There’s no need to confine your AI use to a ChatGPT window. AI agent builders now let you create customized AI agents of all kinds. WIth platforms like the ones in this list, businesses can actually build agentic AI capabilities right into their existing systems.
These platforms have different use cases and different skill-levels attached. They cover a spectrum of expertise from true no-code platforms, allowing anyone on your team to build, to developer-specific tools that need technical knowledge.
They are each best for building different kinds of AI agents. Some can help you build custom apps for your business with agentic abilities. Others are focused on connecting the apps your’re already using with AI agents in between, creating chatbots that are specifically tailored to your needs, or managing high volumes of enterprise data.
This guide is designed to help you choose which tools to use to create your own custom AI agents. In 2025, there’s no reason any business shouldn’t be gaining the power of AI to become more efficient, help teams get knowledge more effectively, and create intelligent systems that automate processes of all kinds.
Quickstart AI agent builder guide:
OpenAI GPTs: For building chatbots that serve specific purposes, no code needed.
Glide: For building fully-featured custom apps with AI agents built-in, no code needed.
Zapier: For connecting existing software with agentic capabilities in between, no code needed.
Anthropic (Claude): For building data-heavy, secure enterprise assistants, some code required.
LangChain: For highly customized or autonomous enterprise agents, developers only.
Learn more about AI agents
Read the guide1. OpenAI GPTs: Fast, lightweight chatbot assistants
OpenAI’s GPTs are custom versions of ChatGPT that users can create themselves. They are personalized chatbots powered by OpenAI’s GPT-4 model (and successors) that can be quickly deployed without coding.
Introduced in late 2023, OpenAI’s GPT builder platform is a no-code way to make custom AI agents specialized for particular tasks. It’s available to ChatGPT Plus and Enterprise users, allowing anyone to spin up a chatbot with a specific knowledge or skill. The idea is to make AI assistants very easy to create and share, emphasizing speed and simplicity over deep customization. Using OpenAI’s GPT builder involves configuring an app with a visual interface rather than programming. Anyone can create one without any programming skill.
You start by giving the new GPT a name and a description of its purpose. You can provide it with extra knowledge (for example, a document or a set of instructions) and set its personality or tone. You can also enable tools or plugins for it, such as web browsing, calculators, or third-party service plugins, depending on what “skills” you want it to have. In the end, you have a chatbot that’s tailored to a specific use case.
The main strength of OpenAI GPTs is how fast and lightweight they are. Need a quick helper for a one-off project or a personal task? You can make a GPT for it rather than waiting for a full app to be developed. For instance, a product manager could create a “Meeting Summarizer GPT” that has access to a transcription plugin: after each meeting, they feed it the transcript and it spits out key points and action items. All of this without writing any code or involving a developer. Another scenario: an HR team could craft a GPT that answers common employee questions about company policy by feeding it the employee handbook. They can share the chatbot link with the whole company, avoiding repetitive Q&A sessions. Since GPTs can be kept private or shared publicly, they offer a quick way to deploy AI assistance either internally or to end-users.
Of course, GPTs are constrained by the ChatGPT environment. Unlike Zapier Agents, they can’t directly connect to external apps without extensive plugins, and unlike Glide Apps, they can’t connect to and use your personal data beyond a basic document. They also rely on OpenAI’s models, so you are somewhat limited to what GPT-4, or other provided models, can do and the platform’s rules. However, for many cases, this is more than enough, and the benefit is that you get a polished, reliable chatbot with minimal effort.
Best for: Rapid creation of personalized AI assistants for specific purposes. Great for prototyping ideas or deploying helpful chatbots without any infrastructure.
Typical use cases: Personal assistants (for productivity, learning, planning), lightweight customer support bots (answering FAQs), team knowledge base Q&A bots, or any scenario where you want a conversational helper focused on a niche topic or task. Essentially, “small-scale” assistants that improve daily life or work tasks (e.g. a travel planner bot, a code snippet generator for a dev team, etc.).
