Writing Code as a Team with AI — Pair Programming Norms
Pair programming changes when AI is the third party in the room. How AI-first teams structure collaborative coding, what norms work, and the question of ownership when AI writes the code.
Akash Talole
AI-First Sprint Planning and Task Breakdown
Sprint planning in an AI-first team looks different — estimation changes, task decomposition changes, and the relationship between a ticket and its implementation changes. What actually works and what to watch out for.
Measuring Progress — What Metrics Actually Matter for an AI-First Team
Most AI team metrics measure the wrong things. Lines of code generated, acceptance rates, time saved — these measure AI activity, not AI value. Here's what to actually measure and why.
AI-First Team Culture: Norms, Expectations, and Psychological Safety
The toolchain is the easy part. The culture is where AI adoption succeeds or fails. What norms an AI-first team needs, what psychological safety looks like in this context, and the conversations most teams avoid having.
Rethinking Team Roles in an AI-First World
AI doesn't eliminate roles — it changes what each role is for. What senior engineers, juniors, and tech leads actually do differently in an AI-first team, and why the value propositions of each role need re-examination.
The Full AI Toolchain for Engineering Teams
Not every AI tool belongs in every team's stack. Here's how to think about toolchain decisions for an engineering team — what each layer does, how the tools interact, and how to avoid the duplication trap.
The AI-First Team Maturity Model — Where Is Your Team Today?
A practical maturity model for engineering teams adopting AI. Not a framework for consultants — a diagnostic for leads and engineers to understand where they actually are and what the next step looks like.
What Does 'AI-First Engineering Team' Actually Mean?
Everyone's talking about AI-first teams. Most of what gets called 'AI-first' is just 'we have Copilot licences.' Here's the actual distinction — and why it matters for how you structure your team.
30 Days Done — What I Learned, What Changed, What's Next
Thirty posts across five arcs. Here's the honest synthesis: the biggest surprises, the things I was wrong about, what actually changed in my thinking, and where I think all of this is heading.
Multi-Agent Debugging and Observability — Staying Sane
When a single agent misbehaves you have one thing to debug. When a multi-agent system misbehaves, the problem could be anywhere across orchestrator, specialists, connectors, and flows. How to build observability that makes the invisible visible.