Use AI agents strategically: Control, specialisation & future-proofing

🔒 Build in human controls for critical tasks

🔹 Why is this important

AI agents are not infallible. For critical tasks such as financial transactions, marketing budgets or sensitive customer communications, a small mistake can quickly become costly. Human control ensures that risky decisions are not automatically executed before they have been checked.

A poorly configured agent can inadvertently cause damage - be it through incorrect invoices, incorrect advertising budgets or inappropriate customer communication. A human control instance can detect errors in good time and prevent major problems.

🚀 Concrete implementation for your AI agent business

✅ Include explicit approval steps:

• Use approval workflows in Notion, Slack, Jira or email.
• Critical decisions require confirmation by a human before they are implemented.

✅ Use a gradual reduction of control:

• Initially: Each agent suggestion is reviewed.
• Later: After several successful actions, the agent can work partially or fully autonomously.

✅ Set limits for critical actions:

• Limit budget releases or transactions to a defined maximum value that cannot be exceeded without manual control.
• Implement notifications in the event of unusual behaviour (e.g. if the agent suddenly increases an advertising budget drastically).

⛔️ Wrong vs. ✅ Correct

📝 Practical example: Automated Facebook advertising with human approval

Scenario:
A company wants to use an AI agent to manage Facebook advertising campaigns.

🔹 Wrong approach - full automation without control:
🛑 The agent creates adverts and sets budgets independently without any human intervention.
➡ Problem: If the agent mistakenly starts a campaign with a budget of €10.000 instead of €100 can result in high costs.

🔹 Correct approach - human control instance:

✅ Step 1 - Campaign proposal by AI:

• The agent creates a campaign with text, image and budget proposal.
• This campaign is not published immediately, but is stored in Notion or Airtable for review.

✅ Step 2 - Manual review:

• A marketing manager reviews the campaign and approves it or requests changes.
• If everything fits, the campaign can be published at the touch of a button.

✅ Step 3 - Automation after successful approvals:

• After several successful approvals without corrections, the agent can publish certain campaigns independently - but still only with a predefined budget limit.

➡ Result: The system combines the efficiency of automation with the security of human control.

💡 Conclusion

🔸 Human oversight is essential for critical AI agent decisions.
🔸 Use approval workflows to monitor risky actions.
🔸 Incremental automation makes sense - but only if proven safety systems are in place.

🔍 Don't build agents around technical limitations - future-proofing instead of short-term solutions

🔹 Why is this important?

AI technologies are developing rapidly. What is a technical limitation today (e.g. token limits or APIs) is a technical limitation.
If you build your agents only around existing limits, you risk that your entire system will soon become unnecessarily complicated or even useless.

Instead of relying on temporary workarounds, you should make agents modular and flexible so that they can easily adapt to new technological developments.

🚀 Concrete implementation for your AI agent business

✅ Build modular systems that can be easily customised:

• Use APIs that are interchangeable (e.g. API wrappers instead of direct API wrappers).
• Make sure that you can easily integrate a new AI model without rewriting the entire logic.

✅ Avoid hard dependencies on current limits:

• Example: If a model only supports 4,000 tokens context today, build your agent so that you can easily use larger models later without restructuring everything.

✅ Think long-term instead of using short-term "hacks":

• Avoid "creative" solutions that only revolve around existing technical limitations but are difficult to maintain later on.
• Develop future-proof mechanisms that can be dynamically adapted.

⛔️ Wrong vs. ✅ Right

📝 Practical example: AI agent for large document processing

Scenario:
A company wants to develop an AI agent that processes and analyses long documents.

🔹 Wrong approach - hard limitation to current token limits:
🛑 The agent is built around the 4,000 token limit of GPT-4.

• It artificially cuts up texts into small sections.
• Stores these fragments in a separate, difficult-to-manage database.
  ➡ Problem: As soon as larger models with 100,000 tokens are available, the entire architecture is superfluous.

🔹 The right approach - future-proof solution:

✅ Step 1 - Dynamic processing:

• The agent uses a flexible strategy for text processing and stores the context dynamically.
• If a model supports larger tokens, it can adapt the strategy directly.

