Your Medium piece is a tour de force for anyone puzzling over the crowded agent-framework landscape. I loved how you open with a crisp executive summary that quantifies adoption (“51 % already in prod, 78 % planning roll-outs”) and frames a three-step maturity path—prototype with LangGraph, scale with AutoGen, lock-down with Semantic Kernel  . That top-down structure makes the rest of the article feel like a guided hike rather than a feature dump.

What stands out

• Decision-grade criteria. The section where you score maturity, orchestration depth, observability & guardrails, and enterprise ecosystem reads like an internal RFP checklist and immediately grounds the comparison in real-world constraints  .
• Deep but digestible dives. Each framework walkthrough balances architecture notes with runnable snippets—e.g., the four-line MemoryGraph → AgentNode chain for LangGraph  and the decorator-based tool registration in AutoGen  . Those code blocks turn abstract claims into concrete “aha!” moments.
• Enterprise lens. Your emphasis on OpenTelemetry hooks, RBAC plugins, and Azure compliance for Semantic Kernel hits the sweet spot for regulated-industry readers who need more than a weekend hack  .
• Actionable take-aways. The five-point best-practice mantra at the end (“Start small… Instrument everything… Plan for interoperability”) gives readers a Monday-morning playbook, not just weekend inspiration  .

Why it matters

Agentic AI is moving from hobby to mission-critical infra, and your post acknowledges the messy realities—API churn, plugin governance, cross-language parity—while still conveying optimism. It’s rare to see a piece that is simultaneously strategic (board-level framing) and tactical (pip install specifics).

Kudos for weaving in market-size figures, GitHub velocity stats, and concrete mitigation tips—details that earn trust without overwhelming. I’ll be pointing engineering leads to this article whenever the “Which agent stack?” question comes up.

Great read, Devashish—keep the deep dives coming!