Скачать или смотреть Most teams chase flashy “agents”—the winners master foundations + feedback loops.

What “Foundations” mean in Agentic AI

Core skills: Python, data structures, APIs, prompt patterns, evaluation basics.

Building blocks: Retrieval (vector DB), tools/actions, memory, planning, guardrails, logging/telemetry.

System thinking: Clear objectives, small scoped tasks, measurable outcomes, and safe fallbacks.

A Simple Path (that actually compounds)

Try & Apply — build one focused agentic rep (e.g., tool-using assistant that retrieves, plans, and acts).

Review & Reflect — evaluate with tight metrics: task success, latency, cost, error types.

Plan & Improve — change one variable (prompt, tool schema, retrieval, planner) and run the next rep.

Repeat. That’s how you move from “it sometimes works” to “it works reliably.”

Good Rep Checklist

Clear hypothesis: “Will adding tool X reduce failure Y?”

Tight scope: 45–120 minutes or one work session.

Measurable output: success rate, precision@k, cost, user feedback notes.

5-minute retro: Keep / Change / Try.

Cadence That Scales

Personal: daily micro-rep + Friday 30-min review.

Team: 2-day build → 30-min retro → 2-day build; weekly demo.

Community: public “Rep Cards” to share learnings and raise the bar.

Bottom line: Agentic AI mastery is not a big leap; it’s REPS—fast loops over solid foundations.

If you want my one-page “Rep Card” and a starter checklist for agentic systems, comment FOUNDATION and I’ll share it with you.

#decodingdatascience #dds #AgenticAI #LLM #AIEngineering