How to Create AI Agents in 5 Steps: A Complete 2025 Guide

Building AI agents might sound complex. But you don’t need to be a developer to learn. The process of making AI agents starts by setting clear goals and choosing the right platform. 

Check out the top platforms like Lindy to rapidly create no-code AI agents, Relevance if you want a flexible building interface, and LangChain if you’re a developer. 

Read on to learn more about:

• What AI agents are and how they work,

• Skills and tools you’ll need

• 5 steps to create AI agents

• 10 platforms for AI agent creation

• Common pitfalls to avoid, key features to add, and how to make your AI agent reliable

• Real-world AI agent examples

What is an AI agent?

An AI agent is a self-directed software system powered by large language models (LLMs) like ChatGPT or Claude. Once given a goal or trigger, it can plan, make decisions, and perform tasks independently. This means you can handle both simple and complex workflows — from managing email to completing entire sales processes — without giving step-by-step instructions. 

AI agents differ fundamentally from chatbots and scripts. Chatbots (not to be confused with AI chatbots) typically offer single-turn or guided responses. They answer user questions and can hand off duties to a human when they recognize the situation is too complex. Scripts, like rule-based chat widgets or e-commerce order trackers, follow fixed, logic-driven paths with little to no adaptability. 

They also contrast with LLMs and reactive agents. LLMs like ChatGPT only generate responses based on user-based text, voice, or visual input — they don’t take action. Reactive agents like Zapier can also trigger actions across apps, but only in response to fixed configurations. They lack memory, reasoning, and the ability to adapt to evolving context.

In contrast, AI agents don’t just respond, they act. They act by taking initiative — they plan next steps, and autonomously connect with other software. This enables them to execute tasks, evaluate outcomes, and adapt to context across multiple platforms or channels.

AI agents vs. other systems: At a glance

How do AI agents work?

AI agents operate by following a repeatable loop. They perceive → reason → act, which enables them to solve tasks without constant human input. Let’s look at each of these steps in detail:

1. Perceive: The agent takes in data from its environment — this could be an incoming email, a CRM update, or even a user prompt. It pulls relevant context from memory (its context window). This may include past interactions, task history, or external knowledge sources, such as databases or documents.

2. Reason: Once the agent has perceived and understood the input, it evaluates what it’s being asked to do. Based on the specific workflow you set up, the agent uses logic to decide the next best step and plans a sequence of actions. The agent’s reasoning is powered by large language models (LLMs). They figure out what you mean and turn that into clear, structured actions — even if your input is a bit vague.

3. Act: The agent then takes action — calling an API, writing an email, updating a spreadsheet, or passing control to another agent. Depending on its setup, it may loop back to the “Perceive” stage and reflect on the result, log it, or decide whether further action is needed.

Here’s an example of how an AI executive assistant operates. First, it scans your calendar and unread emails (perceive) and realizes that a meeting was rescheduled (reason). It then notifies your team via Slack or email, rebooks a Zoom call, and updates your CRM (act) — without you lifting a finger. Then it checks for follow-ups and starts the loop again.

What tools and skills do you need before you start?

Getting started with AI agents doesn’t mean you need a computer science degree or hours of prompt engineering under your belt. Top platforms like Lindy and Rivet are no-code tools: You’ll use a drag-and-drop interface instead of coding your agent from scratch. 

But knowing a few foundational concepts will help you understand the underlying logic that configures your agent’s thinking. This knowledge will also help create effective agents by structuring tasks effectively and connecting your workflows to real-world apps and data. Understanding these foundational concepts gives you an advantage:

• Basic API knowledge: An API can plug into software tools and transfer data, which will help when connecting agents to CRMs like HubSpot or tools like Google Sheets.

• CRMs and workflow tools: Platforms like Airtable, Notion, Trello, or Salesforce help you manage tasks using database tools and drag-and-drop editors. AI agents often integrate directly with these tools to read, write, or update records as part of an agent workflow.

• Prompt design and task clarity: Experience writing prompts with tools like ChatGPT or Gemini will make it easier for you to communicate with your agent. By writing clear instructions like “Send a follow-up email if no response in 3 days” or “Qualify this lead using LinkedIn info,” your agent will know exactly which tasks to execute.

• Testing and iteration: Most platforms include sandbox (testing) environments where you can preview, test, and tweak your agent’s behavior. Just as testing a marketing campaign or product feature helps ensure the agent performs reliably before scaling it, this approach also helps ensure the agent performs reliably.

