AI Coding Agents Changed How We Write Code
The first wave of AI in software development focused on the individual developer. IDE based agents act as copilots. They sit next to the engineer and assist with writing and refactoring code, generating tests, explaining errors, and navigating large and unfamiliar codebases. These tools are extremely effective at optimizing local productivity, and their impact is undeniable. At the same time, they introduce a new reality. Developers are no longer just writing code. They are constantly shifting between product thinking, planning, debugging, reviewing, documenting, and shipping. Often all within the same flow of work. The modern developer is becoming an omni developer.The Rise of the Omni Developer
In many teams today, a single developer carries responsibility across the entire delivery chain. They interpret product requirements, break work into tasks, design solutions, implement code, review changes, address feedback, update documentation, and manage releases. AI makes this breadth of responsibility possible. But it also exposes a new problem. Despite the presence of intelligent agents, almost all of this work still happens manually. Developers decide which agent to run, when to invoke it, what context to provide, how to sequence different tools, and how to connect outputs across steps. The developer becomes the human orchestrator of a set of disconnected capabilities. This approach does not scale.The Missing Layer: Orchestration and Automation
What is missing from today’s AI development stack is not intelligence. It is coordination. Software development is not a series of isolated prompts. It is a dynamic system of workflows triggered by events, governed by policies, and constrained by human judgment. Bugs are discovered, requirements evolve, security issues surface, tickets are created, reviews are requested, fixes are applied, and documentation must remain in sync. Today, this entire flow is held together by people clicking buttons, copying context between tools, and deciding what should happen next. An agentic SDLC introduces a new layer on top of coding agents. An orchestration and automation layer that treats development as a system, not a collection of interactions.From Manual Agent Usage to Agentic Workflows
There is a fundamental difference between using agents manually inside an IDE and running agentic workflows that respond automatically to real events. In a manual model, a developer opens an IDE, selects an agent, pastes in context, reviews the output, and then decides what to do next. Every step requires conscious intervention. In an agentic SDLC, the flow starts elsewhere. A bug is reported. A workflow is triggered automatically. Relevant context is gathered without manual effort. Analysis runs in the background. Results are attached directly to the work item. A human reviews the outcome and decides whether to approve the next step. The intelligence does not change. What changes is the execution model. Work becomes systematic instead of ad hoc.
Humans Are Still in the Loop, But Their Role Changes
One of the most common misconceptions about agentic systems is that humans are removed from the process. In reality, the opposite is true. Humans move up the stack. Instead of executing steps manually, they define policies, decide what requires approval, and review outcomes rather than raw data. Their role shifts from operating tools to orchestrating intent. In an agentic SDLC, developers are no longer operators. They are decision makers. They determine when automation should proceed, when it should stop, when judgment is required, and when risk is acceptable. This is not about replacing developers. It is about changing where their attention and expertise are applied.Automation as the Backbone of the SDLC
In an agentic SDLC, automation becomes the backbone of development rather than an add on. Workflows are triggered by meaningful business signals such as intake requests, bug reports, security findings, or change requests. From there, automation can create and enrich tickets, triage and classify work, run root cause analysis, define requirements, and plan remediation or implementation. At key decision points, the system pauses. Human approval is required before continuing. Once approved, automation resumes. Designs are created or updated. Work is decomposed into smaller tasks. Code is implemented. Reviews are run. Feedback is applied. Documentation stays in sync. Release notes are generated. The result is a flow that is continuous, predictable, and auditable.Managing Agents Where Work Already Happens
Another critical shift in an agentic SDLC is where control lives. Developers do not manage agents through a separate AI interface. They manage them through the systems they already use every day. Jira and GitHub become the control plane, and in many ways, the new IDE for AI driven development. Workflows are triggered by real activity such as issue creation, label changes, status transitions, pull request events, or scheduled maintenance. Interaction happens through tickets, comments, reviews, and approvals. There is no new surface area to learn. The SDLC itself becomes programmable.Out of the Box Agentic Workflows
In Overcut, every box in the Agentic SDLC diagram marked with the Overcut logo represents an out of the box agentic workflow. These workflows are prebuilt but configurable. They are policy driven and designed with humans in mind. Together, they cover the full lifecycle, from intake and triage to planning, implementation, review, documentation, and ongoing maintenance.