Coding level: None. This is a purely no-code setup through a GUI (graphical user interface). If you can chat with ChatGPT, you can create a custom GPT. You might need to input information or choose settings, but no programming is involved. Technical users can extend functionality via plugins.
2. Glide: Fully featured no-code apps with AI agents incorporated
Glide is a true no-code app builder that can be used to create customized business tools with AI agents built in.
Glide enables non-technical users to quickly create custom AI agents, business apps, and automated workflows powered by AI. While it can create standalone agents (like the pre-built AI resume screener), it’s most valuable when used to create custom business tools (like a customer portal, inventory manager, or dashboard) with agentic capabilities baked right in for the user. If you have data sitting in a spreadsheet that needs to be used or a process that needs automation, Glide is a great choice.
Glide is know for its clean, professional design. It’s easy to create beautiful streamlined interfaces, making it well suited for creating AI agents that need to be customer-facing. Glide apps are automatically mobile-adaptive, so they allow your AI agent to be used on a computer, tablet, or smartphone. This makes agents more useful for users who are in the field, in a warehouse, or on a retail floor.
You AI agents use managed AI, meaning you build the feature you need and the platform selects the best AI model for your use case. This means you also get the benefit of high-volume enterprise AI (faster, more secure, and not training on your private data) even if you aren’t using that model extensively.
Glide is good for businesses that can benefit from intelligent automation but don’t have the time or engineering resources to program from scratch. It has native workflows that let you automate tasks on a timer, triggered from other software, or set off by a user’s action. Since it’s so adaptable and easy to use, teams can build their own AI agents to streamline any process they need without worrying about IT resourcing.
For instance, the agency MintLeads created a, AI sales agent to streamline their sales calls. The agent automatically summarizes leads by pulling data from HubSpot CRM for the salespeople before calls and drafts proposal emails in Gmail so they’re ready to send instantly once the call wraps. By automating prep and follow-up, each salesperson saved about 15 minutes per call, doubling the number of calls they could handle. In another case, a construction company built an AI field report agent to compile client updates from site notes and photos, cutting update prep time by 90%.
Glide’s no-code interface means business users can configure AI agents without any programming. You connect your spreadsheets, databases, or other data sources, and design logic through Glide’s app builder. The AI components (powered by large language models) can then be added to perform tasks like writing summaries or answering queries. Glide is specifically designed to be acessable, but it also has the advantage of a large community of professional Experts who can step in to build solutions even faster and add more advanced AI capabilities as-needed.
Best for: Creating custom business apps with agentic capabilities built-in. Glide is best for businesses that need AI agents that use your unique business data and automate repetitive business processes within a professional interface.
Typical use cases: Customer portals, internal knowledge management tools, sales assistants (e.g. lead summary & proposal generation agents), project management helpers, mobile apps or any process that needs to deeply integrate with company data (CRMs, spreadsheets, databases, etc.).
Coding level: No-code. Glide is designed for non-programmers or developers that need to work quickly. If you can build a spreadsheet or slide deck, you can use Glide. All configuration is done via drag-and-drop and settings, with no coding required.
3. Zapier Agents: Simple app connections & automation
Zapier has long been known for connecting web apps and automating workflows without code. Zapier acts as the glue between thousands of apps, allowing users to create “Zaps” (trigger-action rules) that move data and perform tasks automatically. This no-code automation approach empowers non-technical users to integrate services like Gmail, Slack, Salesforce, etc., through a visual builder. In 2025, Zapier has expanded into creating AI agents, enabling users to create simple agent-driven automation.
Zapier Agents (in beta as of April, 2025) is an experimental AI workspace where you can create AI assistants that work across the 7,000+ apps supported by Zapier.
