Insights on agentic SDLC
How engineering teams deploy, govern, and scale AI agents across the software development lifecycle. Insights, guides, and analysis.
Latest articles
The Agentic Awakening, and the Layer Above the Agents
Bessemer's Agentic Awakening found 10x faster engineers and under 50% org gains. What it takes to build the layer that reaches Level IV.
Introducing Orchestrations: The Layer That Carries a Ticket from Reported to Shipped
Overcut's new orchestration module carries a ticket from reported to shipped: AI agent orchestration with hard bounds, human gates, and a full decision record.
Why Overcut Is Building the Run and Control Layers for AI Agents in the SDLC
Why the durable value in enterprise AI sits in the run and control layers that execute agents safely and govern what they're allowed to touch.
How Multi-Agent Software Development Works in Enterprise Engineering
Learn how multi-agent software development helps enterprise engineering teams improve speed, coordination, quality, and scale now.
6 Ways Engineering Teams Are Using AI Agent Orchestration to Scale
Explore 6 ways engineering teams use AI agent orchestration to scale workflows, accelerate delivery, and reduce busywork at scale.
The 6 Best AI Code Review Tools for Automated Pull Request Reviews
Explore the 6 best AI code review tools for faster, smarter pull request reviews that improve code quality and team output today.
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