AI Engineering and Autonomous Systems

Master reliable AI engineering by building production-ready agents and deep technical foundations tailored specifically for your daily software development workflow. * **Build reliable, production-ready AI systems** using rigorous testing and guardrails. * **Architect autonomous agents** that execute complex multi-step software engineering tasks. * **Implement advanced RAG patterns** to provide models with precise technical context. * **Optimize LLM performance** by mastering underlying architectures and fine-tuning strategies.

7 sections ยท 28 lessons

Course outline

Agent Reliability

  1. Building Evals
  2. Catching Regressions
  3. Agent Permissions
  4. Security Risks
  5. Agent Reliability

AI-Native Engineering

  1. Workflow Shift
  2. Agent Archetypes
  3. Codebase Context
  4. Tooling Landscape
  5. AI-Native Engineering

Agent Architecture

  1. Autonomous Loops
  2. Tool Execution
  3. Multi-Agent Systems
  4. Practical Utility

Context Engineering

  1. Context Assembly
  2. Memory Systems
  3. Retrieval Architectures
  4. Knowledge Ingestion

Distributed Systems

  1. Inference Serving
  2. State Consistency
  3. Fault Tolerance
  4. Traffic Orchestration

Model Mechanics

  1. Transformer Fundamentals
  2. Training Pipelines
  3. Reinforcement Learning
  4. Persistent Hallucinations

The Frontier

  1. Frontier Debates
  2. Signal Tracking
  3. Scaling Limits
  4. Future Paradigms

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