Zapier is another true no-code tool, and is ideal for creating agents that perform actions between apps. It won’t create a web app like Glide. Instead, Zapier serves as the connection between whatever software you’re already using. Its agents could prep you for a sales call by crawling your calendar for new contacts, searching Google for info, creating summarized notes about your lead, and sending you that info in a Slack channel. Zapier AI creates the connectiom between Google Calendar, Web Browser, and Slack, replacing that human work.
Zapier is also introducing AI Chatbots in beta that combine conversational AI with its automation backend. This means you can spin up a chatbot, like a to-do-list-manager, that not only talks with users but also uses Zapier to actually add tasks to your task app when instructed. Zapier’s visual workflow editor and templates handle connecting APIs so non-technical users can create pretty sophisticated multi-step automations by simply configuring rules and prompts.
Best for: Connecting apps and automating multi-step workflows with ease. Zapier is great for workflow automation and now adds AI to trigger those workflows in a conversational or intelligent way.
Typical use cases: Notifications and data syncing (e.g. update a Slack channel when a form is submitted), AI-driven assistants that perform tasks like scheduling meetings, updating records in CRM, or populating spreadsheets based on a chat command. For instance, an AI chatbot that adds items to your to-do list or pulls data from Google Sheets on request.
Coding level: No-code. Zapier is built for non-coders; its drag-and-drop editor and templates mean you don’t have to write code to integrate apps. (For advanced users, there are options like Zapier Functions for custom code, but most automation can be done without coding.)
4. Anthropic (Claude): Enterprise agents for high-volume data
Anthropic’s Claude is an AI assistant developed with an emphasis on reliability and ethics. As a platform for AI agents, Anthropic targets enterprise-level needs and is only available at an enterprise-level price. It’s designed for large organizations that are looking for powerful AI capabilities, but also require safety, security, and scalability.
Claude itself is a large language model similar to GPT-4 and can be accessed via an API and dedicated enterprise interfaces. What makes Anthropic’s solution unique is its focus on handling complex tasks with huge amounts of context and its ability to complete tasks in a controlled, private way.
Anthropic’s Claude can work with a very large context window. In an enterprise plan, Claude can handle up to 500,000 tokens of context, meaning it can ingest and reason over hundreds of pages of text in one go. This makes it able to handle use cases like analyzing long financial reports, legal documents, or large knowledge bases. Claude can take in all that information and provide a summary or answer questions without needing chunking.
Claude can be used for things like brainstorming, streamlining internal documentation, and even coding assistance. Importantly, Anthropic ensures that customer data is kept private. They don’t train their model on your conversations or content, and the enterprise plan includes features like single sign-on, role-based access controls, and audit logs for compliance. This focus on data security and privacy is crucial for businesses in regulated industries.
Anthropic is pushing how autonomous AI agents can be. In late 2024, they introduced an advanced feature called “Computer Use” in Claude. This essentially allows the AI to simulate using a computer – it can move a mouse cursor, click on buttons, and type commands to complete multi-step tasks on its own. According to Anthropic’s chief scientist, this means Claude can, for example, “move the mouse, click on specific areas, and type commands to complete intricate tasks” like writing code or using Google Search to gather information.
In practice, an enterprise could set up Claude to perform an automated workflow such as: read an email, open an internal web app, input data, generate a report, and send it off. This level of autonomy is suited for heavy-duty use cases where AI ia acting as a virtual executive assistant that operates software for you. Anthropic’s ongoing research into agentic behavior is one reason Claude is seen as a top choice for complex task automation.
To use Claude as an agent platform you’ll usually interact with its API or interface rather than a drag-and-drop builder. Enterprises may integrate Claude into their applications (for example, adding it to a customer support chat system, or an internal Slack bot) or use partner platforms like AWS Bedrock to deploy it.
While not “no-code” in the Glide/Zapier sense, it doesn’t need as much coding as LangChain. Most of the AI’s capability is available out-of-the-box. Developers or IT will mainly be needed to hook Claude up to the right data sources and define its role. Anthropic provides guidelines and support for “prompt engineering” to guide Claude’s behavior, using their Constitutional AI approach to help it stay on track.