Once again, none of the above are prerequisites. You don’t need development skills to build an AI agent — just need clear goals, the right tools, and smart execution. Let’s discuss how to create one now. 

How to create an AI agent in 5 steps

Learning how to create AI agents doesn’t have to be overwhelming. You can start with simple tools and scale up as your needs grow. I’ll walk you through a 5-step guide for creating an AI agent with a no-code platform like Lindy:

Step 1: Define your agent’s job

Before you build, clarify what your AI agent is intended to do. Is it a one-time email follow-up, or a daily task like handling new inbound leads? Grab a pen and paper and jot down the precise functions you want your agent to execute. 

This decision affects how you create prompts, set up memory, and automate tasks. For example, an agent that only executes one task at a time requires particular, standalone prompts. An agent that completes multiple steps, on the other hand, benefits from threaded context and long-term memory. This allows it to handle complex, evolving tasks over time. 

Understanding this difference helps you choose the right agent framework, whether you're optimizing for speed, scalability, or deeper task automation.

Step 2: Choose your platform or framework

No-code tools like Lindy, Rivet, or Bedrock Agent enable you to create AI agents using drag-and-drop builders. This makes them ideal for freelancers, startup teams, and operations professionals with limited engineering or tech support. These platforms include integrations, prompt libraries, and customizable templates.

For more control, developers may opt for code-based frameworks such as LangChain, React, or CrewAI. These offer deeper logic, custom memory stacks, and chaining mechanisms — but they require Python, infrastructure setup, and familiarity with LLM agent frameworks.

Step 3: Set up triggers, inputs, context, and integrations

Your agent starts working when it receives a prompt or sets off a trigger. A trigger can be receiving an email or Stripe payment, or creating a new CRM profile. These triggers kick off the perceive→ reason → act loop.   

Next, define your context window: The combination of memory and instruction that shapes how your AI agent thinks and responds over time. The context window determines what the agent "remembers" from previous interactions. Should it recall a user’s name, company, or preferences? Should it track progress toward a specific goal? 

Embed key instructions that guide its behavior, like tone, task scope, or fallback protocols. A well-structured context window helps your agent stay consistent, relevant, and effective across every step of the workflow.

Then, integrate external tools or APIs like Google Sheets, HubSpot, Slack, or internal databases. These connections enable your agent to move beyond conversation and perform real-world actions, such as updating records, sending messages, or triggering workflows automatically.

Step 4: Build a test loop

Run simulations using real-world inputs, such as a sample lead, user inquiry, or data event, and observe how your agent responds. Find out if it finishes the task or gets stuck. If it fails, work your way backwards through the loop to pinpoint the issue.

Always log errors, unexpected loops, and failures, so you can fine-tune instructions, adjust memory, or fix tool access. Testing your agent’s behavior before launch ensures reliability and prevents poor user experiences.

Step 5: Evaluate and deploy

Once your agent consistently executes test tasks, it’s time to move from prototype to production. Review how it manages unexpected scenarios, inputs, and real-world data.

Then, think about how your team, leads, and clients will work with your agent. Consider the touchpoints: Should users trigger the agent manually, receive automated outputs, or collaborate with it via chat? Choosing the right interaction shapes the user experience and ensures your agent fits seamlessly into daily workflows.

No-code vs. code-based AI agent platforms

The 10 best tools & frameworks for creating AI agents

My list highlights top solutions like Bedrock Agent, LangChain, Lindy, and others. I’ve selected platforms that cater to beginners and those specifically designed for developers. Here are the top 10 platforms for creating your own AI agents: 

1. Lindy: The best no-code agent builder

Lindy is a no-code platform designed for non-technical users who want AI agents to automate tasks across sales, marketing, support, and operations. For example, you could use it for sales pipeline tasks like automated lead gen, enrichment, qualification, and outreach.

Pros

• 100s of templates for real-world workflows: Rather than starting from scratch, you can use ready-made agents for popular workflows like inbox triage, sales follow-up, or contact syncing. 

• Several third-party integrations: Lindy natively integrates with popular apps like Stripe, Gmail, Slack, Zoom, and HubSpot. And, by partnering with Pipedream and Apify, Lindy connects to over 2,500 apps.

Cons

• Complex automations take time: Building multi-step automations is more challenging than creating AI agents from templates. To learn how to create AI agents from scratch with Lindy, visit the Lindy Academy.

