The AI-First Engineering Team — 30-Day Blog
A 30-day series on what changes when an engineering team — not just an individual engineer — adopts AI as a first-class part of how they work. From culture and workflows to governance and the human side.
Akash Talole
30 Days of AI Engineering — My Content Plan as a Lead AI Engineer
A structured 30-day blog roadmap covering Claude Code, GitHub Copilot, Microsoft Copilot Studio, agentic AI, coding agents, and AI in SDLC — from a Lead AI Engineer with 11 years of experience.
Spec-Driven Development — Writing Specs That AI Agents Can Execute
The primary interface between engineers and AI coding agents is shifting from prompts to specifications. What makes a spec executable by an AI agent, how to write them, and why this is the pattern that scales.
Self-Healing CI — Agentic Pipelines That Fix Their Own Failures
CI pipelines fail. Usually at the worst time. Agentic CI adds a layer that diagnoses failures, attempts fixes, and either resolves the issue automatically or surfaces a precise description of what needs human attention.
Autonomous PR Agents — How to Review Code When AI Opens the PR
Agent-opened pull requests are mainstream. The review process that works for human-written code doesn't fully transfer. What changes, what the failure modes look like, and how to build a review workflow that catches what matters.
MCP Goes Stateless — What the July 2026 RC Means for Your Stack
The July 2026 MCP release candidate removes session management entirely, introduces a formal Extensions Framework, and changes how Tasks, caching, and deprecated features work. What this means if you're building on MCP today.
Beyond Vibe Coding — What AI-Native Engineering Actually Looks Like
Vibe coding gets software built fast. It doesn't get software built well. The five building blocks of AI-native engineering — and what separates teams shipping quality from teams shipping demos.
Context Engineering — The Discipline That Replaced Prompt Engineering
Prompt engineering optimises a single turn. Context engineering designs the entire information environment an agent operates in. The shift from one to the other is the shift from demos to production.
Harness Engineering — Building the AI Control Plane
Harness engineering is the discipline of building the execution environment around AI agents — the layer that determines reliability, safety, and cost more than model choice does.
Where Agentic AI Goes Next — The Honest View
30 days of production agentic AI engineering. What the hype gets wrong, what's genuinely changing, the capabilities that are real versus the ones that are demo-ware, and the engineering skills that will matter over the next two years.