Best for: Handling complex, multi-step tasks and large volumes of information in a reliable, safe way. Claude is built for enterprise needs; high volumes, strict data privacy, and integration into business workflows.
Typical use cases: Enterprise knowledge assistants (answering questions based on huge internal knowledge bases), report generation and document analysis (summarizing a 100-page policy document), coding assistants for developers (Claude has strong AI coding capability improvements), and autonomous task bots (an AI that can use internal tools to carry out a task, such as onboarding a new employee across different IT systems).
Coding level: Low-code to Medium. Setting up Claude for use can require some coding or IT configuration, like using APIs or integration platforms. You don’t write the agent’s logic from scratch since Claude handles the AI reasoning. Non-technical end users interact with Claude through chat interfaces, while developers need to make sure it’s properly set up.
5. LangChain: Deep agent customization for developers
LangChain is an open-source software framework that helps developers build AI agents and applications powered by large language models. In contrast to the no-code tools, LangChain is code-centric, typically used in Python or JavaScript, and has a large library of components that can be used to design complex LLM-driven agents. If you have technical skills and need fine-grained control over how an AI thinks, what tools it can use, and how it integrates with data, LangChain will give you the building blocks.
LangChain is basically an AI agent construction kit for developers. You get pieces such as chat model interfaces, vector databases for embedding-based search, and connectors to APIs, and you assemble the exact agent your application needs.
What LangChain is best at is deep customization of AI workflows. It gives you modular components for things like prompts, memory, tool usage, knowledge retrieval, and more. Developers can chain these together (hence the name) to create sophisticated agents. For example, you could build an agent that first retrieves documents from a database, then asks the LLM to summarize them, then uses an external calculator tool if needed, etc. LangChain even supports autonomous agents that can decide their own actions. The AI can choose which tool to invoke at each step, like performing a web search or running code, as it works towards an answer in a flexible reasoning loop.
One notable feature of LangChain is its support for stateful agents with memory and advanced debugging tools. It includes LangGraph for building agents that maintain state over a conversation and even involve humans in the loop when necessary, and LangSmith for testing and monitoring your AI chains. This is important for enterprise developers who want to evaluate an agent’s performance or check their reliability before deployment. Because it’s open-source, LangChain also lets you integrate any third-party service; many developers use it alongside APIs from OpenAI, Anthropic, or others so they can use powerful language models inside a custom framework.
Real-world use cases for LangChain tend to be highly specialized projects. Companies have used it to build internal chatbots that ingest proprietary documents and answer employee questions with citations. Others have created agents that perform multi-step research tasks like browsing the web, pulling data, then composing a report. Another common use is for “vibe coding” assistants. LangChain can be used to create an agent that writes and executes code to solve a problem, checks the output, and iterates. This idea was popularized by tools like AutoGPT, which can be implemented with LangChain.
Best for: Highly customizable AI agent development for enterprise companies with available time, resources, and engineering talent. It’s suited for crafting a bespoke reasoning process or integrating AI deeply with various tools/data. LangChain is best when you need complex logic, custom data pipelines, or specialized model tuning.
Typical use cases: Developer-built chatbots that use proprietary data (e.g. an AI trained on your company’s knowledge base), autonomous research agents that use multiple tools (search, calculators, code execution), and any experimental AI system where you need to tweak the agent’s “brain” at a low level.
Coding level: High-code. LangChain is intended for developers. You’ll be writing Python/JavaScript and working with AI model APIs. Some understanding of prompt design and AI concepts is also needed to use it effectively.