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2. Relevance AI: Modular and flexible

Relevance AI is a low-code platform designed to help teams build and deploy AI agents tailored to business-specific tasks. It’s ideal for teams that want to develop AI workflows for e-commerce, customer service, and analytics. Use cases include customer feedback analysis, chat-based support agents, or agents that monitor KPIs (key performance indicators). 

Pros

• Flexibility: Relevance AI offers a node-based interface where you can create agents by chaining logic, tools, and models into modular workflows.

• Low-code extensibility: Low-code customization and API support allow you to embed agents into existing systems or expand functionality using Python scripts and custom API connectors.

Cons

• Steep learning curve: Advanced capabilities like dynamic memory, chaining custom logic, or vector operations often require understanding of LLM internals, semantic workflows, and basic Python/JavaScript knowledge.

3. LangChain: For devs building custom flows

LangChain is a developer-first open-source framework designed for building advanced, customizable LLM-powered agents and applications. It’s suited for software engineers, technical founders, and data scientists. LangChain lets you build the logic for autonomous agents that can generate research summaries, with support for custom workflows like document parsing.

Pros

• Developer-friendly: LangChain offers granular control over every aspect of agent creation, from prompt design to multi-agent orchestration. You can mix and match models, define custom tools, and route logic. 

• Large community: LangChain has a large open-source community with plugins, projects, and great troubleshooting support. Frequent updates and detailed documentation make it easier for users to find a reliable tutorial on how to create AI agents and get hands-on quickly.

Cons

• For developers only: Beginners unfamiliar with LLMs, Python, or AI agent architecture may struggle to understand how components fit together.

4. Botpress: Agent SDK (Software Development Toolkit) + orchestration

Botpress has a robust SDK and orchestration layer tailored for teams building conversational agents. It’s ideal for customer experience teams, enterprise developers, product managers, and technical consultants. It also excels at creating intelligent support bots that escalate issues, appointment schedulers that integrate with calendars, or sales agents that pull data from CRMs.

Pros

• Powerful agent SDK for custom logic: Botpress’s SDK offers a flexible and extensible way to define agent behavior, integrate third-party tools, and inject business logic.

• Orchestration layer with memory and guardrails: The platform includes built-in orchestration features like memory control, logic flows, and behavioral guardrails. Agents can engage in multi-turn conversations, recover from failures, and make informed decisions based on previous user interactions.

Cons

• Limited flexibility for non-conversational agents: Botpress primarily focuses on conversational agents, though it now supports more advanced integrations for broader workflows.

5. OpenAI Assistants API: Plug into the OpenAI ecosystem

The OpenAI Assistants API lets you build structured agents that use the same underlying models as ChatGPT. It’s best for developers and AI product teams. Industries like SaaS, finance, education, and healthcare can use the API to create custom conversational agents that they can include in web apps or internal tools.

Pros

• Deeply integrated with Open AI ecosystem (GPT-4): This simplifies tool calling and persistent threads, which can enable you to create memory-aware agents capable of handling multi-step tasks.

• Reliable and secure: Because OpenAI maintains it, the Assistants API benefits from high uptime, enterprise-grade security, and continuous model improvements. It also includes safeguards like rate limits, content moderation, and sandboxed tool usage

Cons

• Tied to OpenAI’s platform and pricing: You're locked into OpenAI's models, pricing tiers, and infrastructure. This can be a drawback if you want to experiment with other LLMs or need to control your costs. 

6. Beam: Multi-agent coordination

Beam is a platform purpose-built for orchestrating multi-agent systems to collaborate on complex tasks. It’s designed for technical teams in industries like logistics, research, and product ops. Use Beam to build a research assistant team or automate ticket triage and escalation across departments.

Pros

• Built for multi-agent workflows: Beam’s architecture supports agents that specialize in different functions, such as researching, verifying, or executing tasks. They can work in parallel or hand off tasks intelligently.

• Developer-focused with Python SDK: The platform’s Python SDK enables easy definition of agents, assignment of capabilities, and orchestration of coordination patterns, such as manager-worker or peer-to-peer.

Cons

• Early in ecosystem maturity: Community support, documentation depth, and third-party resources are still growing. Compared to frameworks like LangChain or OpenAI’s Assistants API, Beam has fewer tutorials. 

