For years, AI in software development has mostly meant asking a machine to help write the code. That is beginning to change.

AI coding assistants respond to instructions. AI agents can take a goal and work through the steps needed to achieve it, using code, tools, tests and feedback along the way. This is the shift from AI-assisted development to agentic software development.

The difference may sound subtle, but it changes the question software teams need to ask. It is no longer simply how AI can help developers write software faster, but how software development itself changes when part of the work can be planned and executed by an agent.

The Shift from AI-Assisted Development to Agentic Development

The difference becomes clearer when we look at who controls the sequence of work.

In AI-assisted development, a developer remains responsible for breaking work into individual tasks. An AI tool can generate code, suggest an implementation, explain an error or write a test, but the developer decides what happens next.

Agentic development changes that dynamic. Given a defined objective and constraints, an AI agent can plan a sequence of actions, use development tools, execute changes, evaluate the results and iterate without requiring a new instruction at every step.

The important change, then, is not simply greater automation. It is a shift from instruction-driven development to goal-oriented execution. The developer specifies what needs to be achieved and the boundaries within which the agent can work; the agent handles more of the path between the two.

The traditional development stages remain, but the way work moves through them begins to change.

AI Agents Across the Software Development Lifecycle

That change becomes more visible across the software development lifecycle. The stages remain familiar; what changes is how work moves between them.

The more significant change is how these stages connect. In a conventional workflow, a test failure may become a new task for a developer to investigate. An agent can potentially carry the context forward: inspect the failure, identify the relevant code, make a change, rerun the test and continue iterating.

This creates a more continuous development loop rather than a series of isolated handoffs. The agent can act on the output of one stage as it moves into the next, while human involvement remains important at points that require judgement, architectural decisions or approval.

That makes the agentic SDLC less about replacing individual development activities and more about connecting them into an executable workflow. The next question is therefore not simply what agents can do, but how the developer’s role changes when more of that workflow can be delegated.

The Changing Role of Software Developers

When an agent can take a feature from a written requirement through implementation and testing, the developer no longer has to manage every step of that process manually. The work shifts toward setting the direction and checking the result.

That means developers increasingly need to be good at:

• Defining objectives and acceptance criteria
• Setting architectural, access and operational constraints
• Reviewing agent-generated implementation and test results
• Handling exceptions and assessing security, performance and other risks

When more implementation can be delegated, the quality of the specification and the strength of the review process matter more.

That leads naturally to the next problem. If agents are going to make changes with less step-by-step instruction, the software they work with needs to be structured in ways an agent can reliably understand and use.

Software Architecture for AI Agents

The developer’s role is only part of the change. If an agent is expected to work with an application rather than simply generate code for it, the application has to expose enough structure for the agent to understand what it can do and how it should do it.

That has several practical implications for software architecture:

• APIs and tools need clearly defined capabilities that an agent can discover and invoke.
• Structured data gives agents predictable inputs, outputs and schemas.
• Business logic should keep important rules deterministic and enforceable by the application.
• Permissions need to limit which data and actions an agent can access.
• Documentation, contracts and observability need to give agents and developers a reliable picture of available capabilities, constraints and actions taken.

Together, these characteristics form the foundation of agent-ready software: software whose capabilities, data and rules can be reliably understood and used by AI agents within defined boundaries.

Making an existing application accessible to an AI model does not automatically make it agent-ready. An agent needs reliable ways to discover capabilities, take permitted actions and interpret the results.

APIs, Tools and Protocols as the Agent Interface

Once software exposes capabilities in a form an agent can understand, the next question is how the agent actually uses them. APIs, tools and protocols provide that interaction layer.

APIs expose application capabilities; tool or function calling gives an agent a structured way to invoke them. Protocols such as MCP can standardise how tools and resources are exposed, while approaches such as A2A address communication between agents.

Consider a customer requesting a revised quote. An agent might retrieve account information from a CRM, check pricing data, apply the relevant rules and submit the change for approval. The agent is not replacing those systems. It is coordinating actions across them.

This is part of a broader shift in which agents increasingly become an interface between users and the software they rely on. We explore that shift in our blog: Why Are AI Agents Becoming the New User Interface for Software?

That distinction matters. An AI model provides the reasoning; the connected software provides the capabilities and enforces the rules. The reliability of an agent therefore depends partly on whether those underlying capabilities are well-defined, accessible and appropriately controlled.

Testing and Quality Assurance for Agentic Software

Once an agent can choose tools and take multiple actions, testing has to look beyond whether the underlying software works.

A conventional test might ask: Did the application return the expected result? 

An agentic system also needs to answer: Did the agent take the right actions to produce that result?

That means evaluating tool selection, inputs, permissions, business rules, error handling and the final outcome. 

This creates an important distinction between code correctness and agent behaviour correctness. An API can function exactly as intended while an agent uses it incorrectly.

Functional and regression testing therefore remain necessary, but they are supplemented by scenario-based evaluation and workflow testing. Traces can show how an agent reached a result, while human review remains appropriate for consequential decisions.

The result is a broader definition of software quality: an agentic system needs to produce the right result and behave within the boundaries it was given.

Security, Governance and Human Oversight

Giving an agent access to a system does not mean giving it unrestricted authority. Its identity, permissions and available actions need to be defined by the software around it.

For example, an agent might be allowed to retrieve a customer’s account details but not change them, issue a refund or approve a high-value transaction. Those boundaries can be enforced through least-privilege access, approval thresholds and audit trails, rather than relying on the model to decide what it should or should not do.

The controls should answer five practical questions: 

• What the agent can access
• What it can change
• Which actions require approval
• Whether those actions can be reversed
• Whether they can be audited

Human oversight can therefore be targeted rather than continuous, with approval reserved for actions where an error carries meaningful consequences. 

This becomes harder when the agent has to work with legacy systems that were never designed for controlled, automated access.

AI Agents and Legacy Software Systems

The systems behind an agent can become the real constraint. Many enterprise applications still rely on limited APIs, undocumented business rules, fragmented data or workflows designed entirely around human interaction.

An agent does not remove those limitations. It can make them more visible and more consequential, particularly when it needs to retrieve information, execute actions or recover from failures without constant human intervention.

The answer is not always replacement. Existing systems can often be made more usable by exposing reliable interfaces, documenting critical rules, strengthening access controls and improving observability.

The important question is therefore not whether an agent can connect to a legacy application, but whether that application can support reliable and controlled interaction at the level of autonomy required.

The Emerging Agentic Software Development Model

Whether an existing system can support agentic software ultimately comes down to how much responsibility can safely be delegated to the agent and what remains under human and system control.

In this model:

• People define intent, architecture, constraints and approval boundaries.
• Agents handle more of the planning, implementation, testing, debugging and iteration.
• Software systems enforce business rules, permissions and data access.

This creates a model that is neither human-built nor agent-built. A more accurate description is human-directed, agent-executed and system-verified software development.

In Conclusion,

AI agents are changing software development by making more of the development process executable rather than manually coordinated.

The deeper shift is that these actions can increasingly happen within a software environment that defines what the agent can access, what it can change and how its work is verified. 

For engineering teams, that makes the real question less about how much code an agent can produce and more about how much of the development process can be safely delegated.

Agentic software development works when the three parts are designed to work together: human judgement sets the direction, agents carry out more of the work, and the software provides the boundaries and checks.