- Selecting the right AI agent builder in 2025 is key to scaling automation, improving support, and unlocking more value from enterprise data. The article explains what AI agent builders are, outlines the main categories (no-code, low-code, and developer frameworks), and shows how to align each with your team’s skills and ownership model. It recommends a requirements-first approach: define target use cases, volumes, integration needs, security and compliance expectations, budget, and rollout timeline before comparing tools.
- The guide then walks through how to evaluate leading platforms such as Vertex AI Agent Builder, LangChain, Lindy AI, CrewAI, and Glean Agent Builder across criteria like agent capabilities, workflow orchestration, integration breadth, deployment flexibility, and enterprise readiness. It emphasizes validating platforms through real-world prototypes, connecting to your actual systems, and collecting feedback from both technical and business stakeholders.
- Finally, the article stresses the importance of documentation, ecosystem and community, vendor reliability, and roadmap when making a long-term platform decision. It concludes with a weighted scoring framework that helps organizations systematically compare options and select an AI agent builder that aligns with business goals, reduces implementation risk, and supports sustainable, organization-wide AI adoption.
Selecting the right AI agent builder can transform how your organization automates workflows, supports customers, and scales operations. In 2025, the market offers diverse platforms—from no-code tools for business users to developer frameworks for custom integrations. The best AI agent builder for your enterprise depends on your team's technical skills, integration needs, security requirements, and business goals. This guide walks you through a structured evaluation process to help you choose a platform that delivers measurable value, integrates seamlessly with your existing tools, and supports long-term AI adoption at scale.
Understand what an AI agent builder is
An AI agent builder is a software platform that enables organizations to create, deploy, and manage AI-powered agents—automated software entities that interact with users, systems, or data to complete designated business tasks. These platforms provide visual interfaces, coding frameworks, or hybrid approaches for building customizable automation, integrations, and conversational experiences.
Modern AI agent builders support capabilities such as multi-step task orchestration, natural language understanding, and workflow integration. For example, LangChain offers developers advanced tools for building custom agent logic, while Vertex AI Agent Builder provides enterprise-grade infrastructure for scalable, compliant applications. Lindy AI enables business users to automate workflows quickly without writing code.
AI agent builders typically fall into three categories:
- No-code platforms: Visual, drag-and-drop interfaces designed for non-technical users
- Low-code platforms: Simplified development environments with some customization options
- Developer-focused frameworks: Full coding environments for advanced integrations and custom logic
Understanding these distinctions helps you align platform capabilities with your team's skills and your organization's automation goals.
Identify your requirements for AI agent builders
Before evaluating platforms, clearly define what you need AI agents to accomplish. Start by mapping the specific tasks or workflows agents should handle—customer support automation, knowledge retrieval, data analysis, IT service management, or sales enablement.
Determine your organizational priorities. Scalability matters if you plan to deploy agents across multiple departments or geographies. Security and compliance are essential for enterprises handling sensitive data or operating in regulated industries. Production readiness—the platform's ability to support high-volume, mission-critical operations—becomes critical as AI adoption matures.
Outline your integration requirements early. Identify the business systems your agents must connect with, such as Slack, Microsoft Teams, Jira, CRM platforms, or internal databases. Document compliance needs like SOC 2 or ISO certifications, which are crucial for enterprise deployments.
Create a checklist that includes:
- Primary use cases and workflows
- Expected agent volume and user scale
- Required integrations and data sources
- Security, compliance, and governance requirements
- Budget constraints and total cost of ownership
- Timeline for deployment and adoption
This structured requirements document becomes your foundation for comparing vendors and making an informed decision.
Assess your team's technical skills and preferences
The right AI agent builder depends heavily on who will build, deploy, and maintain your agents. Evaluate your team's composition and technical proficiency to choose a platform that maximizes efficiency and adoption.
No-code and low-code platforms suit business users, operations teams, and support staff who need to automate workflows without engineering support. These tools feature visual interfaces that allow users to configure agents through drag-and-drop actions, pre-built templates, and guided workflows. Lindy AI, for instance, enables rapid automation for common business processes.
Developer-focused frameworks appeal to engineering teams requiring deep customization, complex integrations, or specialized logic. These platforms offer APIs, SDKs, and coding environments that support advanced use cases but demand programming expertise.