7. Make.com: Visual agent workflows

Make is a no-code automation platform that lets you design AI-powered workflows using a visual, drag-and-drop interface. It’s popular across e-commerce, SaaS, finance, and creative services. You can create agents that score leads in a CRM, generate content drafts, and monitor feedback channels.

Pros

• Extensive flexibility and customization — without code: Make is a strong choice when you need detailed control over data routing, transformation, and processing logic.

• Cost-effective: Make provides a generous free plan and affordable paid options beginning at just $9/month.

Cons

• Not optimized for multi-agent systems: Make is excellent for single-agent automations, but not built for coordinating multiple AI agents with defined roles or shared context. If your project requires collaborative agents for research, summarization, and sending notifications, Make will feel limited.

8. CrewAI: LLM agents with memory + tools

CrewAI is a Python-based framework designed to build and orchestrate multi-agent systems where each agent has defined roles. It’s geared toward technical product teams and AI engineers. Utilize Crew to create SEO content pipelines, conduct compliance reviews, and develop workflow agents that can independently execute delegated objectives.

Pros

• Structured roles and coordination between agents: Agents communicate through messages and hand off work just like humans.

• Long-term memory and tool access: Each agent can maintain memory across interactions and utilize external tools, like APIs, file systems, and databases. Combined with tool access, agents can retrieve data, process input, and act autonomously.

Cons

• Limited UI and deployment features: There’s no built-in interface for monitoring agents, visualizing memory, or managing workflows in production. You must build your own dashboards or deploy logic within other platforms.  

9. Vertex Agent Builder: Google Cloud-native

Vertex AI Agent Builder is Google Cloud’s low-code platform for creating enterprise-ready conversational agents and AI workflows. It’s ideal for product teams, enterprise developers, and IT leads. Use the platform to build customer support AI bots that access internal databases, travel planning assistants, or insurance claims processors.

Pros

• Integrated with Google Cloud ecosystem: The platform integrates with Google Cloud services like BigQuery, Firebase, and Cloud Storage. You can ground responses in your private datasets and control access through Google’s robust IAM settings.

• Low-code UI with advanced configurability: The low-code interface helps non-engineers define goals, flows, and grounding sources. Developers can fine-tune models, define tool functions, and deploy agents across environments.

Cons

• Not optimized for multi-agent use cases: Vertex is great for building single, robust agents, but lacks native support for multi-agent collaboration or delegation patterns.

10. Zapier AI Actions: Automation + LLMs

Zapier is an original automation platform, connecting to over 7,000 third-party apps to run automated workflows. It now offers AI tools to build custom agents that fetch and process data from your favorite apps without coding.

Pros

• Fast setup: Zapier excels at automating simple tasks across apps. Its vast library of ready-made templates and chatbot-style interface makes it easy to launch workflows.
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• Unmatched app integrations: With over 7,000 supported apps, Zapier connects to more third-party platforms than nearly any other automation tool on the market.

Cons

• Limited AI model control: Zapier limits LLM selection flexibility but allows integration with top models through partner APIs.

Common pitfalls to avoid

Many folks who create AI agents get caught in traps like making a simple workflow complicated or mixing up memory settings. Here's how to avoid the most common issues and build effective agents that scale:

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• Too many tools, no clear goal: Jumping between multiple platforms without a precise objective results in unfocused builds. Start with a defined problem and choose only the tools essential for solving it.

• Overcomplicating prompts: Overly complex or vague prompts confuse agents and increase failure rates. Stick to clear, action-oriented instructions that guide the agent without relying on human-like reasoning.

• Agents without fallback mechanisms: If an agent fails a task and has no backup plan, it stalls or returns errors. Always build in fallback steps, retries, or human handoff options to ensure resilience and reliability.

• Forgetting user input validation: Without input checks, agents can misinterpret or act on incomplete or invalid data. Adding validation prevents logic errors, wasted operations, and frustrating user experiences.

• “Stuck” agents without an end state: Agents without a defined stop condition may loop or freeze during execution. Define a clear success condition, timeout rule, or fail-safe to ensure the agent exits when needed.

Avoiding these pitfalls only requires a strategy, thoughtful design, and clear workflows. With the right approach, you can build reliable and effective AI agents that perform work at scale.

What makes a great AI agent? 4 key features

Great AI agents are designed to tackle real tasks, adjust to different contexts, and manage setbacks. Here are four traits that separate simple bots from truly effective, scalable systems:

1. Goal-driven, not prompt-driven: Effective AI agents are built around clear goals. Rather than reacting to every input, they use logic and steps to pursue a clear outcome.
   
