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AI Agents: How to Design Revenue-Generating Architectures

A course comprising 5 modules and 18 lessons that transforms generative AI LLM agents that only drain money through API costs into a structure that controls expenses and actually generates margins through model routing, cascading, caching, and loop guardrails. You’ll build everything hands-on, from analyzing real-world cost explosion cases and simulating profitability to designing the architecture. Recommended for AI developers and makers burdened by API costs, as well as anyone looking to operate or plan agent-based services.

(4.5) 6 reviews

50 learners

Level Basic

Course period Unlimited

Python
Python
Interview
Interview
Service Planning
Service Planning
AI
AI
LLM
LLM
Python
Python
Interview
Interview
Service Planning
Service Planning
AI
AI
LLM
LLM

What you will gain after the course

  • When developing an AI service, you can learn the technical techniques essential for making your service actually generate revenue and turning a profit by increasing revenue beyond costs.

  • By applying per-call metadata logging, three-level visibility into calls, requests, and steps, and the three FinOps principles, you can diagnose where your agent is spending money.

  • You can directly implement various techniques for optimizing LLM costs—such as model routing, cascading, caching, and guardrails—through hands-on practice.

  • I can design an architecture diagram and implementation plan that combine routing, caching, and guardrails into one, and write a cost structure diagnostic report.

Recommended for
these people

Who is this course right for?

  • AI developers and makers who are burdened by API costs: Those operating LLM agents whose costs are growing uncontrollably and who need to identify the cause and find solutions

  • For those planning or operating agent-based services: those who want to diagnose situations where the existing cost structure is no longer profitable due to changes in advertising rates or the revenue environment, and create a profit-and-loss simulation.

  • Practitioners with experience using LLM APIs and basic programming knowledge who want to implement cost-saving techniques such as model routing and caching directly in code.

Need to know before starting?

  • If you have experience using LLM APIs (OpenAI/Anthropic, etc.) and basic programming knowledge (Python/Node, etc.), it will be easy to follow the hands-on exercises.

  • Suitable for those who are currently operating or planning to operate an agent service as an individual maker or small team.

  • For the hands-on exercises, you will need to obtain an LLM API key, set up a cost-tracking dashboard, and install a local practice environment; instructions will be provided in Lesson 1-3.

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Career Verified

159

Learners

11

Reviews

4

Answers

4.7

Rating

3

Courses

🏦 Former early member of Toss · Founding member of Toss Payments | Backend developer with experience developing advertising, loan, insurance, and payment APIs

🚗 Former) Kakao Mobility large-scale traffic systems · Founding member and CTO of BoltUp, a joint venture between LG Uplus and Kakao Mobility

🛒 Former e-commerce development lead at Peace Minor One, Carhartt, and Worksout · Former CTO of Newploy

🚀 Former undergraduate startup CEO for 5 years (2014 Red Herring Asia Top 100 Startup) · Solo-developed “MERS Map,” reaching 1 million DAU and covered by domestic and international media · Indie game developer ranked in the top 10 in the App Store Games category (2010)

🎓 Software Maestro Mentor | Conducting 0-1 mentoring and system design classes

🤖 In the age of AI coding, I teach juniors not “what to use,” but “why seniors choose it.”

 

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Curriculum

All

20 lectures ∙ (2hr 24min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

6 reviews

4.5

6 reviews

  • bluepicture081732님의 프로필 이미지
    bluepicture081732

    Reviews 3

    ∙

    Average Rating 5.0

    5

    61% enrolled

    It was very fresh and helpful because it covered "cost reduction," which is hard to find anywhere else! I really hope those who have struggled with the costs of currently running services, or those who found it difficult to set monitoring metrics for similar agent-based services in their companies, will definitely listen to this!!

    • chococoditat님의 프로필 이미지
      chococoditat

      Reviews 2

      ∙

      Average Rating 5.0

      5

      100% enrolled

      I’m running a service that uses AI APIs as a side project, and after taking this course, I applied model routing and caching with agents. It seems to have reduced my usage by around 20%, though I still need to experiment with it further. Rather than simply listing tips, the course explains why these approaches are necessary based on code, which made it easier to understand. That said, the techniques covered in the course are things I hadn’t paid attention to before taking it, but they are also the kind of information you can find if you do some research afterward. However, there aren’t many resources that properly organize everything around real-world examples in one place. So it was helpful for quickly reviewing concepts I was unfamiliar with from start to finish, understanding how to use them for monetization, and grasping their importance.

      • donghyeongo3481님의 프로필 이미지
        donghyeongo3481

        Reviews 6

        ∙

        Average Rating 5.0

        5

        32% enrolled

        • sanghyukp2471님의 프로필 이미지
          sanghyukp2471

          Reviews 9

          ∙

          Average Rating 5.0

          5

          63% enrolled

          • peace7335님의 프로필 이미지
            peace7335

            Reviews 1

            ∙

            Average Rating 5.0

            5

            30% enrolled

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