Consider creating a simple decision framework:
<div class="overflow-scroll" role="region" aria-label="Team profiles and recommended platform types">
<table class="rich-text-table_component">
<thead class="rich-text-table_head">
<tr class="rich-text-table_row">
<th class="rich-text-table_header" scope="col">Team Profile</th>
<th class="rich-text-table_header" scope="col">Technical Skill Level</th>
<th class="rich-text-table_header" scope="col">Recommended Platform Type</th>
</tr>
</thead>
<tbody class="rich-text-table_body">
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Business users, operations</td>
<td class="rich-text-table_cell">Low to moderate</td>
<td class="rich-text-table_cell">No-code platforms</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Citizen developers, analysts</td>
<td class="rich-text-table_cell">Moderate</td>
<td class="rich-text-table_cell">Low-code platforms</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Software engineers, data scientists</td>
<td class="rich-text-table_cell">High</td>
<td class="rich-text-table_cell">Developer frameworks</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Mixed teams</td>
<td class="rich-text-table_cell">Varies</td>
<td class="rich-text-table_cell">Hybrid platforms with both visual and code interfaces</td>
</tr>
</tbody>
</table>
</div>
Understanding your team's capabilities ensures you select a platform that empowers users rather than creating friction or requiring extensive training.
Research and compare available AI agent builder platforms
Conduct a structured comparison of leading AI agent builder platforms to identify the best fit for your requirements. In 2025, the market offers diverse solutions tailored to different user profiles and enterprise needs.
<div class="overflow-scroll" role="region" aria-label="AI agent platforms comparison">
<table class="rich-text-table_component">
<thead class="rich-text-table_head">
<tr class="rich-text-table_row">
<th class="rich-text-table_header" scope="col">Platform</th>
<th class="rich-text-table_header" scope="col">Best For</th>
<th class="rich-text-table_header" scope="col">Key Strengths</th>
<th class="rich-text-table_header" scope="col">Integration Breadth</th>
<th class="rich-text-table_header" scope="col">Deployment Options</th>
</tr>
</thead>
<tbody class="rich-text-table_body">
<tr class="rich-text-table_row">
<td class="rich-text-table_cell"><a href="https://cloud.google.com/vertex-ai" target="_blank">Vertex AI Agent Builder</a></td>
<td class="rich-text-table_cell">Enterprise-grade applications</td>
<td class="rich-text-table_cell">Scalability, compliance, security</td>
<td class="rich-text-table_cell">Extensive enterprise tools</td>
<td class="rich-text-table_cell">Cloud, hybrid</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell"><a href="https://www.langchain.com" target="_blank">LangChain</a></td>
<td class="rich-text-table_cell">Advanced developers</td>
<td class="rich-text-table_cell">Deep customization, open-source</td>
<td class="rich-text-table_cell">Flexible API integrations</td>
<td class="rich-text-table_cell">Self-hosted, cloud</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell"><a href="https://www.lindy.ai" target="_blank">Lindy AI</a></td>
<td class="rich-text-table_cell">Business automation</td>
<td class="rich-text-table_cell">No-code interface, speed</td>
<td class="rich-text-table_cell">Common business apps</td>
<td class="rich-text-table_cell">Cloud</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell"><a href="https://www.crewai.com" target="_blank">CrewAI</a></td>
<td class="rich-text-table_cell">Multi-agent workflows</td>
<td class="rich-text-table_cell">Collaborative agents, open-source</td>
<td class="rich-text-table_cell"><a href="https://www.crewai.com" target="_blank">Customizable</a></td>
<td class="rich-text-table_cell">Self-hosted, cloud</td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Glean Agent Builder</td>
<td class="rich-text-table_cell">Enterprise Work AI</td>
<td class="rich-text-table_cell">Deep organizational data integration, security</td>
<td class="rich-text-table_cell">100+ enterprise apps</td>
<td class="rich-text-table_cell">Cloud, on-premises</td>
</tr>
</tbody>
</table>
</div>
Vertex AI Agent Builder excels in enterprise environments requiring robust compliance and scalability. LangChain appeals to development teams building highly customized agent architectures. Lindy AI accelerates deployment for business users who need quick automation without technical complexity. CrewAI, with over 31,000 GitHub stars and 900,000 downloads, offers collaborative multi-agent systems ideal for complex workflows.
Glean's Agent Builder distinguishes itself for enterprises seeking production-ready solutions, providing seamless integration across organizational knowledge bases and workflows. It combines ease of use with enterprise-grade security, making it suitable for large-scale AI adoption.
Evaluate each platform against your prioritized criteria: ease of use, extensibility through APIs and plugins, integration capabilities, security readiness, vendor support quality, and deployment flexibility.
