Overview
01Command Line0/8
  • Shell, Quoting, and Expansion
  • Redirection, Pipes, and Exit Codes
  • Processes and Job Control
  • Environment Variables and PATH
  • Package Managers and Toolchains
  • Search and Text Processing
  • Git Objects, Branches, and Diffs
  • Git Worktrees, Reflog, and Recovery
02Model Behavior0/7
  • Tokens and Tokenization
  • Context Windows
  • Sampling Parameters
  • Reasoning and Test-Time Compute
  • Why Models Hallucinate
  • Prompting, RAG, and Fine-Tuning
  • Model Selection and Cost
03How Claude Code Works0/11
  • The Agentic Loop
  • The System Prompt and What Loads
  • The Built-In Tools
  • Context Management and Compaction
  • The Permission Model
  • Auto Mode and the Classifier
  • Settings and Precedence
  • CLAUDE.md, Rules, and Memory
  • Sessions, Checkpoints, and Worktrees
  • Headless Mode and the CLI
  • Cost and Prompt Caching
04Customization0/8
  • Agent Skills
  • Writing a Skill
  • Slash Commands
  • Hooks
  • Writing a Hook
  • Subagents
  • Plugins and Marketplaces
  • Choosing Between Skill, Subagent, Command, and MCP
05MCP0/8
  • What MCP Is
  • Tools, Resources, and Prompts
  • Transports and the Stateless Model
  • MCP in Claude Code
  • The API MCP Connector
  • Building a Server
  • Tool Design for Agents
  • When Not to Use MCP
06Building on the API0/9
  • The Messages API
  • Tool Use
  • Writing the Agentic Loop
  • Server Tools
  • Structured Outputs
  • Extended Thinking and Effort
  • Prompt Caching
  • Message Batches
  • The Claude Agent SDK
07Context and Retrieval0/9
  • Context Engineering
  • Compaction, Notes, and External Memory
  • Embeddings
  • Chunking
  • Hybrid Search
  • Reranking
  • Contextual Retrieval
  • Agentic Search and Long Context
  • Evaluating Retrieval
08Orchestration0/7
  • Orchestration Topologies
  • Orchestrator and Workers
  • When Multi-Agent Helps and When It Hurts
  • Passing State Between Agents
  • Harness Design
  • Long-Horizon Agents
  • Multi-Agent Failure Modes
09Evals and Observability0/8
  • Eval-Driven Development
  • Error Analysis and Failure Taxonomies
  • Graders and Eval Types
  • LLM-as-Judge
  • Statistics for Evals
  • Evaluating Agents
  • Benchmarks and Their Limits
  • Tracing and Observability
10Security0/10
  • The Agent Threat Model
  • Prompt Injection
  • The Lethal Trifecta
  • Exfiltration Channels
  • Tool Permissioning and Least Privilege
  • Human Approval and Its Limits
  • Sandboxing
  • Supply Chain Risk in Skills, Plugins, and MCP Servers
  • Vetting a Third-Party Skill or Server
  • Secrets and Agent Identity
11Deployment and Governance0/7
  • Enterprise Administration
  • Managed Settings and Policy
  • Gateways and Cloud Routing
  • Cost Controls and Usage Analytics
  • Data Retention and Zero Data Retention
  • Compliance Posture
  • AI Governance Frameworks
12Adoption0/6
  • Adoption Program Models
  • Enablement and Internal Libraries
  • Measuring Adoption and Impact
  • The Productivity Evidence
  • Change Management for Knowledge Workers
  • Teaching Methods That Work
Extended lessons
Curriculum/Evals and Observability/Evaluating Agents

Evals and Observability · Agent evals

Evaluating Agents

Video + articles

Video

From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI

AI Engineer · 20 min · July 25, 2026

Articles

Demystifying evals for AI agents

Anthropic Engineering · Anthropic · January 9, 2026

Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations

arXiv · Seshadri et al. · January 23, 2026

AI Agents That Matter

arXiv · Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan · July 1, 2024

Supplementary

Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor, Laude Institute

AI Engineer · 21 min · July 24, 2026

Agentic Evaluations Workshop - Deep Dive on the Future on Evals for Agents.

Hugging Face · 1 hr 49 min · March 20, 2026

Agentic Evaluations at Scale, For Everybody — Nicholas Kang & Michael Aaron, Google DeepMind

AI Engineer · 20 min · May 25, 2026

Previous

Statistics for Evals

Evals and Observability · AI Agent Frontier · 1 hr

Up next

Benchmarks and Their Limits

Evals and Observability · TNG Technology Consulting GmbH · 50 min