The Complete AI Agent Engineering Curriculum (7 Core Masterclasses) ⚡

The 7-Module Agent Engineering Roadmap

Study these modules in sequential order to build a rock-solid mental model of modern agentic systems:

Core Concept: Operating autonomous CLI agents that navigate codebases, inspect diffs, execute terminal commands, and solve complex multi-file engineering problems directly in your shell.

• The CLI agent loop: perception → tool selection → execution → reflection.
• Terminal safety rails, permission boundaries, and background process management.
• Configuring CLAUDE.md architecture guides and memory files for long-horizon tasks.

Core Concept: How to build modular, declarative capability packages that teach agents how to perform specialized workflows without bloating the system prompt.

• The SKILL.md specification: YAML frontmatter, execution instructions, and validation gates.
• Progressive context loading: keeping context windows lean by dynamically loading scripts only when triggered.
• Packaging deterministic bash and python tools alongside semantic agent instructions.

Core Concept: You cannot improve what you do not measure. How to build rigorous eval suites that gate prompt updates and code releases with quantitative pass rates.

• The difference between deterministic unit tests and semantic LLM-as-a-judge rubrics.
• Pass@1, Pass@k, and regression benchmark tracking.
• Building synthetic golden evaluation datasets from historical production edge cases.

Core Concept: Understanding the underlying mathematics and architecture of auto-regressive transformers to predict failure modes and optimize context usage.

• Self-attention mechanisms, query-key-value vectors, and positional embeddings.
• Tokenization pitfalls, needle-in-a-haystack context degradation, and KV caching.
• Sampling temperature, top-p, and logit bias mechanics.

Core Concept: Taking agents from local prototypes to robust production environments with end-to-end tracing, rate-limiting, and runtime error recovery.

• Full execution tracing with LangSmith and Langfuse: inspecting latency, tokens, and prompt payloads.
• Defensive guardrails: input sanitization, JSON schema enforcement with Pydantic, and hallucination tripwires.
• Cost allocation, model tiering (fast flash models vs frontier reasoning models), and cache hits.

Core Concept: Orchestrating multiple specialized agents working together in cyclic state graphs with deterministic branching, persistence, and human oversight.

• State definitions, node execution, and conditional edge transitions.
• Hierarchical patterns: Supervisor → Worker → Evaluator multi-agent clusters.
• Time-travel debugging and human-in-the-loop approval gates before high-stakes actions.

Core Concept: When prompting and RAG hit their ceilings, learn how to adapt open-weight models (Llama, Mistral, Qwen) using Parameter-Efficient Fine-Tuning (PEFT).

• High-quality dataset curation: synthetically bootstrapping training pairs with frontier reasoning models.
• LoRA (Low-Rank Adaptation) and QLoRA: training 70B parameter models on single consumer GPUs.
• Direct Preference Optimization (DPO) and RLHF alignment strategies.

Use this prompt to design and blueprint a complete multi-agent production system before writing your first line of code:

You are a principal AI systems architect. I want to build an autonomous agent for: [DESCRIBE OBJECTIVE, e.g., Automated Competitor Pricing Intelligence].

Generate a production-grade Agent Architecture Specification containing:
1. AGENT TOPOLOGY: Define whether this requires a single ReAct loop or a multi-agent supervisor graph.
2. STATE SCHEMA: Define the exact state variables, typed Pydantic models, and persistence checkpoints.
3. TOOL SUITE: Specify deterministic tools (APIs, scrapers, DB queries) vs. LLM reasoning nodes.
4. ERROR RECOVERY & RE-TRY LOOPS: How the agent handles tool execution failures, hallucination fallbacks, and infinite loop breaks.
5. EVALUATION HARNESS: 5 automated unit test assertions and an LLM-as-a-judge scoring rubric to verify accuracy before deployment.

Format as a technical architecture design document.