✅ Step 2 - Interchangeable AI models:

• The agent is not fixed to GPT-4, but can access other models as required (e.g. GPT-5, Claude or Mistral).

✅ Step 3 - API-based design:

• The interface to the AI is kept modular so that new models can be easily inserted without having to change the entire code.

➡ Result: The agent remains future-proof and can benefit from new technologies without major modifications.

💡 Conclusion

🔸 Avoid developing AI agents around existing technical limits.
🔸 Rely on modular architectures that can be easily adapted to new technologies.
🔸 Build systems that can grow flexibly instead of implementing short-term workarounds.

🚀 Agile subscription models instead of rigid waterfall projects for AI agents

🔹 Why is this important?

AI agents are dynamic and constantly evolving. A rigid waterfall model, in which all requirements are defined from the outset, is unsuitable for AI projects as it offers no flexibility for new insights and optimisations.

An agile subscription model is better:

• The customer receives continuous updates and improvements.
• Adaptations to new technological developments or changed business processes are possible at any time.
• Instead of a large one-off payment, customers benefit from a plannable, monthly investment.

🚀 Concrete implementation for your AI agent business

✅ Use a one-off setup fee for the basic version:

• Start with a setup fee (e.g. €3,000) to provide an initial, functioning version of the agent.
• This basic version contains the most important functions and can be used immediately.

✅ Provide a monthly subscription for continuous development:

• Instead of a rigid project plan, the agent is improved step by step.
• Monthly payments (e.g. €300-800/month) finance regular updates, new features and optimisations.

✅ Create long-term customer loyalty instead of one-off sales:

• Companies benefit from a continuously optimised agent instead of buying a one-off version that quickly becomes obsolete.
• You ensure stable revenue and a closer customer relationship.

⛔️ Wrong vs. ✅ Right

📝 Practical example: AI agent for customer service

Scenario:
A company needs an AI agent for automated customer service.

🔹 Wrong approach - one-off payment without further development:
🛑 The customer pays €10,000 for a finished agent that no longer receives updates.
➡ Problem: As soon as new customer requests are added or AI models are improved, the agent remains at the old level.

🔹 Correct approach - Agile subscription model:

✅ Step 1 - Setup fee for basic version (€3,000):

• The customer receives a first functional version of the agent.
• The agent can answer simple customer queries and solve common problems.

✅ Step 2 - Monthly subscription for ongoing optimisation (€500/month):

• Monthly improvements: New features, better answers, integration into other systems.
• Technological updates: Adaptations to new AI models and better training data.
• Feedback-based further development: The agent is adapted to the real requirements of the users.

➡ Result: The customer receives a permanently up-to-date and powerful agent instead of a solution that is outdated after one year.

💡 Conclusion

🔸 Strict waterfall models are unsuitable for AI agents - use agile subscription models instead.
🔸 A one-off setup fee + monthly payments ensure continuous improvements.
🔸 Long-term customer loyalty and predictable revenue make your AI agent business more stable.

🚀 2025 will be the year of vertical AI agents - specialisation beats generality

🔹 What exactly does "vertical AI agents" mean?

Vertical AI agents are highly specialised solutions for a specific industry or target group.
Instead of building universal ("horizontal") AI tools that are suitable for many different purposes, vertical AI agents focus on concrete, industry-specific problems.

Why is this important?

• 🔹 Maximum effectiveness: The agent is tailored precisely to the requirements of a specific industry.
• 🔹 Easier scaling: A well-developed vertical agent can be sold to many similar companies.
• 🔹 Higher willingness to pay: Customers will invest more in a solution that is 100% focused on their problem rather than a generic AI.

🚀 Concrete implementation for your AI agent business

✅ Focus on a clearly defined target group:

• Choose an industry or niche that you know particularly well or in which there are no optimal AI solutions yet.
• Examples: E-commerce, automotive (Porsche dealerships), fitness studios, management consultants.

✅ Understand the business processes of your target group precisely:

• Identify the biggest bottlenecks and time wasters in the industry.
• Develop agents that solve real problems, e.g. lead generation, support automation or data-based decisions.

✅ Build horizontal agents first - then optimise them vertically:

• Step 1: Create horizontal agents that can be used for multiple industries.
• Step 2: Analyse which industries particularly benefit and develop specific functions for them.
• Step 3: Scale the optimised vertical agent and market it specifically for this industry.

