Hey there!
In this tutorial, we're going to teach you something powerful: how to build custom AI agents that work exactly the way you need them to.
The idea is simple: instead of using generic AI that tries to do everything, you create specialized agents that excel at specific tasks, think like experts in their field, and follow your exact workflows.
Basically, we'll show you how to:
Transform a general AI into a specialized expert (researcher, analyst, designer, etc.)
Define exactly how your agent should think and work
Give your agent the right tools for the job
Build agent teams where specialists work together
Create agents that handle complex, multi-step workflows automatically
Using this approach, people have built research assistants that write McKinsey-level reports, code reviewers that catch bugs before deployment, content creators that maintain brand voice, and customer support agents that handle inquiries 24/7.
By the end of this guide you’ll understand how to create any specialized agent you can imagine.
Let's get started!
Understanding Custom AI Agents
So here's the thing: most people use AI like a search engine. You ask a question, get an answer, and that's it.
Custom AI agents are completely different.
Custom agents are specialized AI systems that you design to think like experts, follow specific workflows, and consistently perform complex tasks the way you want them done.
Think of it like this:
Generic AI:
You: "Summarize this meeting"
AI: Gives you 3 bullet points
You: "Now extract action items"
AI: Gives you a list
You: "Format it professionally"
AI: Changes the format
You're managing every step
Custom Agent:
You: Upload meeting transcript
Agent: Automatically analyzes the conversation, identifies key decisions, extracts action items with owners, highlights important quotes, generates executive summary, formats everything professionally, and asks if you want it sent to attendees
Agent manages the entire workflow
The difference? The custom agent knows its job and follows a consistent process every single time.
What Makes an Agent "Custom"?
A custom agent has four defining characteristics:
1. Specialized Expertise It thinks and acts like an expert in a specific domain (marketing, research, code review, etc.)
2. Defined Workflow It follows a consistent, multi-step process you've designed
3. Equipped with Tools It has access to specific capabilities (web search, file reading, screenshots, etc.)
4. Clear Guidelines It knows your preferences, constraints, and quality standards
When You Need a Custom Agent
Ask yourself:
Do I do this task repeatedly? → If yes, an agent can automate it
Does this require multiple steps? → If yes, an agent can follow your workflow
Do I need consistent quality? → If yes, an agent maintains standards
Does this take significant time? → If yes, an agent gives you that time back
Real Examples of Custom Agents
Research Assistant Agent
Searches multiple sources for information
Synthesizes findings into clear insights
Cites sources properly
Formats reports professionally
Used by: Students, analysts, consultants
Code Review Agent
Analyzes code for bugs and security issues
Checks against best practices
Suggests optimizations
Explains problems clearly
Used by: Developers, tech leads, QA teams
McKinsey Consultant Agent
Conducts deep research on business problems
Gathers and cleans relevant data
Creates strategic insights
Generates executive-level reports
Used by: Consultants, business analysts, strategists
Content Creator Agent
Maintains consistent brand voice
Generates social media content
Adapts tone for different platforms
Includes relevant hashtags and CTAs
Used by: Marketers, social media managers, creators
These agents don't just answer questions, they execute entire workflows independently.
The 5-Step Framework for Agent Creation
Now let's build your first custom agent together. We're creating a Meeting Notes Summarizer that turns messy meeting transcripts into professional, actionable summaries.
We'll go through each step of the framework and build this agent piece by piece. By the end, you'll have a complete, working agent.
ChatGPT said:
To create your first custom AI agent, you’ll need Pro Mode on Emergent - it includes everything: a 1M context window, Ultra Thinking, 2x bigger machine and more!
Once you’ve activated Pro Mode, we can begin building your first agent from scratch.
Step 1: Give Your Agent a Personality (The Persona)
Why this matters: Just like people excel in different areas, AI agents work best when they know exactly who they are and what they're expert at.
The persona shapes how your agent thinks, communicates, and approaches problems.
