A clear, do-first roadmap from foundations to agentic systems and MCP/A2A interoperability.
Why this plan
Outcome: Ship a small, real-world agentic app that calls tools, communicates with other agents, and syncs a simple frontend.
Cadence: 4 weeks, ~4 hours/day.
Tooling focus: Python + TypeScript, LangChain/LangGraph/OpenAI Agents, “vibe coding” tools (Cursor, Windsurf, Claude Code), MCP/A2A, TanStack Query, Convex.
High-level timeline 🗓️
The skills pipeline (at a glance) 🧭
Week 1 — Foundations 🧱
Goal: get productive in Python and TypeScript, set up clean environments, and lock a daily routine.
What “done” looks like: you can read docs fast, write small scripts, type confidently in TS, and commit with discipline.
Targets
Python refresh: functions, OOP, async, packaging, venv/pipenv.
TypeScript fundamentals for front-end ergonomics.
Dev setup: VS Code/IDE, Git/GitHub, linters, formatters.
Daily loop (2×2 hours)
Block 1: Python drills → mini script with CLI and a simple API call.
Block 2: TypeScript drills → transform data + render in minimal UI.
End: 10-minute journal of errors learned.
Week 2 — Agent frameworks 🤖
Goal: build agents that call tools, persist state, and hand off tasks.
Stack: LangChain for tools, LangGraph for stateful flows, OpenAI Agents SDK for multi-agent handoffs.
Targets
LangChain: a tool-calling agent with one external API.
LangGraph: state container, retries, guardrails; long-running flow.
OpenAI Agents SDK: two agents with a triage→solve handoff.
Checkpoint: one repo with a CLI entry that runs the full flow end-to-end.
Week 3 — “Vibe coding” tools ⚡
Goal: accelerate with AI coding assistants without losing control.
Tools: Cursor, Windsurf, Claude Code.
Working rules
Use AI to draft, but you own the diff.
Keep prompts inside the codebase as .ai-notes/ for audit.
Prefer small, reversible edits.
By Friday, the assistant can refactor a module, extend tests, and fix type errors on request.
Week 4 — Protocols + full-stack shipping 🛰️
Goal: Make your agent communicate with tools and other agents using open protocols, then display results in a compact, reactive UI backed by a managed backend.
Focus: MCP (Model Context Protocol), Google A2A, TanStack Query, Convex.
How MCP/A2A fit together
Frontend↔Backend data model
Capstone scope
Feature: question triage + answer draft + optional tool call.
Interop: one A2A handoff to a specialist.
UI: list of requests with optimistic updates and cached detail view.
Ops: simple logging, error paths, and a “retry” button.
Daily operating rhythm ⏱️
Milestones & proof of work ✅
W1: TS + Py kata repo with clean tooling.
W2: agent with tool calling + state + handoff.
W3: assistant-driven refactor with auditable diffs.
W4: capstone app with MCP/A2A interop and a minimal UI.
Risk controls 🧯
Freeze scope weekly. No new tools mid-week.
Keep every run reproducible with a single script and a short README.
Track model/input/output and tool versions in a run log.
Add circuit-breakers and timeouts on tool calls.
Visual index of the plan 🧠
Suggested courses to pair with each week 🎯
Week 1 foundations → AI & Machine Learning Mastery for Python foundations and ML context. ( AI Trainings )
Week 2 agents → Generative AI & LLMs / Mastering Large Language Models for LLM mechanics; pair with Mastering Prompt Engineering. ( AI Trainings )
Week 3 vibe coding → Generative AI for Beginners for quick wins and mindset on GenAI tooling. ( AI Trainings )
Week 4 automation + interop → n8n Automation & AI Agents Training to practice agent flows and orchestration. ( AI Trainings )
Stretch/advanced → Mastering Advanced AI & Prompt Engineering for complex workflows and multi-model design. ( AI Trainings )
Catalog overview → Browse all trainings and pick depth based on gaps. ( AI Trainings )
Final checklist before you ship 🧪
Traces show every tool call and A2A exchange.
Latency within target; add caching where safe.
Frontend queries are cached and invalidated predictably.
README documents how to run, test, and extend.
You’ve got the map. Build small. Integrate early. Ship weekly.
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