Fundamentals of successful AI agents: SOPs, focus & clear roles

⚙️ AI agents are not employees - they need clear SOPs

🔹 Why is this important

AI agents are not creative problem solvers, but automated systems that follow precisely defined processes. Without clear standard operating procedures (SOPs), they deliver unreliable or unexpected results. To develop efficient and precise AI agents, their tasks and boundaries must be clearly defined.

🚀 Concrete implementation for your AI agent business

✅ Create detailed SOPs:

• Write precise instructions as if you were training a new employee.
• Define all relevant inputs, steps and expected outputs.

✅ Set up specialised agents:

• Develop several small, focused agents instead of one "jack of all trades".
• Separate research, creation and publishing into separate agents.

✅ Set clear guidelines:

• Determine clear structures, data sources and permitted actions.
• Prevent unwanted input or incorrect conclusions.

⛔️ Wrong vs. ✅ Right

⛔️ Wrong: "The agent should take over all marketing tasks."
✅ Right: "Three specialised agents with clearly defined SOPs work together."

📝 Practical example: Blog article creation with AI agents

Scenario: A company wants to use AI agents to create and publish blog articles.

🔹 Solution with SOP-based agents:

1️⃣ Research agent

• Searches for relevant content ideas using Google Trends and other tools.
• Delivers a list of topics with suitable keywords.

2️⃣ Agent for article creation

• Creates blog articles based on a fixed structure (introduction, main section, conclusion).
• Uses predefined SEO guidelines for better visibility.

3️⃣ Agent for publishing

• Adds the article to WordPress.
• Checks SEO with Yoast and sets categories & tags.
• Saves as a draft and notifies the editor via Slack.

📋 Example of an SOP (Publish blog article)

1. Open WordPress.  
2. click on "New post".  
3. insert headline, text and images.  
4. check SEO with Yoast.  
5. choose categories and tags.  
6. save as a draft and notify the editor via Slack.

💡 Conclusion

🔸 KI agents only work with clearly defined SOPs. The more detailed the instructions, the more reliable the result.
🔸 Specialisation improves efficiency. A modular approach prevents errors and increases quality.
🔸 Structured workflows enable smooth automation.

🤝Business owners don't create their agents themselves - they need experts

🔹 Why is this important?

Companies don't have the time or expertise to develop high-quality AI agents themselves - even if no-code tools exist. Customers don't pay for a tool, they pay for a solution that solves a specific problem for them. Your role is to understand these problems and develop customised AI agents.

🚀 Concrete implementation for your AI agent business

✅ Provide more than just technology:

• Your customers don't need software, they need an efficient solution to their problem.
• Supervise the entire process: analysis, development, maintenance and optimisation.

✅ Position yourself as a partner, not just a service provider:

• Don't market yourself as a "technician", but as an expert in process optimisation through AI.
• Highlight that you understand and optimise business processes, not just write code.

✅ Guide customers through the process:

• Use consultations to identify the actual bottleneck.
• Reveal which agents actually make sense and where they add the most value.

⛔️ Wrong vs. ✅ Right

📝Practical example: The right consulting approach

Scenario:
A company wants to automate its support with AI and asks you for a chatbot.

🔹 Wrong approach:
💬 Customer: "I need a chatbot for my support."
🛑 Answer: "No problem, I can set up a chatbot for you."

🔹 Correct approach:
💬 Customer: "I need a chatbot for my support."
✅ Answer: "Let's first check whether an AI agent can really take over the entire support process or whether it's more efficient to automate individual steps."

Next steps:

• You analyse the company's support process.
• You find out that the biggest waste of time is not caused by customer questions, but by manually looking up product data.
• Instead of just building a chatbot, you develop an internal knowledge database agent that provides employees with the information they need in real time.
• Result: 30% faster processing times and higher customer satisfaction.

💡 Conclusion

🔸 Customers don't just need AI, they need customised solutions to their problems.
🔸 Your role is not just technical implementation, but holistic consulting and optimisation.
🔸 Position yourself as an expert in process improvement through AI, not just as a developer.

