How To Build An AI agent (2026)
Davis Christenhuis
AI agents are transforming how businesses work in 2026. Unlike chatbots that simply answer questions, AI agents can independently complete tasks, make decisions, and take actions across your tools and data sources. The best part? You don't need to be a developer to build one.
This guide shows you exactly how to build an AI agent from the ground up. Whether you're creating your first agent or looking to optimize your workflow, you'll learn the key steps and best practices to get started.
📌 TL;DR
Short on time? Here's the quick version of how to build an AI agent:
- Create your agent – Choose your model and set up your workspace
- Write clear instructions – Define role, context, and tasks
- Choose the right model – Match AI capabilities to your use case
- Add tools – Give your agent abilities beyond conversation
- Connect knowledge – Provide access to relevant data sources
- Test and refine – Iterate based on real performance
- Deploy to your team – Share and set access controls
What is an AI agent?
An AI agent is a system that can plan and execute multi-step work within approved tools and data access. Think of it like a very competent intern — you give them a goal, the necessary tools, and access to information. They figure out the steps needed and execute independently.
Unlike traditional chatbots that only respond to direct prompts, AI agents can plan multi-step workflows, adapt when their initial approach doesn't work, and use various tools to accomplish objectives—all without you needing to guide every action.
Three ways to build an AI agent
There are three main approaches to building AI agents, each suited to different skill levels and use cases:
- No-code platforms: The fastest way to get started. Platforms like Dust and others provide visual interfaces where you write instructions in plain English, connect data sources through drag-and-drop, and add pre-built integrations.
- Framework-based development: For developers who want more control. Frameworks like LangChain, AutoGPT, or CrewAI provide pre-built components and patterns for agent development. You write code to define agent behavior, but the framework handles complex orchestration, memory management, and tool integration.
- Custom development: Building from scratch using LLM APIs directly (OpenAI, Anthropic, etc.). This gives complete control over architecture, tool integration, and agent behavior, but requires significant development resources.
💡 See how Dust works for your team. Talk to our team about your use case →
Which approach is right for you?
The best approach depends on your team's priorities. No-code works well for speed and simplicity. Frameworks suit teams with developers who want more control. Custom development fits commercial AI products or highly specialized requirements.
Why teams choose no-code vs. custom development
- Speed to value: No-code platforms let you build and deploy agents in hours, not weeks. Test ideas quickly, iterate based on feedback, and solve problems today instead of waiting.
- No engineering bottleneck: Business teams can build their own agents without waiting on developer availability. Your support team builds support agents. Your sales team creates qualification agents. No sprint planning required.
- Lower total cost of ownership: Custom builds mean ongoing maintenance. Every API update, model change, and integration becomes your responsibility. No-code platforms handle infrastructure, security updates, and tool maintenance so you can focus on results.
- Enterprise-ready by default: Custom development requires building and maintaining your own security, auditability, and permission systems. No-code platforms include role-based access controls, permission-aware data access, and full auditability out of the box.
Why build AI agents with Dust
Building AI agents with Dust gives your team the power to automate workflows without technical barriers. Here's what sets Dust apart:
- Connects to your existing tools: Agents integrate natively with Google Drive, Slack, Notion, Gmail, Zendesk, HubSpot, and more. Your agents work with actual company context, not generic responses.
- Smart data scoping: More data doesn't equal better results. With Dust, you can separate tools per source, set up restricted-permission spaces, and control exactly how data is processed — whether that's semantic search, SQL queries on structured data, or extracting from large datasets. Agents access only what they need.
- No-code platform: Write instructions in plain English, connect data through a visual interface, and test using a chat window. Your support team can build support agents. Your sales team can create qualification agents. No developers required.
- Purpose-built agent categories: Different work requires different designs. Build specialized support agents, research agents, content generators, or structured data agents tailored to specific use cases.
- Problem-first approach: Build agents that solve real user needs, not just showcase technology. Start with the workflow challenge, then design the agent around it.
- Enterprise-ready security: SOC 2 certified with role-based access, full auditability, and scalable from one user to thousands.
With Dust, you get more than no-code — you get permission-aware knowledge access, auditability, and safe tool orchestration. No code, but enterprise grade.
How to build an AI agent with Dust
Step 1: Create your agent
Before you start:
- Connect your knowledge sources (Google Drive, Notion, etc.)
- Upload any specific documents or data your agent will need
- Create a private test space for experimentation
To create your agent:
- Click "Create" in Dust
- Choose "agent from scratch" or use a template
- Select your foundation model (Dust offers multiple leading models — choose based on quality, speed, cost, and use case)
Choose your use case:
Most teams start with a knowledge agent—one that answers questions from internal documentation. Examples include support copilots, onboarding assistants, or research agents.
Step 2: Write clear instructions
Your instructions are the most critical component. Use this structure:
Role: Who is the agent?
- You are a support agent for [Company]. - You help customers resolve technical issues quickly.
Context: What does it need to know?