What makes a good persona:
Specific expertise - Not "you're helpful," but "you're an expert executive assistant with 10 years of experience"
Relevant experience - What background knowledge should they have?
Communication style - Professional? Casual? Technical?
Key strengths - What are they particularly good at?
Examples of strong personas:
Research Assistant:
You are an expert researcher with a PhD background, proficient at searching through vast amounts of information and synthesizing complex topics into clear, actionable insights. You excel at finding credible sources and presenting information in an organized, easy-to-understand format.
Creative Designer:
You are an experienced UI/UX Designer with 8+ years at top tech companies. You excel at creating beautiful, user-friendly interfaces that balance aesthetics with functionality. You have a keen eye for modern design trends and understand user psychology.
McKinsey Consultant:
You are an expert consultant working as a senior consultant at McKinsey. You have substantial experience in creating reports and PDFs for business cases. You excel at strategic thinking, data analysis, and creating executive-level presentations.
Step 2: Explain What You Need Done (The Task)
Why this matters: Clear task descriptions lead to consistent results. Your agent wants to help, but needs to know exactly what success looks like.
Think of this as writing a job description for a new hire. Be specific about what you want them to accomplish.
What makes a good task description:
- Clear objective - What's the end goal?
- Specific requirements - What must be included?
- Expected output - What format should the result take?
- Quality standards - How good should it be?
Examples of strong task descriptions:
For a Research Agent:
Your task is to research the given topic thoroughly, gather information from credible sources, synthesize the findings into key insights, and create a comprehensive report with proper citations. The report should be accessible to non-experts while maintaining accuracy.
For a Code Review Agent:
Your task is to analyze the provided code for bugs, security vulnerabilities, performance issues, and adherence to best practices. Provide specific, actionable feedback with code examples showing the problem and the recommended fix.
For an Email Drafter Agent:
Your task is to compose professional email responses based on the context provided. Match the tone to the situation (formal for executives, friendly for team members), keep messages concise, and ensure all questions are addressed.
Step 3: Share Current Context (Optional)
Why this matters: Sometimes your agent needs to know about recent developments, specific circumstances, or timely information to do its job well.
This step is optional - only include it if your agent's task involves current events, recent changes, or specific situational awareness.
When to include context:
- Task involves current events or news
- Recent changes affect how the task should be done
- Specific company/industry knowledge is needed
- Time-sensitive information matters
When to skip context:
- Task is timeless (like formatting, basic analysis)
- No recent developments affect the work
- Agent doesn't need external knowledge
Examples of good context:
For a Tech News Summarizer:
It's currently November 2025. Recent major developments include: AI models reaching new capabilities, increased focus on AI safety regulations, and the rise of multimodal AI applications. Keep these trends in mind when analyzing tech news.
For a Company Analyst:
Our company recently migrated from Slack to Microsoft Teams (October 2025). When analyzing communication patterns, account for this transition period and potential learning curves.
For a Market Research Agent:
The latest industry regulations (Q3 2025) require enhanced data privacy measures. Ensure all research recommendations comply with these new standards.
Step 4: Map Out Your Ideal Workflow
Why this matters: This is where you teach your agent to think like you. A clear workflow ensures consistent, high-quality results every time.
Think of the workflow as your agent's standard operating procedure - the step-by-step process it follows for every task.