Evaluate integration capabilities with existing tools and workflows
Robust integration determines whether AI agents deliver value or create isolated automation. Modern enterprises rely on interconnected systems—messaging platforms, CRM tools, project management software, data warehouses, and knowledge repositories. Your AI agent builder must connect seamlessly with these core systems.
Start by inventorying your critical business tools. Common integration points include Slack, Microsoft Teams, Salesforce, Jira, email platforms, and internal databases. Determine which agent builders offer prebuilt connectors for your stack and which require custom API development.
Platforms like Vertex AI Agent Builder support broad enterprise tool integration with built-in security and compliance features. Glean's platform connects to over 100 enterprise applications, enabling agents to access organizational knowledge and execute workflows across systems without manual data movement.
Create a requirements matrix matching each platform against your integration needs:
- Pre-built connectors: Does the platform offer native integrations with your tools?
- API flexibility: Can you build custom integrations when needed?
- Data security: How does the platform handle data in transit and at rest?
- Workflow orchestration: Can agents trigger actions across multiple systems?
- Real-time updates: Do integrations support bidirectional data sync?
Strong integration capabilities accelerate deployment, reduce maintenance overhead, and ensure agents operate within your existing workflows rather than requiring users to adopt new systems.
Consider real-world applications and use cases
Understanding how organizations deploy AI agent builders in practice helps validate platform capabilities and inspires implementation strategies. Leading companies use AI agents across diverse business functions to drive measurable outcomes.
Customer support automation represents one of the most common use cases. Companies deploy agents to handle routine inquiries, route complex issues to human specialists, and provide 24/7 assistance. Platforms like Sendbird enable support teams to build conversational agents that reduce response times and improve customer satisfaction.
Knowledge retrieval and discovery helps employees find information quickly across fragmented systems. Glean's AI agents excel in this domain, surfacing relevant documents, answers, and experts based on organizational context and user permissions.
Workflow orchestration allows agents to automate multi-step processes spanning multiple systems. CrewAI specializes in multi-agent systems where specialized agents collaborate to complete complex tasks like content creation, data analysis, or project management.
Sales enablement agents help sales teams by automating lead qualification, scheduling meetings, and providing real-time competitive intelligence. General solutions offer pre-built sales agents that integrate with CRM platforms.
Personal productivity agents manage calendars, summarize emails, and draft responses. ChatGPT-powered agents demonstrate this category's potential for individual efficiency gains.
Multi-agent systems—where multiple specialized agents collaborate—enable sophisticated automation for complex business processes like financial analysis, product development, and supply chain management. These architectures distribute tasks across agents with complementary capabilities, improving accuracy and scalability.
Test platforms with prototypes and proofs of concept
Validating platform fit through hands-on experimentation reduces risk and ensures data-driven decisions. Most AI agent builders offer free trials, community editions, or sandbox environments that enable rapid prototyping without significant investment.
Begin by defining a representative workflow that aligns with your primary use case. Choose a scenario that involves realistic data sources, user interactions, and system integrations. This approach surfaces potential issues around data connectivity, performance, and usability early in the evaluation process.
Follow a structured testing methodology:
- Setup and configuration: Measure how quickly you can create an account, configure basic settings, and access the builder interface
- Agent creation: Build a simple agent using your test workflow, noting ease of use and available features
- Data integration: Connect the agent to relevant data sources and business systems
- Testing and iteration: Run the agent with real or realistic data, collecting feedback from potential users
- Performance evaluation: Assess response times, accuracy, and reliability under expected load
- Team feedback: Gather input from technical and business stakeholders on usability and capabilities
Document your findings in a comparison matrix that tracks setup time, integration complexity, agent performance, and user satisfaction across platforms. This evidence-based approach helps you move beyond marketing claims to understand actual platform capabilities.
Rapid prototyping also reveals hidden costs, such as additional fees for integrations, API calls, or enterprise features. Testing multiple platforms in parallel accelerates your evaluation timeline and provides direct comparison data.
Analyze community support, documentation, and vendor reliability
Long-term success with an AI agent builder depends on the quality of vendor support, documentation, and community resources. These factors directly impact implementation speed, troubleshooting effectiveness, and ongoing maintenance costs.
Evaluate documentation quality by reviewing each platform's guides, API references, and tutorials. Comprehensive documentation should include getting-started guides, integration examples, best practices, and troubleshooting resources. Frequent updates signal active platform development and vendor commitment.
