 88% of respondents had at least some low-code projects underway. It also found that 55% said low-code helped them modernize more cost-effectively, showing why enterprises increasingly use visual development for complex technology initiatives.
Types of low-code AI agent builders
Not every product described as a low-code or no-code AI agent builder solves the same problem. The market generally falls into four categories:
A workflow orchestrator may be enough to connect an AI model to a few business tools. Enterprises building agents that access sensitive data, coordinate other agents, or operate inside mission-critical applications typically need a broader development and governance foundation.
Key features and functionalities to look for in a low-code AI agent platform
Enterprise buyers should evaluate what happens after the first agent works.
Integration depth: Look for API integration, enterprise connectors and MCP support that let agents work with both modern and legacy infrastructure.
Enterprise context: Agents need controlled access to the data required for the task. Evaluate support for RAG, embeddings, vector databases, and other grounding approaches.
Model flexibility: Teams should be able to choose models based on the use case and change them as requirements, performance and costs evolve.
Security and governance: Look for role-based access, guardrails, human-in-the-loop controls, auditability, and centralized governance.
Testing and observability: Teams need to inspect execution traces, evaluate output, troubleshoot failures, and monitor production behavior.
Low-code and pro-code extensibility: Visual development should accelerate common work without preventing developers from using code when an enterprise requirement demands it.
7 best low-code AI agent platforms for enterprise teams
The best low-code AI agent platform depends on your existing technology stack, the systems an agent needs to access, and whether you’re building a standalone agent or an agentic application with a broader user experience and business logic.
Pricing and packaging change frequently, so confirm current licensing with each vendor during evaluation.
1. OutSystems Agent Workbench
Best for: Enterprises building agents as part of larger custom applications, processes, and experiences.
OutSystems Agent Workbench provides a visual environment for building, orchestrating, and governing AI agents while connecting them to enterprise applications, systems, and data. Because Agent Workbench is part of the broader OutSystems platform, teams can develop the applications and business logic surrounding an agent in the same environment.
Key features
- Visual drag-and-drop agent and workflow design
- Integrations with AI models, enterprise data, APIs, and MCP servers
- Human-in-the-loop controls, observability, and governance
Advantages of using OutSystems
- Build agents with access to the application and enterprise context they need
- Coordinate agents, applications, workflows, and people rather than managing agents in isolation
- Combine visual development with reusable components and developer control
- Apply established enterprise security and governance across applications and agents
What are real users saying about OutSystems?
OutSystems was named a Leader in G2’s 2026 AI Agent Builders report, with reviewers highlighting development speed, enterprise capabilities, and ease of use.
See what G2 users say about OutSystems AI agent development.
Customers are also putting Agent Workbench into production. Kevin Hearn, SVP and head of consumer bank development at Axos Bank, said: “With Agent Workbench, we can quickly and safely create agents for specific use cases.”
Explore Agent Workbench customer stories and demos.
2. Salesforce Agentforce
Best for: Organizations whose customer and operational workflows already center on Salesforce.
Salesforce Agentforce lets teams create agents that use Salesforce data, business logic, and automation. It’s particularly well suited to customer service, sales, commerce, and marketing scenarios where agents need direct access to CRM context.
Key features
- Low-code Agentforce Builder
- Salesforce Flow and business data integration
- Guardrails, testing and agent management
Advantages
G2 reviewers commonly praise Agentforce for its Salesforce integration, approachable low-code experience, and ability to automate CRM workflows.
Limitations
Reviews also point to a learning curve for advanced implementations, potentially complex usage-based pricing, and greater implementation effort when agents need to operate beyond the Salesforce ecosystem.
3. Microsoft Copilot Studio
Best for: Organizations already invested in Microsoft 365, Azure, and Power Platform.
Microsoft Copilot Studio allows teams to create agents through natural language or a graphical interface, connect them to business data, and publish them across Microsoft and external channels.
Key features
- Natural-language and visual agent development
- Microsoft 365 and Power Platform integration
- Centralized administration and governance
Advantages
Copilot Studio reviewers on G2 frequently highlight its visual interface and integration across Microsoft applications.
Limitations
More advanced agents can require deeper Power Platform expertise, and organizations should model Copilot Credit consumption carefully as usage grows. Microsoft’s free trial also supports building and testing agents but not publishing them.
4. ServiceNow AI Agent Studio
Best for: Enterprises already running significant workflows through ServiceNow.
ServiceNow AI Agent Studio lets teams create, manage, and test agents and agentic workflows using a guided, natural-language-based development experience. Agents can operate against ServiceNow data and workflows and collaborate through multi-agent orchestration.
Key features
- Natural-language agent creation
- Agentic workflow and multi-agent orchestration
- Integrated testing and management
Advantages
G2 reviews of ServiceNow AI Agents frequently cite workflow automation, reduced manual effort, and integration with existing ServiceNow processes.