What makes a good workflow:
- Logical sequence - Steps flow naturally from one to another
- Clear phases - Break complex tasks into manageable parts
- Feedback loops - Agent checks its own work
- Quality gates - Verification before moving forward
- Iterative improvement - Agent learns from outputs
Workflow structure patterns:
Pattern 1: Analysis → Execution → Verification
1. Analyze the input
2. Perform the main task
3. Verify quality before delivering
Pattern 2: Plan → Build → Test → Refine
1. Create a plan
2. Execute the plan
3. Test the results
4. Make improvements
Pattern 3: Gather → Process → Synthesize → Present
1. Collect necessary information
2. Process and clean the data
3. Synthesize into insights
4. Present in desired format
Example: McKinsey Consultant Agent Workflow
Here's how a professional consulting agent structures its work:
Core Workflow:
1. Analysis Phase:
- Understand the problem statement thoroughly
- Create a detailed plan for achieving a McKinsey-style report
- Document the plan in <Plan> tags for reference
2. Data Gathering Phase:
- Use search tool for broad research
- Use crawl tool for specific website data
- Use Perplexity deep research for complex topics
- Clean and sanitize all gathered data
3. Synthesis Phase:
- Identify data that makes meaningful contributions
- Transform raw data into actionable insights
- Structure findings strategically
4. Creation Phase:
- Generate McKinsey-level PDF reports
- Ensure professional formatting and presentation
- Include executive summaries and key recommendations
5. Verification Phase:
- Review output for accuracy
- Verify all sources and citations
- Ensure report meets quality standards
Why this workflow works:
- Clear phases - Each phase has a distinct purpose
- Specific actions - "Use search tool" not just "do research"
- Quality checkpoints - Verification phase at the end
- Structured thinking - Plan documented before execution
Step 5: Set Your Guidelines and Preferences
Why this matters: These are your "house rules" - they help your agent work exactly how you prefer and avoid common pitfalls.
Think of guidelines as the cultural values and non-negotiables for your agent. They define priorities, constraints, and quality standards beyond the basic workflow.
What makes good guidelines:
- Clear priorities - What matters most?
- Specific constraints - What to avoid or limit?
- Quality standards - How good is good enough?
- Edge case handling - What to do when things get tricky?
- Tone and style preferences - How should it communicate?
Example guidelines from different agents:
For a Content Creator Agent:
Guidelines:
- Always prioritize user-friendly language over technical jargon
- Keep social media posts under 280 characters unless specifically requested longer
- Include relevant hashtags (3-5 max) for discoverability
- Maintain a friendly, conversational tone that matches our brand voice
- Never use controversial topics or divisive language
- Always include a call-to-action when appropriate
For a Code Review Agent:
Guidelines:
- Prioritize security vulnerabilities over style issues
- Always provide specific code examples showing the fix, not just descriptions
- Use the troubleshooter tool primarily for debugging live issues
- Flag critical issues immediately at the start of your review
- Be direct about problems but constructive in tone
- When suggesting optimizations, explain the performance impact
For a Research Agent:
Guidelines:
- Always cite sources with URLs
- Prioritize recent information (last 2 years) unless historical context is needed
- Use the web search tool for current information
- Cross-reference important claims with multiple sources
- Present conflicting viewpoints when they exist
- Avoid making definitive statements on controversial topics
Equipping Your Agent with the Right Tools
Think of tools as your agent’s superpowers. Each one gives your agent a specific ability to help you better.
The Art of Tool Selection
Purpose-Driven Selection
Each tool should have a clear job to do. Before adding one, ask: “Will my agent actually need this for the task I’m giving it?”
Quality Over Quantity
Giving your agent too many tools is like giving someone a Swiss Army knife when they just need a screwdriver. More tools can mean scattered focus.
Essential Tools for Your Agent
Think of each tool as adding a new skill to your agent’s toolkit. Here are the most commonly used ones:
Web Search Tool
Like giving your agent internet access - perfect for:
Current Events
Market Research
Technical Documentation
File Reader Tool
Helps your agent access and understand documents you provide for analysis and processing.
Document Analysis
Data Processing
Content Review
Screenshot Tool
Enables your agent to capture and analyze visual information from web pages and applications.
UI Testing
Visual Analysis
Documentation
About Default Tools
The tools selected by default are mandatorily required by the agents to function. Think of default tools as the starter pack - like the engine in a car. Your agent needs these to understand requests, communicate effectively, and maintain core functionality.
System Constraints (Read-Only Section)
This section contains essential technical guidelines that help your agent operate properly within its environment. Think of it as the “operating manual” that ensures everything runs smoothly.