Community adoption provides valuable insight into platform viability. CrewAI's 900,000 downloads and active GitHub community indicate strong ecosystem support and peer resources. Platforms with vibrant user communities offer informal support channels, shared code examples, and real-world implementation advice.
Assess vendor reliability through:
- Enterprise support options: Availability of dedicated support teams, SLAs, and escalation paths
- Vendor stability: Company funding, customer base, and market presence
- Product roadmap: Transparency about future features and strategic direction
- Update frequency: Regular feature releases and security patches
- Customer references: Case studies and testimonials from similar organizations
For enterprise deployments, verify that the vendor offers professional services, training programs, and dedicated customer success management. These resources accelerate adoption and help you maximize platform value.
Open-source platforms like LangChain benefit from community contributions but may require more internal technical expertise. Proprietary platforms typically offer more structured support but may involve vendor lock-in considerations.
Make an informed decision aligned with business goals and budget
Synthesize your research, testing results, and stakeholder feedback into a confident platform selection. Use a structured scoring approach to evaluate each contender against your prioritized criteria.
Create a weighted decision matrix that reflects your organization's priorities:
<div class="overflow-scroll" role="region" aria-label="Platform evaluation criteria and weighted scoring table">
<table class="rich-text-table_component">
<thead class="rich-text-table_head">
<tr class="rich-text-table_row">
<th class="rich-text-table_header" scope="col">Criteria</th>
<th class="rich-text-table_header" scope="col">Weight</th>
<th class="rich-text-table_header" scope="col">Platform A Score</th>
<th class="rich-text-table_header" scope="col">Platform B Score</th>
<th class="rich-text-table_header" scope="col">Platform C Score</th>
</tr>
</thead>
<tbody class="rich-text-table_body">
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Agent capabilities</td>
<td class="rich-text-table_cell">25%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Integration breadth</td>
<td class="rich-text-table_cell">20%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Security/compliance</td>
<td class="rich-text-table_cell">20%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Ease of use</td>
<td class="rich-text-table_cell">15%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Vendor reliability</td>
<td class="rich-text-table_cell">10%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
<tr class="rich-text-table_row">
<td class="rich-text-table_cell">Total cost of ownership</td>
<td class="rich-text-table_cell">10%</td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
<td class="rich-text-table_cell"></td>
</tr>
</tbody>
</table>
</div>
Incorporate feedback from prototype testing and stakeholder reviews. Technical teams should validate integration complexity and customization capabilities. Business users should confirm the platform meets their workflow needs without excessive training requirements.
Consider total cost of ownership beyond initial licensing fees. Factor in implementation costs, integration development, ongoing maintenance, training, and potential scaling expenses. Some platforms charge based on agent volume, API calls, or user seats, which can significantly impact long-term costs.
Evaluate vendor lock-in risk and platform flexibility. Can you export agent configurations? Does the platform support standard APIs and data formats? How easily could you migrate to another solution if needed?
Look forward by assessing each platform's innovation trajectory. Review product roadmaps, recent feature releases, and vendor investment in AI research. Choose a platform that aligns with your organization's long-term AI strategy and demonstrates a commitment to continuous improvement.
For enterprises seeking production-ready solutions with comprehensive organizational integration, Glean's Agent Builder provides enterprise-grade security, seamless connectivity to over 100 business applications, and deep integration with organizational knowledge. Its approach to AI agent creation balances ease of use with advanced capabilities, making it suitable for organizations at any stage of AI adoption.
The right AI agent builder transforms how your organization works by automating routine tasks, surfacing relevant information, and enabling employees to focus on high-value activities. A methodical evaluation process ensures you select a platform that delivers measurable business value while supporting your long-term AI vision.
Frequently asked questions
What criteria should I use to evaluate AI agent builders?
Focus on agent capabilities, integration with existing systems, security and compliance certifications, scalability, and the quality of vendor support and documentation.
How do I choose between no-code and developer-focused platforms?
Choose no-code platforms for non-technical teams needing fast deployment, and select developer-focused solutions when your team requires advanced customization and integration.
What integration capabilities are important in an AI agent builder?
Look for seamless integration with critical tools like CRM platforms, messaging apps, data warehouses, and workflow automation systems to ensure agents operate within existing workflows.
How can I assess the security and compliance of a platform?
Evaluate compliance certifications like SOC 2 or ISO, data protection features, and auditability to ensure the platform meets your organization's regulatory and governance requirements.
What level of support and community should I expect from an AI agent builder?
Seek platforms with active user communities, comprehensive documentation, and responsive vendor support channels to ensure smooth onboarding and ongoing success.
