Thirty days of typed decisions, memory, orchestration, RAG, critics, and a capstone multi-turn assistant. Rate yourself on the same six questions from Day 1 — the gap is the whole point.
On Day 1 I had never written a stateful agent. Now I reach for result_type, system_prompt, and history lists without thinking. The mental model shifted from "AI as magic" to "agent as function."
And the capstone on Day 28 — how did wiring search plus plan plus history into one function feel compared with your first Literal call on Day 3?
Much smaller than I expected. Once the patterns click, three agents composed is shorter than my first attempt at one.
That is the shift the track is designed to land — from isolated calls to a clean state-flow pipeline. Rate honestly and compare with Day 1.
On Day 1 you rated your confidence on six agent skills: decisions, memory, orchestration, RAG, feedback loops, and the full agent cycle. Today you rate the same six.
The functions you wrote this month — decide_action, stateful_response, morning_orchestration, rag_answer, refine_with_feedback, full_agent_run — are real, production-shape agents. Not demos. They call live LLMs, return validated Pydantic types, and compose through plain Python values.
The mental model transfers: an agent is a function whose output shape you declare. Memory is state your code passes between calls. The specific patterns in this track are examples; the discipline of narrow roles sharing typed state is what you keep.
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Thirty days of typed decisions, memory, orchestration, RAG, critics, and a capstone multi-turn assistant. Rate yourself on the same six questions from Day 1 — the gap is the whole point.
On Day 1 I had never written a stateful agent. Now I reach for result_type, system_prompt, and history lists without thinking. The mental model shifted from "AI as magic" to "agent as function."
And the capstone on Day 28 — how did wiring search plus plan plus history into one function feel compared with your first Literal call on Day 3?
Much smaller than I expected. Once the patterns click, three agents composed is shorter than my first attempt at one.
That is the shift the track is designed to land — from isolated calls to a clean state-flow pipeline. Rate honestly and compare with Day 1.
On Day 1 you rated your confidence on six agent skills: decisions, memory, orchestration, RAG, feedback loops, and the full agent cycle. Today you rate the same six.
The functions you wrote this month — decide_action, stateful_response, morning_orchestration, rag_answer, refine_with_feedback, full_agent_run — are real, production-shape agents. Not demos. They call live LLMs, return validated Pydantic types, and compose through plain Python values.
The mental model transfers: an agent is a function whose output shape you declare. Memory is state your code passes between calls. The specific patterns in this track are examples; the discipline of narrow roles sharing typed state is what you keep.