Insights on agentic SDLC
How engineering teams deploy, govern, and scale autonomous agents across the software development lifecycle.
Latest articles
Build vs Buy Your SDLC Orchestration Layer: The Legacy Clock Starts at Commit One
Why homegrown AI orchestration becomes legacy infrastructure from the first commit, and why your engineering attention belongs on the product instead.
Agentic SDLC Orchestration vs. Synchronization
Why centralized workflow engines fail AI-driven engineering teams, and how modular SDLC orchestration enables agent autonomy and event-driven agility.
The Plateau at Level Three
Why most AI-native teams stall at level 3 of agentic development, what it takes to climb to level 4, and where the road leads after that.
Workflows That Remember
Building a self-improving automation engine with persistent memory, automated retrospectives, and weight-based knowledge management
The Agentic Software Development Lifecycle
AI made individual developers faster, but software development is still slow, manual, and fragmented at the lifecycle level. The Agentic SDLC is about moving from isolated AI assistance to automated, policy driven workflows with humans in control.
The Real Future of AI Development Isn’t a New IDE. It’s a New Interface for Work
The future of development depends on redefining the interface where work is planned, coordinated, and approved
From Engineering Chaos to Agentic Chaos
How AI agents are creating a new kind of disorder in the SDLC - and how to turn chaos into collaboration
Overcut vs. n8n for Production-Grade Dev Automation
Ship Faster with Confidence: Overcut vs. n8n for Production-Grade Dev Automation
How Enterprises Can Adopt AI Developer Tools Successfully
Challenges, benefits, and best practices for enterprises adopting AI dev tools and how Overcut helps.
Overcut vs. Copilot, Cursor, and ChatGPT
Overcut vs. Copilot, Cursor, and ChatGPT - Choosing the Right AI for Your Dev Workflow
Introducing Overcut
Introducing Overcut - Automate your SDLC with Agentic Workflows