Your cart is empty.
Keep shoppingClaude Capybara Mythos Mastery: Build Frontier AI Systems
1,519 students
Claude Capybara Mythos Mastery: Build Frontier AI Systems
Design, evaluate, and deploy next-gen Claude Mythos agents for coding, reasoning, and secure enterprise workflows
Created bySchool of AI
Last updated 4/2026
English
English [Auto],
What you'll learn
- Build agentic AI systems that go beyond prompting by combining planning, execution, and evaluation workflows
- Design and implement multi-agent architectures using the Planner → Executor → Critic pattern
- Apply dual-mode reasoning and create structured outputs such as JSON plans and execution graphs
- Develop AI-powered solutions for code review, debugging, refactoring, and system design
- Create security-aware AI systems that detect vulnerabilities and generate risk reports with remediation steps
- Integrate memory systems (FAISS/Chroma patterns) to enable context retention and long-running workflows
- Implement guardrails, policy engines, and human-in-the-loop approval workflows for enterprise readiness
- Use LLM-as-a-judge evaluation techniques to measure quality, reliability, and performance of AI outputs
- Build systems with tool usage and API integration for real-world automation
- Design observability pipelines with logging, tracing, and cost monitoring for AI systems
- Design observability pipelines with logging, tracing, and cost monitoring for AI systems
- Deliver a complete production-grade frontier AI system as a portfolio-ready capstone project
Explore related topics
Course content
7 sections • 33 lectures • 4h 10m total length
Requirements
- Basic understanding of Python programming (functions, APIs, JSON handling)
- Familiarity with AI/LLM concepts such as prompts, tokens, and model behavior
- Experience using tools like OpenAI API or similar LLM providers (helpful but not mandatory)
- A development environment set up with Python 3.10+, terminal/command line, and a code editor (e.g., Visual Studio Code)
- Ability to read and understand basic code workflows (no advanced software engineering required)
- Curiosity to learn agentic AI systems, multi-agent workflows, and real-world AI applications
- Willingness to build hands-on projects and experiment with prompts, agents, and system design
- Internet connection and ability to install Python packages and run scripts locally
- No prior experience with multi-agent systems is required—this course builds everything step by step from foundational concepts to advanced systems
Description
This course contains the use of artificial intelligence.
Claude Capybara Mythos Mastery: Build Frontier AI Systems is a hands-on, production-focused bootcamp designed to help you move beyond basic prompting and into building real agentic AI systems. This course is built around a simple idea: modern AI is no longer just about generating answers—it’s about designing systems that can reason, plan, act, evaluate, and improve over time.
Across 7 intensive days, you will learn how to work with Mythos-class models, a new generation of AI systems capable of dual-mode reasoning, advanced planning, and structured execution. You’ll start by understanding the evolution from Haiku → Sonnet → Opus → Mythos, and what separates frontier models from traditional production models. This foundation sets the stage for building systems that are not only intelligent, but also reliable and scalable.
The core of the course focuses on turning AI into a thinking engine. You will learn how to design multi-step reasoning chains, enforce planning vs execution separation, and generate structured outputs such as JSON plans and execution graphs. These patterns are critical for building systems that can operate in real-world environments, where ambiguity, failure modes, and incomplete data are the norm.
As you progress, you’ll move into frontier-level coding workflows, where AI is used not just for code generation, but for code understanding, debugging, refactoring, and system design. You will build a Code Review Agent and implement LLM-as-a-judge scoring to evaluate output quality—mirroring how modern AI engineering teams validate systems in production.
A major highlight of the course is Cybersecurity-Native AI, where you will design systems that can detect vulnerabilities, assess risks, and recommend secure fixes. This introduces the critical concept of dual-use AI, along with the controls required to safely deploy powerful models in enterprise environments.
The course then shifts into multi-agent orchestration, where you will build systems using the Planner → Executor → Critic pattern. You’ll integrate tools, memory (FAISS / Chroma patterns), and long-running workflows to create systems that behave like autonomous teams rather than single models.
To make these systems enterprise-ready, you’ll implement guardrails, policy enforcement, and human-in-the-loop approval workflows. You’ll also design full observability pipelines, including logging, tracing, and cost tracking, ensuring that every decision made by the system is transparent and auditable.
The final capstone brings everything together. You will build a production-grade frontier AI system such as an AI Security Analyst Agent, Autonomous Code Refactor System, or Enterprise Decision Agent. This system will include natural language to structured planning, multi-agent execution, memory integration, governance controls, and evaluation pipelines.
By the end of this course, you will not just understand AI—you will be able to design and deploy end-to-end agentic systems that are scalable, secure, and ready for real-world use. This is the shift from prompting models to building intelligent, governed AI systems.
Who this course is for:
- Developers and engineers who want to move beyond basic prompting and build production-grade agentic AI systems
- AI practitioners looking to design multi-agent architectures with planning, execution, and evaluation workflows
- Product managers and technical leaders who want to understand how to build and scale real-world AI systems
- Software engineers interested in applying AI to code review, refactoring, and system design workflows
- Security professionals exploring how to use AI for vulnerability detection, risk analysis, and secure coding practices
- Data and AI specialists aiming to implement LLM evaluation, guardrails, and governance patterns in enterprise settings
- Builders and innovators who want hands-on experience creating autonomous AI workflows with memory, tools, and APIs
- Professionals transitioning into AI who already have basic programming knowledge and want to learn advanced system-level AI design
- Anyone interested in understanding how modern AI systems evolve from single models to intelligent, orchestrated systems