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AI Agents, How to Design Profitable Architectures

This is an 18-lesson course across 5 modules designed to transform Generative AI LLM agents—which often drain money through API costs—into a profitable structure by controlling expenses with model routing, cascading, caching, and loop guardrails. You will personally build everything from analyzing real-world cases of "cost bombs" to profit-and-loss simulations and architecture design projects. We recommend this course to AI developers and makers burdened by API costs, as well as those planning or operating agent-based services.

2 learners are taking this course

Level Basic

Course period Unlimited

Python
Python
AI
AI
AI Agent
AI Agent
Service Planning
Service Planning
LLM
LLM
Python
Python
AI
AI
AI Agent
AI Agent
Service Planning
Service Planning
LLM
LLM

What you will gain after the course

  • You can diagnose where your agent is spending money by applying call-level metadata logging, three-stage visibility (Call/Request/Step), and the three principles of FinOps.

  • You can directly implement model routing logic in code to call either low-cost or high-cost models depending on the difficulty of the task.

  • You can configure a model cascading structure that processes with a lower-cost model first and switches to a higher-tier model upon failure, and compare costs before and after application.

  • By applying prompt caching and semantic caching, you can reduce re-calls of redundant and similar requests and monitor changes in response speed and costs.

  • You can prevent agent infinite loops and overspending by implementing loop guardrails that set a maximum number of iterations and a token budget.

  • You can create your own profit and loss simulation by entering the token unit price per model, call frequency distribution, expected revenue, and effective cost after applying usage limits.

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


LLM Agents That Earn Even a Single Penny: How to Design a Profitable Architecture

Lecture Summary

Diagnose the causes of LLM agent cost bombs (infinite loops, unnecessary model calls, lack of caching) and design a profitable architecture that generates margins by applying model routing, cascading, caching, and guardrails in this course consisting of 5 modules and 18 lessons.

You will personally build everything from analyzing real-world cases of "cost bombs" to profit-and-loss simulations and architecture design projects. This is recommended for AI developers and makers burdened by API costs, as well as those planning to operate or design agent-based services.

Recommended for these people

Service developers, makers

AI developers and makers feeling the burden of API costs: Those who are running LLM agents and need to identify the causes and find solutions because costs are increasing uncontrollably.

Service Operations, Planning, PO, PM

Those planning or operating agent-based services: Those who want to diagnose situations where existing cost structures are no longer profitable due to changes in ad unit prices or the revenue environment, and create profit and loss simulations.

Working professionals, Job seekers

Practitioners who have experience using LLM APIs and basic programming knowledge and want to directly implement cost-reduction techniques such as model routing and caching in code, as well as job seekers who want to demonstrate business logic during interviews

What will you achieve after the course?

  • You will be able to directly calculate and diagnose where and why API costs are leaking.


  • You will be able to control costs by implementing model routing, cascading, caching, and guardrails through code.


  • You will walk away with a profit and loss simulation for your service, a cost structure diagnostic report, and an integrated architecture design as your final deliverables.

Lecture Preview

Frame 4

Examples through real-world cases

Specific management methods

Why do you need to know this design method now?

As services incorporating LLM agents increase, it is becoming common to see cases where API costs grow faster than revenue due to calling expensive models for every request or repeatedly processing the same requests.

In times when advertising costs (CPA·CTR) are volatile, you may find yourself in a situation where no margin remains even if sales increase, unless you change your cost structure.

This course examines this issue through actual operational cases and covers how to connect technical cost-reduction measures to operational decision-making that protects profits.

Introduction to Learning Content

  • Module 1. Course Introduction and Basic Setup — We will identify typical patterns of agent cost issues, set up API keys, cost monitoring tools, and the practice environment, and then diagnose the cost structure of your own agent.

  • Module 2. Causes of Cost Bombs and Principles of Resolution — Examine cost bomb scenarios through actual operational cases and learn the theoretical principles of model routing, cascading, and prompt/semantic caching.

  • Module 3. Hands-on: Applying Cost-Reduction Architecture — Directly apply concepts through four hands-on sessions, including implementing routing logic, configuring cascading flows and comparing costs, applying caching, and setting up loop guardrails and token budgets.

  • Module 4. Connecting to a Profitable Structure — We will summarize why cost optimization directly becomes a revenue strategy, cover cases of redesigning cost structures in response to changes in the advertising/sales environment, and create a profit and loss simulation.

  • Module 5. Practical Project — You will wrap up by creating a cost structure diagnostic report for your service, designing an architecture that combines routing, caching, and guardrails, and reviewing additional points to consider during operation.

You will be able to do this.

  • You will be able to directly calculate and diagnose where and why API costs are leaking.


  • You will be able to implement model routing, cascading, caching, and guardrails in code to control costs.


  • You will walk away with a profit and loss simulation for your service, a cost structure diagnostic report, and an integrated architecture design as final deliverables.


The person who created this course

  • I come from a startup background and currently work as a development lead at a large corporation. I lead projects that emphasize not only development but also business literacy.

  • Through side projects, I learned the key considerations when using AI LLM models, and I conducted several offline lectures by documenting these insights in a simple and concrete manner. Following strong requests for an online course, I plan to release a series of AI-related content.

Notes before taking the course

FAQ

Is a paid API subscription absolutely necessary for the practice sessions?

- Yes, you need to register a payment method for both OpenAI and Anthropic. The course is guided based on issuing keys for both model providers (this is necessary for the practice sessions, as there is a configuration to compare the two models during the model routing and cascading exercises).

Can I follow along even if I don't know much about programming?

- It will be easy to follow if you have basic experience using Python or Node environments. The hands-on exercises are designed to implement core logic in short snippets rather than large-scale projects.

Learning Materials

  • Source code provided for each code implementation

Prerequisite Knowledge / Notes Before Taking the Course

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


  • It is suitable for individual makers or small teams who are currently operating or planning to operate an agent service.


  • For the practice session, you will need to issue an LLM API key, set up a cost-tracking dashboard, and install a local practice environment, all of which will be guided in lesson 1-3.


Recommended for
these people

Who is this course right for?

  • AI developers and makers feeling the burden of API costs: Those who are running LLM agents and need to identify the causes and find solutions because costs are increasing uncontrollably.

  • Those who intend to operate or plan agent-based services: individuals who wish to diagnose situations where existing cost structures are no longer profitable due to changes in advertising unit prices or the revenue environment, and who want to create profit and loss simulations.

  • Practitioners who have experience using LLM APIs and basic programming knowledge, and who want to directly implement cost-reduction techniques such as model routing and caching through code.

Need to know before starting?

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

  • It is suitable for individual makers or small teams who are currently operating or planning to operate an agent service.

  • For the practice session, you will need to issue an LLM API key, set up a cost-tracking dashboard, and install a local practice environment, all of which will be guided in lesson 1-3.

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Curriculum

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18 lectures ∙ (2hr 24min)

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