How to Build AI Agents Without Code

You don't need to be a developer to build AI agents that automate real work. With the right platform, you can describe what you want in plain English and have an agent running in minutes.

This guide walks you through everything—from understanding what agents actually are, to building your first one, to scaling up with multi-agent workflows.

In this guide

• 1. What is an AI agent (vs chatbot vs workflow)?
• 2. The 4 components every agent needs
• 3. Step-by-step: Build your first agent
• 4. 5 agent templates you can use today
• 5. Advanced: Multi-agent workflows

What is an AI agent?

First, let's clear up the confusion. An AI agent isn't the same as a chatbot or a workflow automation.

An AI agent is like a smart employee who understands context, uses the right tools, and knows when to ask for help.

The 4 components every agent needs

Whether you're building with code or no-code, every effective agent has four parts:

• 1. InstructionsWhat the agent does, described in plain language. "You're a sales assistant. Every Monday, pull last week's pipeline data and send me a summary. Flag any deals that haven't moved in 2 weeks."
• 2. ToolsThe systems and data the agent can access. CRM, spreadsheets, email, Slack, databases. The agent uses these to gather information and take actions.
• 3. MemoryContext that persists between sessions. Past conversations, analysis results, learned preferences. This is what makes agents get smarter over time.
• 4. GuardrailsRules for when to ask humans for approval. "Run reports automatically, but check with me before sending any external emails." This is what makes agents trustworthy.

Step-by-step: Build your first agent

Let's build a real agent that summarizes your weekly sales pipeline. Total time: about 15 minutes.

• Create a new agentOpen your Lazarus workspace and click "New Agent." Give it a name like "Weekly Pipeline Reporter."
• Write the instructionsDescribe what you want in plain language: "Every Monday at 9am, pull the current pipeline from our CRM. Calculate total value by stage and compare to last week. Identify any deals that haven't moved in 14+ days. Send me a summary via email with the key numbers and any deals that need attention."
• Connect your toolsClick "Add Tool" and connect your CRM (HubSpot, Salesforce, etc.) and email. The agent will use these to pull data and send reports.
• Set the scheduleUnder "Schedule," select "Weekly" and choose Monday at 9:00 AM. The agent will run automatically at this time.
• Add guardrails (optional)If you want the agent to check with you before sending, enable "Require approval for external communications." You'll get a message to approve or edit before it sends.
• Test itClick "Run Now" to test. The agent will pull your pipeline data, analyze it, and show you the report it would send. Make adjustments as needed.

That's it. You've built an AI agent that will save you 30+ minutes every week.

5 agent templates you can use today

Don't want to start from scratch? Here are proven templates for common use cases:

• Lead QualifierScores incoming leads based on your criteria, enriches with company data, and routes to the right rep.
• Weekly ReporterPulls data from multiple sources, compiles key metrics, and sends formatted reports on your schedule.
• Meeting PrepBefore each meeting, researches attendees, pulls relevant account history, and sends you a brief.
• Follow-up AssistantMonitors deals and contacts, drafts personalized follow-up emails, sends after your approval.
• Research AssistantAnswers questions about your business data, finds patterns, and saves findings to your workspace.

Advanced: Multi-agent workflows

Once you're comfortable with single agents, you can build teams of agents that work together.

Example: Content Pipeline

• Research Agent monitors industry news and saves relevant articles to the workspace
• Content Agent reads the research, drafts blog posts, saves drafts for review
• Distribution Agent takes approved posts and schedules them across channels

Each agent works in the same workspace, building on the others' work. You just approve at key checkpoints.

The key is that agents share the same workspace—files, databases, and context flow between them naturally.