I know, we all dream of building a business that practically runs itself? I certainly did, and that’s exactly what we’ve achieved through AI agents.
Let me share our complete blueprint that took us from zero to becoming pioneers in the AI agency space.
Key takeaways:
- You can get started building AI agents without mastering the technology first
- Finding the right processes to automate is more important than technical perfection
- A systematic approach to building multiple agents creates exponentially more value
1. Finding Your First AI Agent Client
One of the biggest mistakes I see people make is waiting too long to find their first client.
Many believe they need to master AI agent development completely before offering services.
The truth? You can get your first client before building any agents at all.
Learning while working with real clients offers three massive advantages:
- You get paid while learning
- You learn faster through real feedback
- You solve actual business problems, not theoretical ones
Where should you look for clients? Start with warm outreach. Think about the business owners you already know:
- Friends who run small businesses
- Former classmates who started companies
- Even your current employer
Simply reach out and explain how AI agents can improve efficiency and automate repetitive tasks.
Another effective approach is freelance platforms like Fiverr or Upwork.
The competition for AI agent services is practically non-existent right now.
Most people still offer basic services that could be completely replaced by AI agents.
For example, searching for “marketing” shows people offering marketing strategies for over $600.
Why would anyone pay that much for a single strategy when they could invest in an agent that generates unlimited strategies?
The best part?
These methods require zero upfront investment.
Later, you can expand into cold outreach or content marketing, but find your first client first.
2. Identifying the Perfect Problems to Automate
After securing your first client, you need to find the right problems to solve. We look specifically for:
- Recurring problems that clients have struggled to automate
- Dynamic processes performed repeatedly by one or more employees
- Adaptive workflows that aren’t executed the same way twice
We avoid simple, linear processes with predetermined steps.
Those can be easily automated with tools like Zapier or Make.
Great examples include:
- Project management across time zones
- Content creation requiring regular feedback
- Customer support with unique scenarios
The easiest way to discover these processes is by reviewing Standard Operating Procedures (SOPs).
Most businesses have already documented their processes somewhere.
If they haven’t, get curious with questions like:
- What roles currently exist in your company?
- Which processes cause the most frustration?
- Which departments struggle to scale?
- What tasks do employees repeat daily or weekly?
3. Choosing the Right Agent to Build First
Before jumping into development, carefully consider which agent to deploy first.
Your first agent is critical – if it takes too long to build or doesn’t deliver enough value, the client likely won’t continue.
We use this ROI formula to evaluate processes:
ROI = (Hours × Hourly Rate - Operational Costs) ÷ Development Effort
This formula helps identify processes that are:
- Performed by many employees simultaneously
- Executed by expensive positions
- Relatively simple to build
Resist the temptation to build the first agent solution that comes to mind.
Instead, focus on what will deliver the most value with reasonable effort.
A good complexity indicator is the number of APIs your agent needs to connect to.
The more systems involved, the harder the agent will be to build.
4. Building Your Minimum Viable Agent
When building your MVP, you have two options:
- Use a framework – More flexibility and control, but requires technical expertise
- Use a platform – Easier to use, but may involve extra costs
My recommendation? Go with a platform whenever possible. Only use a framework when necessary.
When we started, we used frameworks exclusively.
We’d spend two days building an agent, then three days deploying it – tedious backend engineering that no one enjoyed.
Regardless of your approach, all agents consist of three primary components:
- Instructions
- Knowledge
- Actions (tools)
Most of your time will be spent on tools – the most important component.
Unlike standard language models, agents shouldn’t just provide responses; they should execute tasks.
Start by connecting your agent to the same systems employees use throughout the process.
If your client uses Trello for project management, ensure your agent can interact with the Trello API.
If they use Google Drive or GitHub, connect to those as well.
For reliable tools, we use Pydantic to validate all agent inputs and outputs.
This reduces hallucinations and prevents errors that could cause serious issues.
What if the client doesn’t have all necessary data stored somewhere for the agent to access?
Simple solution: prompt the agent to ask for this information directly.
5. Integrating Your Agent Effectively
Integration is just as important as capabilities. How easily clients can use your agent often determines its success.
Six primary integration options include:
- Web Interface – Standalone chat app, great for internal and external agents
- Widgets – Embeddable chats, ideal for customer-facing scenarios
- Messengers – Platforms clients already use (Slack, WhatsApp)
- Third-Party Software – Integration into tools like Salesforce, GitHub, or Notion
- Cron Jobs – Scheduled execution (hourly, daily, weekly)
- API – Full flexibility to connect anywhere
The key principle: your agent must work in the same systems employees use daily.
If employees use Slack to communicate, integrate your agent there too.
6. Iterating on Your Agent
All agent development is iterative. You will never get it right on the first attempt.
AI agent projects are incredibly agile – more so than any other type of project I’ve worked on.
Once you start building an agent, you’ll immediately discover opportunities you didn’t realize were there.
This is why we shifted from a project-based billing model to a subscription approach. When building AI agents, you’re not just a software developer; you’re a business growth partner.
While it’s fine to start with one-off projects, switch to a more flexible model once you gain momentum.
Repeating the Process
Today, virtually any business role can be replaced with an AI agent. It’s just a matter of time and effort.
To succeed, use a divide-and-conquer approach:
- Start by automating SOPs
- Progress to automating roles
- Then automate entire departments
The key is consistency – focus on the same roles and departments first. Don’t spread yourself too thin.
The more agents you build for a single role or department, the more powerful they become. Unlike traditional automations, agents can collaborate.
After deploying your first agent, immediately look for additional processes to automate within the same area. Keep repeating until the business runs virtually by itself.
7. Productizing and Scaling
After building several agents, you’ll notice patterns and similarities.
This is your opportunity to create scalable, specialized vertical solutions.
For example, after building many Facebook marketing agents for different companies, we started developing a vertical Facebook marketing agent that can be quickly customized for any business.
This approach allows for outcome-based pricing similar to traditional agencies – charging per lead, client, or appointment booked.
Don’t create vertical agents without first building several horizontal custom agents. Otherwise, you risk developing solutions no one needs.
Final Thoughts
The AI agent revolution is happening now.
Even with current technology, we can automate practically any business – it just takes longer than it will in the future.
The process for automating businesses, however, will remain fundamentally the same.
The people who start today will lead the AI agent space tomorrow.
Your goal should be to deliver agents to help businesses scale in ways they never thought possible.
That’s the true power of AI agents – not just automation, but transformation.