Limitations
Reviewers also mention initial setup and customization complexity, the need for strong underlying data, and configuration and cost as adoption expands.
5. IBM watsonx Orchestrate
Best for: Enterprises that want low-code accessibility without giving up pro-code extensibility.
IBM watsonx Orchestrate supports no-code agent development alongside Python, APIs, OpenAPI, MCP servers, LangGraph agents, and other developer tooling. This makes it a strong hybrid option for organizations with mixed technical requirements.
Key features
- Visual agent and workflow development
- Python, API, MCP, and LangGraph support
- Choice of IBM and third-party models
Advantages
G2 reviewers commonly highlight its natural-language interface, ease of automation, and ability to connect tools and workflows.
Limitations
Common considerations include setup complexity and the learning curve associated with a broad enterprise AI environment, particularly for teams that need only a narrowly scoped agent builder.
6. Google Gemini Enterprise Agent Platform
Best for: Technical teams building and governing agents within Google Cloud.
Gemini Enterprise Agent Platform brings together visual agent development, models, deployment, orchestration, and governance. Agent Studio provides a low-code visual designer, while developers can transition to code when they need more control.
Key features
- Low-code Agent Studio
- Visual agent workflows and testing
- Broader model, deployment, and governance capabilities
Advantages
G2 users highlight integration with Google Cloud, scalability, and the ability to support both experimentation and production AI workloads.
Limitations
The platform is more technically oriented than many pure no-code AI agent tools. Reviewers commonly mention a steeper learning curve for teams unfamiliar with Google Cloud and potentially complex usage costs at scale.
7. UiPath Agent Builder
Best for: Enterprises combining AI agents with process automation and RPA.
UiPath Agent Builder is a visual drag-and-drop environment for configuring agents inside UiPath Studio. Its key distinction is the ability to orchestrate agents alongside robots, APIs and people, which is useful when a process spans systems that don’t all expose modern APIs.
Key features
- Visual agent development
- RPA and API-based automation
- Human-in-the-loop orchestration
Advantages
UiPath Agent Builder reviews point to its value for automating decision-heavy workflows, while broader UiPath feedback highlights ease of use and support for complex enterprise automation.
Limitations
Users cite debugging, cost, and learning curve among the considerations as automation programs become more complex.
Which AI agent platform is right for your business?
Use your existing architecture and intended workflow to narrow the field:
- Choose OutSystems when agents are part of larger custom applications or processes and you need to build the surrounding experience, logic, integrations, and governance.
- Choose Salesforce Agentforce when agents primarily need to act on Salesforce data and CRM processes.
- Choose Microsoft Copilot Studio when Microsoft 365, Azure and Power Platform already provide most of the agent’s data and workflow context.
- Choose ServiceNow AI Agent Studio when the workflows you want to automate already run predominantly through ServiceNow.
- Choose IBM watsonx Orchestrate when your teams want visual development plus extensive pro-code options.
- Choose Google Gemini Enterprise Agent Platform when you need a low-code/pro-code hybrid built around Google Cloud’s broader AI ecosystem.
- Choose UiPath Agent Builder when agents need to combine AI reasoning with RPA and deterministic automation.
For a broader platform assessment, use the OutSystems evaluation guide to evaluate requirements including architecture, security, integration, scalability, and lifecycle management.
OutSystems for low-code AI agent development
The harder part of enterprise agent development isn’t creating a demo. It’s giving agents the right enterprise context, connecting them safely to existing systems,and governing what they can do once they reach production.
OutSystems combines its low-code foundation with agentic development and orchestration in one open, unified platform. Teams can use OutSystems Agent Workbench to visually build and coordinate agents across applications, data, and enterprise workflows, with human oversight, observability, and governance built into the environment.
That broader application context is key when an agent is only one part of the experience. Rather than introducing another disconnected agent builder, teams can build, extend, and govern the applications and agents that support the process together on the OutSystems platform.
Useful Resources
Related Resources
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Frequently asked questions
Not necessarily. Low-code AI agent platforms can provide orchestration, integrations, workflow design, testing, and governance without requiring teams to assemble those capabilities from multiple open source frameworks.
Open source frameworks can still be useful when developers need highly specialized architectures or granular code-level control. The tradeoff is that the organization also owns more of the integration, infrastructure, and ongoing maintenance.
Low-code AI agent platforms typically connect to existing systems through APIs, prebuilt connectors, databases, and MCP servers. Some also support approaches such as RPA for systems without modern APIs.
This allows organizations to add agentic capabilities around existing CRM, ERP, databases, and legacy applications rather than replacing those systems solely to introduce AI.
Strong candidates combine information-heavy decisions with repeatable actions across systems. Common examples include customer support, IT service management, employee onboarding, document processing, compliance workflows, sales operations, and back-office automation.
Start with a bounded process that has clear success criteria, reliable data, and defined escalation paths. Once teams can observe and govern that workflow effectively, they can expand into more autonomous agents and multi-agent collaboration.
