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.
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.
Open-Source vs Commercial Models — The 2026 Decision
In 2025, open-source models were clearly behind. In 2026, the gap has closed significantly on many tasks. The framework for deciding when open-source deployment makes sense and when commercial APIs are still the right call.
The Reasoning Model Revolution — Beyond Next-Token Prediction
Reasoning models represent a different approach to AI capability — not better pattern matching, but explicit test-time computation. What changed architecturally, why the benchmark gaps are real, and where the technology is heading.
CrewAI for Enterprise Multi-Agent Workflows
CrewAI's role-based model makes multi-agent collaboration intuitive to design and reason about. How to structure enterprise workflows as agent crews, integrate MCP tools, handle the enterprise deployment requirements, and where CrewAI's model excels over LangGraph's.
Building Production Agents with LangGraph — A Hands-On Walkthrough
A complete production-grade research agent built with LangGraph: typed state, tool execution, human-in-the-loop review, PostgreSQL persistence, and error recovery. The patterns that make LangGraph agents reliable in production.
Scaling Agentic Systems — Cost, Latency, and Reliability
Agentic systems that work at 10 users per day face different problems at 10,000. Horizontal scaling, async processing, caching, rate limit management, and the reliability patterns that keep agents working when the underlying LLMs don't.