These constraints ensure your agent can:
Access the right tools and features
Communicate effectively with other agents
Maintain security and performance standards
Follow best practices automatically
You don’t need to modify this section - it’s the foundation that supports everything you build on top.
Learn About Sub-Agents
What are Sub-Agents?
Think of sub-agents as your agent’s network of expert colleagues. Just like how a general contractor calls in an electrician or a designer, your main agent can call on sub-agents when it needs specialized help.
Hyper-Specialization: Masters of Their Craft
Each sub-agent is a master of one specific domain - they’re the focused experts your main agent can rely on.
On-Demand Collaboration: Right Expert, Right Time
Your main agent intelligently decides when to bring in sub-agents based on the situation.
How Main Agent and Sub-Agent Interact
You give your main agent a task
It identifies which sub-agent is needed
Hands over that specific subtask automatically
Sub-agent performs its work
The result is sent back to the main agent
Creating Sub-Agents Effectively
Identify Your Needs
Name your sub-agent and ask yourself:
What tasks come up repeatedly?
Where do I need specialized expertise?
Give Definition to the Sub-Agent
This definition tells your main agent exactly when and why to call this sub-agent.Define System Prompt
Give your sub-agent a step-by-step process. Include best practices, constraints, and things to avoid.Configure Sub-Agent Tools
Assign only the tools required for your sub-agent to complete its work.
Best Practices for Sub-Agent Success
DOs:
Give each sub-agent a clear, focused role
Provide context about when they should be consulted
Allow sub-agents to communicate when necessary
Test the handoffs between agents
DON’Ts:
Make sub-agents too broad - specialization is key
Add too many at once - start small and grow
Creating Your Sub-Agent: A Step-by-Step Guide
Step 1: Define Your Sub-Agent’s Hyper-Specialized Persona
Sub-agents excel through laser-focused expertise.
If your main agent is a general contractor, your sub-agent should be the master electrician.
Example:
Code Review Specialist: “You are an expert code reviewer who identifies bugs, security issues, and optimization opportunities in code...”
Step 2: Create Two Descriptions - One for Each Audience
A. Definition (for Main Agent): tells the main agent when and why to call this sub-agent
B. Task Description (for Sub-Agent): tells the sub-agent what to do once called
Step 3: Design the System Prompt with Focused Workflow
Your sub-agent should:
Understand the specific request
Apply its expertise
Deliver targeted results
Recommend improvements
Step 4: Set Interaction Guidelines
Always format responses with clear sections: Analysis, Findings, Recommendations
Flag critical issues first
Use technical language when speaking with the main agent
System Constraints (for Sub-Agents)
These ensure sub-agents can:
Access tools properly
Communicate efficiently with the main agent
Maintain performance and security standards
Note: Sub-agents cannot call other sub-agents. They only hand over tasks to and from the main agent.
Using Your Agent
Once you’ve finished creating your agent - congrats!
Now it’s time to see it in action.
Just head back to the Emergent landing page, find your newly built agent in the list, and click on it.
From there, you can start prompting it right away - ask questions, upload files, or give it tasks based on the workflow you designed.
That’s it. No setup, no waiting.
Your custom AI agent is live, ready to think, act, and work exactly the way you built it to.
Wrapping Up
By now, you’ve learned the full process of creating a custom AI agent - from defining its personality and workflow to setting its rules and giving it tools to act independently.
Your agent doesn’t just “do tasks”; it thinks the way you would, executes your workflow, and delivers consistent, high-quality results without constant supervision.
Once you understand this framework, you can create entire ecosystems of agents - researchers, analysts, content creators, consultants - each designed to handle a part of your process and work together like a specialized team.
If you’re building inside Emergent, this process becomes even faster - you can visually design, test, and deploy your agents in one place, with built-in tools and feedback loops that help your agents get smarter over time.
Now that you know the framework, the next step is experimentation.
Start small, iterate, and keep refining until your agents feel like true extensions of your own mind and workflow.
Welcome to the era of custom AI.