🔍Customers often don't know which agents they really need

🔹 Why is this important?

Many customers have a rough idea of which AI agents they need ("I need a chatbot"), but rarely a clear understanding of where the actual bottlenecks lie. Often, a deeper process analysis can lead to better solutions than the idea proposed by the customer.

🚀 Concrete implementation for your AI agent business

✅ Start every customer project with a process analysis:

• Use tools such as Miro or Figma to create a visual representation of the customer journey or internal processes.
• Identify recurring bottlenecks, delays and inefficient workflows.

✅ Recognise where automation creates the greatest added value:

• Search specifically for time wasters instead of just solving obvious problems.
• Find repetitive tasks that can be automated by AI.
• Consider whether a support agent is really necessary or whether another tool would be more effective.

✅ Don't just recommend to customers what they want - but what they really need:

• Your goal is to advise the customer, not just implement their first request.
• A well-placed automation can often save more time and money than the originally desired agent.

⛔️ Wrong vs. ✅ Right

📝Practical example: Optimisation through customer journey analysis

Scenario:
A company wants to develop an AI agent that answers customer questions automatically.

🔹 Wrong approach:
💬 Customer: "I want a chatbot that answers support queries."
🛑 Answer: "No problem, I'll build you a support chatbot."

➡ Result: The chatbot answers questions, but support times remain high as employees still have to look up product data manually.

🔹 Correct approach:
💬 Customer: "I want a chatbot that answers support queries."
✅ Answer: "Let's first analyse which tasks are currently taking up the most time. Maybe there's a more efficient solution."

➡ You carry out a customer journey analysis and realise that it's not the customer questions that are the problem, but the manual search for product information.
➡ Instead of just building a support chatbot, you propose an internal knowledge base agent that automatically provides product specifications and answers.
➡ Result: 30% faster response times and less support effort for employees.

💡 Conclusion

🔸 Customer ideas are often just starting points - your job is to find the best solutions.
🔸 A targeted process analysis uncovers better automation potential.
🔸 The most obvious AI agent is not always the best choice - find bottlenecks and optimise the most important processes first.

⚖️ Less is more: a maximum of 20 agents per system

🔹 Why is this important

Too many AI agents lead to unnecessary complexity and make maintenance, scaling and troubleshooting more difficult. A lean system with specialised agents is more efficient, more reliable and easier to manage.

🚀 Concrete implementation for your AI agent business

✅ Start with a few, specialised agents:

• Start with 2-3 agents per customer project instead of directly with a large, complex system.
• Expand the system gradually as additional requirements become apparent.

✅ Structured agent groups according to process steps:

• Divide agents according to clearly defined tasks.
• Avoid a single agent having to take on too many different processes.

✅ Set conscious limits for scaling:

• Keep the number per system below 20 agents to keep administration manageable.
• If more are needed, create separate systems or modular solutions instead.

⛔️ Wrong vs. ✅ Right

📝 Practical example: Lead management with AI agents

Scenario:
A company wants to automate the entire lead generation and customer acquisition process with AI agents.

🔹 Wrong approach:
🛑 One single agent takes care of everything:

• Collects leads
• Evaluates and qualifies leads
• Creates personalised cover letters
• Sends follow-ups
• Organises appointments

➡ Result: An overloaded, error-prone agent that doesn't work efficiently.

🔹 The right approach:
✅ Three specialised agents with clear separation:
1️⃣ Research agent - Finds potential leads.
2️⃣ Qualification Agent - Evaluates and filters leads according to relevant criteria.
3️⃣ Follow-up Agent - Creates and sends personalised messages.

➡ Result: Each agent works in a focused manner, the system remains stable and easily expandable.

💡 Conclusion

🔸 Focus on clear specialisation instead of overloaded "all-rounder agents".
🔸 A maximum of 20 agents per system - less complexity, easier maintenance.
🔸 Group agents sensibly according to processes for maximum efficiency.