   
2. Operates autonomously with supervision: Great agents execute tasks independently while allowing for human oversight, such as confidence-based handoffs or review checkpoints, ensuring reliability without micromanagement.
   
   
3. Has memory or persistent context: An AI agent with memory, or persistent context, can reference past actions, user input, or instructions across steps.
   
   
4. Can handle exceptions (edge cases) or escalate: The best AI agents don’t break when things go wrong. They validate input, retry failed actions, and know when to escalate to another agent or human.

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How safe and reliable are AI agents?

Modern AI agents can be highly reliable — if built with the right structure and safeguards. Here are some features to include that help ensure an AI agent is safe and reliable:

• Output validation: Ensures the agent’s responses meet specific format, logic, or content rules. This reduces unnecessary errors or mistakes before the output goes to users.

• Action logging: Captures every decision, tool call, and response during execution. This makes it easier to debug issues, audit behavior, and continuously improve agent workflows over time.

• Tool permissioning: Tool permissioning limits API access to prevent risks like data deletion, financial misuse, or privacy breaches.

• Fallbacks and escalation paths: When agents encounter failure or uncertainty, fallback logic or escalation to a human ensures the workflow continues smoothly. 

With the proper setup, AI agents can be as dependable as any team member. By combining structure, oversight, and smart design, you’ll build agents that automate tasks safely, reliably, and at scale.

AI agent examples & use cases

AI agents are transforming the way businesses operate by automating repetitive tasks and executing goal-driven workflows across departments. Here are some real-life use cases of effective agents:

• Meeting note taker + team notifier: Lindy’s Meeting Note Taker agent can join virtual meetings, transcribe discussions, and generate clear summaries with action items. It then notifies your team via Slack or email, keeping everyone aligned.

• Ticket triage + summaries: Use Zapier and GPT in tandem to read incoming Zendesk or Gmail tickets, classify the topic, generate a response, and store a summary in Airtable. It’s a reliable setup for support teams needing automation without switching platforms.

• Calendar coordination + follow-up: Rivet enables agents to coordinate webinars and live events. One agent syncs across Google Calendar, another sends reminders, and a third drafts personalized follow-up emails based on attendance or engagement. These can also be personal assistants that manage your day-to-day calendar.
  
  
• Automated blog writing crew: Build a team of CrewAI agents to fill the roles with a researcher, writer, and editor for your SEO team. They can collaborate to create, refine, and publish SEO-friendly articles for your content strategy.

These examples demonstrate how AI agents can plan, coordinate, and execute real-world tasks. With the proper setup, your workflows become faster, smarter, and far less manual across every team.

Frequently asked questions

What’s the easiest way to create an AI agent?

Start with no-code tools like Lindy, Zapier, or Make. They offer guided workflows and templates, and learning materials that are perfect for beginners seeking AI agent advice. Define your task, connect your tools, and launch in minutes. No dev skills needed, just clear goals and structure.

Can I build AI agents without coding?

Yes, no-code platforms let you build agents using drag-and-drop logic and built-in tools. You’ll find tutorials and agent advice designed for non-coders. These platforms remove complexity, making AI automation accessible, even if you’ve never written a line of code.

How do AI agents differ from chatbots or plugins?

AI agents perceive, reason, and act independently. Chatbots reply to text or voice-based prompts, but plugins execute commands. Agents are goal-driven systems with memory and logic. 

Try Lindy: Your new AI agent — and way more

Now that you know how to create an AI agent, try Lindy. It’s a platform that lets you build agents for outreach, document summarization, and more. You’ll be able to create automations in minutes, without any programming. Here’s why Lindy is a go-to agent builder:

• No-code builder: Lindy’s intuitive drag-and-drop interface lets you build automations without any programming or technical background. You can create workflows visually, making it easy for anyone to launch powerful AI agents quickly.

• AI agents tailored to your workflow: Lindy allows you to create AI agents that understand natural language and streamline your daily tasks. For example, build an agent that sources leads from websites and databases, such as UpLead and Crunchbase, and then create another that automates outreach and meeting scheduling for your sales team.

• Budget-friendly automation plans: Lindy offers a generous free plan with up to 400 automated tasks to get you started. When you're ready to scale, the Pro plan supports up to 5,000 tasks, providing more value compared to similar platforms.

Start creating your own AI agents and try Lindy today for free.