- [Company] is a SaaS platform for [purpose]. - Our customers are typically [description].
Instructions: What should it do?
When a user asks a question: 1. Search support documentation 2. Search past support tickets if needed 3. Provide a clear answer with specific steps 4. Always cite your sources 5. Ask if they need follow-up help
Guidelines: What should it NOT do?
- Never make things up — say "I don't have enough information" if uncertain - Always cite retrieved documents as sources — don't present the model's own reasoning as a source - Keep answers concise but complete - Maintain a professional, helpful tone
Pro tip: Use @PromptWriter (a built-in Dust agent) to help structure your instructions.
Step 3: Choose the right model
For most tasks: Claude 4.5 Sonnet (default)
For complex reasoning: Set reasoning effort to Medium or High — ideal for analysis, strategy, and multi-step problem solving
Reasoning effort settings:
- Light — Quick analysis
- Medium — Balanced reasoning
- High — Deep reasoning (slower)
Step 4: Add tools & capabilities
Tools let your agent do things, not just answer questions.
Built-in tools:
- File generation (create documents)
- Image creation
- Sub-agents — use specialized sub-agents for repeatable workflows, where each sub-agent specializes in a specific task (e.g., research sub-agent, drafting sub-agent, QA sub-agent)
Integration tools:
- Gmail (read, draft, send emails)
- Google Calendar (check availability, schedule)
- Slack (post messages, search conversations)
- CRM tools (update records, pull data)
- Zendesk (access tickets, create responses)
When to add tools:
Start simple. Add only what's needed for your agent's goal.
Example: A support agent might need:
- Search tool for documentation
- Search tool for past tickets
- File generation (for response templates)
Step 5: Connect knowledge sources
The selective data principle:
More data ≠ better results. Give agents access to only what they need. Too much information causes slower performance, less relevant results, and higher costs.
Four types of access:
Search data (most common):
- Agent searches when it needs specific information
- Use for: large documentation, knowledge bases, past conversations
Include data (for templates):
- Entire document included with every query
- Use for: templates, brand guidelines, specific examples
Extract data:
- Pull specific information from documents for precise, structured outputs
- Use for: extracting key details from contracts, reports, or forms
Query tables:
- Translate natural language into SQL queries for structured data
- Use for: databases, spreadsheets, CRM exports, or any tabular data
Pro tip: Create separate, clearly-named search tools (e.g., "Search Product Docs" vs. "Search Support Tickets"). This helps agents choose the right source.
Step 6: Test and refine
Use the preview function:
Test directly in the builder with typical questions and edge cases.
Click "thinking" to debug:
See exactly what your agent is doing—which sources it searched, what it found, which tools it used, and why.
Common fixes:
Problem | Solution |
Not finding right info | Narrow data access |
Making things up | Set to "factual"; add "okay not to know" |
Inconsistent format | Add 2-3 output examples |
Too slow | Switch to non-reasoning model |
Using wrong tools | Clearer tool names; reference in instructions |
Iterate systematically:
Change one thing at a time. Test with the same queries before and after each change.
Step 7: Deploy to your team
Set access controls:
- Published — Visible & usable by all members of the workspace
- Unpublished — Visible & usable by editors only
Name and describe clearly:
Use names that indicate function (@SupportCopilot, not @Agent1). Include example questions in the description.
Pro tip: Add your agent to Slack or Teams channels where it's most useful.
Train your team:
- Share example questions
- Explain capabilities and limitations
- Show how to check "thinking" to understand answers
- Encourage feedback
💡 Ready to build your first AI agent? Try Dust Free for 14 days →
See what others have built
- Wakam — 70% employee adoption and 136 deployed AI agents within 2 months. Read the story →
- Malt — Cut support ticket closing time by 50%. Read the story →
- Alan — Produces customer stories 80% faster. Read the story →
💡 Explore more on the Dust Customer Stories page →
Frequently asked questions (FAQs)
How long does it take to build an AI agent?
It depends on your approach. With no-code platforms, you can build a basic agent in 30 minutes to a few hours. Framework-based development typically takes days to weeks, while custom development can take months depending on complexity.
How to build an AI voice agent?
Building a voice agent requires adding speech-to-text and text-to-speech capabilities to your AI agent. Most no-code platforms offer voice integrations, or you can connect APIs like OpenAI's Whisper (for transcription) and ElevenLabs or Google Text-to-Speech (for voice output) to your agent workflow.
Do I need coding skills to build an AI agent?
Not necessarily. No-code platforms let you build agents using plain English instructions and visual interfaces. However, coding skills become valuable if you need custom integrations, want to use frameworks like LangChain, or need to build from scratch.
How much does it cost to build an AI agent?
Costs vary widely depending on your approach. No-code platforms typically charge a monthly per-user fee — ranging from free tiers to enterprise plans costing hundreds per month. If you're building with frameworks or custom code, your main costs are LLM API usage plus developer time. Enterprise solutions can run significantly higher depending on scale and usage.