Software development is changing, not because developers have stopped writing code and not because AI is magically replacing software teams. The more interesting shift is the move from writing every line of code manually to supervising, guiding, reviewing, and increasingly orchestrating AI-enabled development work.
For business leaders, this is not just a technology change. It affects delivery speed, governance, risk, team structure, quality assurance, procurement, and the way organisations think about building software.
What is it?
We can think about this as a type of maturity model with 5 stages.
Stage 1 - Conventional Development: Software development has followed a fairly familiar model. Developers wrote code in an IDE, used Git for version control, relied on tickets or user stories to guide the work, and moved changes through testing, review, and deployment pipelines (increasingly automated).
Stage 2 - AI Assisted Development: Stage 1 still exists but AI has started to reshape the workflow around it. At the foundational level, developers use tools like OpenAI ChatGPT, Anthropic Claude, or GitHub Copilot to explain errors, suggest snippets, or help troubleshoot problems with the developer still firmly in control. The AI is useful, but mostly reactive. It answers questions, suggests options, and helps with isolated tasks.
Stage 3 - AI-integrated development: Here, AI is built directly into the development environment. Tools such as Cursor, Windsurf (I started with Codeium in VSCode and loved it), and deeper GitHub Copilot integrations can help across files, refactor code, generate tests, explain dependencies, and work more directly inside the flow of development. The developer is no longer just asking questions on the side. AI becomes part of the working environment.
Then comes agentic development.
Stage 4 - Agentic Development: (This is where I am.) This is where coding agents begin to take on bounded development tasks. Tools such as Claude Code, OpenAI Codex-style agents, GitHub Copilot coding agent, and Cursor Agent can work through a task, modify files, run checks, respond to errors, and iterate. The developer is not just an author or editor. They become a supervisor of work being carried out by an AI system (this is where the difference between vibe coding and agentic engineering really appears).
Stage 5 - Orchestrated Agentic Engineering: This is where multiple agents, potentially with different roles, work together across a broader software lifecycle. One agent may analyse requirements. Another may write code. Another may generate tests. Another may review security concerns. Another may update documentation. At that point, the work starts to look less like “AI helping a developer code” and more like “a human orchestrating a small team of specialised AI workers.”
That is a very different operating model.
It does not remove the need for skilled people, in fact, I’d argue at Stage 5 it may increase the need for people who understand architecture, quality, risk, security, maintainability, and business context. The work changes from “can you write the code?” to “can you define the right outcome, constrain the work, review the result, and know when the system has gone wrong?” - a much higher-level skill.
What does it mean from a business perspective?
This is not just a developer productivity story, it is an organisational change story. A few practical implications stand out:
Speed will increase, but not evenly. Some tasks will move dramatically faster, especially prototypes, internal tools, scripts, test generation, documentation, and refactoring. Other work will still require careful design, stakeholder input, security review, integration planning, and change management.
The bottleneck may move. If coding becomes faster, the constraint may shift to requirements, architecture, review, testing, security, data access, deployment, or business decision-making. Faster code generation does not automatically mean faster delivery of useful systems.
Quality assurance becomes more important, not less. AI-generated code can be impressive. It can also be subtly wrong, insecure, inconsistent, or hard to maintain. Organisations need stronger review practices, not blind trust in the output. (I try and keep my PR’s small so I can read them and then iterate through them more quickly - doesn’t always work out that way.)
Governance needs to catch up with practice. Many teams are already using AI tools informally. The question is whether the organisation understands which tools are being used, what data is being exposed, how code is reviewed, and where accountability sits.
The human role changes. Developers, architects, analysts, product owners, and managers may all need to adapt as the lines between them blur. The value shifts toward problem framing, decomposition, review, judgement, and orchestration.
Junior roles may need rethinking. If AI handles more of the simple implementation work, organisations need to be intentional about how junior developers learn. You cannot build senior judgement if people never develop foundational understanding.
Procurement and vendor management become more complex. Buying “AI-enabled development tools” is not enough. Organisations need to understand where tools sit in the workflow, what permissions they require, how they handle data, and whether they fit the organisation’s risk profile.
Architecture matters more. Agentic tools can generate a lot of code quickly. Without architectural direction, that can become a faster route to technical debt. The better the structure, standards, and constraints, the more useful these tools become.
The gap between mature and immature teams may widen. Teams with clear practices, good documentation, automated testing, modern repositories, and strong review habits will benefit more. Teams with messy environments may simply produce more mess, faster.
That last point is important - agentic software engineering does not magically fix weak delivery practices - it magnifies the system it enters.
What do I do with it?
As usual. the right response is not panic or is it blind enthusiasm. The practical path is to understand where your organisation is today, then move deliberately. A few practical next steps:
Assess your current maturity. Are your teams doing conventional development, AI-assisted development, AI-integrated development, agentic development, or early orchestrated agentic engineering? Different levels require different controls, skills, and expectations.
Separate experimentation from production use. It is reasonable to let teams explore. It is risky to let experimental workflows quietly become production delivery methods without review, standards, or governance.
Start with bounded use cases. Good early candidates include code explanation, test generation, documentation updates, refactoring support, internal scripts, proof-of-concept builds, and controlled backlog items.
Define what must remain human-controlled. This may include architecture decisions, security-sensitive changes, production releases, data model changes, customer-facing logic, and anything with regulatory or operational consequences.
Update your development standards. AI-generated code should still meet your standards for readability, maintainability, security, testing, documentation, and review. “The AI wrote it” is not a quality argument.
Invest in review capability. The ability to review AI-generated work may become one of the most important skills in the team. This includes code review, design review, security review, and business logic validation.
Look at your toolchain honestly. Agentic development works best when repositories, documentation, tickets, tests, environments, and deployment processes are reasonably well structured. Poor foundations limit the value.
Create simple guardrails. Teams need clarity on approved tools, acceptable data use, review expectations, repository access, security boundaries, and when human approval is required.
Train managers as well as developers. Business and delivery leaders need to understand what has changed. Otherwise, they may either overestimate what the tools can do or miss the opportunity entirely.
Treat this as a capability journey. The goal is not to jump straight to multi-agent engineering. The goal is to build confidence, learn where value appears, manage risk, and develop organisational muscle.
One simple mental model is useful: the more autonomy you give the system, the more maturity you need around context, constraints, review, and accountability. That applies whether you are using one AI assistant in an IDE or orchestrating multiple agents across a workflow.
The evolution of software development in the agentic era is not just about faster coding, it is about a gradual shift in how work is structured, who does what, and where human judgement is applied. For business leaders, the real question is not “Will AI write code?” It already does, the better question is: “Do we have the practices, skills, governance, and leadership needed to use this well?”
That is where the opportunity sits. Not in replacing software teams, but in helping them work at a higher level, with better leverage, clearer guardrails, and a more thoughtful path from idea to working system.