Other tools for building AI agents
The landscape of AI agent builders is only growing. While the tools above are solid choices for most businesses, there are other platforms that can help if you have more engineering resources or specialized needs. Some might even be used in conjunction with other agent builders or systems. Here are some other AI agent builder options:
LlamaIndex: Data-driven AI agents
LlamaIndex (formerly GPT Index) is a tool built specifically to empower AI agents with access to private data. It helps connect language models to your data sources, likedocuments, PDFs, SQL databases, APIs, by transforming them into a format that can be easily searched and referenced during conversations. This makes it ideal for building retrieval-augmented generation (RAG) agents.
Businesses use LlamaIndex to create internal knowledge assistants or customer-facing chatbots that answer questions based on company documents. For example, a legal team might create an AI agent that can search and summarize content across thousands of policy documents, while a SaaS business could build a chatbot that answers onboarding questions using the help center.
You can use LlamaIndex with LangChain or as a standalone tool. It requires some programming knowledge, but is relatively beginner-friendly if you’re already comfortable with Python.
Best for: Making your internal data searchable and usable by AI agents. Enables reliable question answering and document summarization.
Typical use cases: Company knowledge base bots, internal Q&A agents, legal or technical document assistants.
Coding level: Medium. Requires Python knowledge but is simpler than building an entire agent framework from scratch.
Vellum: Managing agentic AI systems
Vellum AI is a developer-first platform for deploying and monitoring reliable AI agents at scale. It focuses on versioning, monitoring, and evaluating LLM workflows, making it a good choice for enterprises that need observability and performance. Vellum helps teams manage how AI models are prompted, monitored, and iterated rather than acting as the AI model itself,
Companies use Vellum to ensure consistent behavior in production AI agents, particularly ones that are interacting with end users. For example, a fintech firm might use Vellum to monitor how their AI assistant handles client onboarding chats and make sure that model updates don’t introduce regressions.
Excels at: Managing, testing, and deploying AI agents in production with version control and monitoring.
Typical Use Cases: QA testing for agents, AB testing different prompts, maintaining reliability across model updates.
Coding Level: Low to Medium. Developers will configure agents through a UI or API, but no deep ML experience is required.
Reka: Multimodal AI agents
Reka is a newer entrant focused on building multimodal AI agents—systems that can process and respond using text, images, and video. It's targeted toward research and high-performance commercial applications where reasoning over multiple data types is important.
Use cases include document processing that uses both visuals and text (e.g. financial documents with charts), or AI agents for design workflows that need to interpret sketches or screenshots. Reka has API access for its own models and high flexibility for building domain-specific agents.
Best for: Advanced agents that need vision and language models and multimodal reasoning.
Typical use cases: Research bots, document intelligence agents, creative workflow assistants.
Coding level: High. This is a developer-focused platform requiring experience with APIs and model configuration.
AutoGen: Multi-agent collaboration framework
Developed by Microsoft, AutoGen is an open-source framework for multi-agent systems, where multiple AI agents collaborate to complete tasks. AutoGen is good for orchestrating role-based interactions. It can be used to create a “researcher” agent that gathers data, a “coder” agent that writes scripts, and a “QA” agent that tests them.
AutoGen is designed for advanced use cases like autonomous software generation, AI research assistants, and complex workflows involving several types of reasoning. This ability has made it popular for internal R&D tools and developer productivity bots.
Best for: Multi-agent orchestration and role-based task division.
Typical use cases: Autonomous research agents, collaborative coding agents, and simulated team workflows.
Coding level: High. Built in Python, requires developer expertise and experience with agent planning.
Build an AI agent step-by-step
Learn howBuild AI agents that work for your unique needs
In 2025, any business can effectively employ AI agents to save time, enhance customer experiences, and drive innovation in your projects. An AI agent is really a classification for a new category of tool that helps build efficiency into any business system.
When it comes to deploying a specific agent, businesses should look at what skillsets and resources they have and which parts of their business need the most help from AI. Many businesses will likely end up using a variety of these tools for different purposes.
If you’re interested in learning more, try this guide to creating custom AI agents to get started.