⛔️ Wrong vs. ✅ Right

📝Practical examples of vertical AI agents

✅ Automotive vertical agent - for Porsche dealers

• Automatically updates vehicle data and listings.
• Qualifies potential buyers and generates leads.
• Optimises sales processes and pricing strategies.

✅ Fitness vertical agent - for studios & personal trainers

• Creates personalised training and nutrition plans.
• Communicates automatically with members (e.g. motivation, feedback).
• Adapts plans dynamically to customer progress.

✅ E-commerce vertical agent - for small & medium-sized shops

• Optimises product descriptions for SEO automatically.
• Analyses product reviews and marketplace trends (e.g. Amazon).
• Develops data-based content strategies for better visibility.

🚀 Step-by-step guide: How to build your vertical AI agent

1️⃣ Define a clear niche - Choose an industry with high automation potential.
2️⃣ Analyse the problems of your target group - Where are there bottlenecks or repetitive tasks?
3️⃣ Create a horizontal solution - Develop a universal agent for similar problems.
4️⃣ Customise the agent vertically - Optimise it specifically for one industry.
5️⃣ Scale the agent as a product - Market it specifically to similar companies.

➡ Result: You have a high-quality, industry-specific AI agent that you can sell as a product to many similar customers.

💡 Conclusion

🔸 2025 will be the year of vertical AI agents - specialisation beats generality.
🔸 The more clearly your target group is defined, the more valuable your agent will be.
🔸 Build horizontal agents first, then optimise them for a specific industry.

🚀 Agents don't replace people - they help with scaling

🔹 Why is this important?

Many companies fear that AI will cause job losses. However, the reality is different:

💡 AI agents do not replace people - they relieve them!

• They take over repetitive tasks so that employees can concentrate on more valuable activities.
• They increase productivity by making processes more efficient.
• They enable growth as companies have more capacity for innovation and strategic work.

AI agents should be positioned as "productivity helpers" - not as a threat to jobs.

🚀 Concrete implementation for your AI agent business

✅ Actively communicate the supporting role of AI agents:

• Instead of talking about cost savings through staff reductions, emphasise increasing efficiency and improving quality.
• Make it clear that AI agents do not replace people, but help them to do a better job.

✅ Show how AI improves work:

• AI agents relieve undemanding routine tasks so that employees can focus on value-adding activities.
• Teams can concentrate more on creativity, strategy and individual customer care.

✅ Use positive terms instead of threatening formulations:
❌ Bad: "AI replaces your support staff and saves you personnel costs."
✅ Better: "AI takes over repetitive support requests so that your team has more time for individual customer care."

⛔️ Wrong vs. ✅ True

📝Practical examples of successful communication

✅ Marketing agent (positively worded)

• Wrong: "Our AI agent replaces your social media team."
• Right: "Our AI agent takes over repetitive social media posts so your marketing team can focus on creative campaigns."

✅ Support agent (positively worded)

• Wrong: "Our AI agent reduces the need for support staff."
• Right: "Our AI agent answers common questions automatically and frees up your team to focus on complex requests."

➡ Result: AI is perceived as a tool to increase productivity, not a threat.

💡 Conclusion

🔸 AI agents do not replace employees - they increase efficiency and relieve teams.
🔸 Focus your communication on support, not savings.
🔸 Use positive wording to allay fears and make the added value of AI agents clear.

🔥 Conclusion: AI agents are not a sure-fire success - but a real opportunity

If you are a service provider developing AI agents or workflows for customers, it is not enough to simply click together a few automations. AI agents are powerful tools - but only if they are set up correctly.

🔹 Without clear SOPs Your agents deliver unreliable results.
🔹 Too many agents without structure Your system becomes chaotic and inefficient.
🔹 No focus on ROI? Your customers see no added value and bounce.

But if you do it right, you can deliver real efficiency gains for companies - and build a profitable AI business for yourself. With a clear concept, specialised agents and a well thought-out strategy, you can avoid rookie mistakes and increase the value of your solutions.

Rely on smart automation instead of blind actionism.
Start small, test, optimise - and become the expert that companies really need. 🚀