Evolving software delivery for agentic AI

Modern software delivery has been shaped by a simple assumption—that you control the systems you ship. You define requirements, write logic, test against expected outcomes, and deploy predictable services. Even Agile and DevOps approaches still rely on the principle that each sprint delivers something deterministic, verifiable, and largely within human oversight.

Agentic AI upends that foundation. Agentic systems interpret, reason, and adapt rather than follow scripts. Their behavior depends on the code you write, the context they operate in, the inputs they're given, the tools they can access, and the goals they're assigned. In short, they don't follow orders; they pursue outcomes.

This makes delivery less about control and more about alignment. Rather than providing instructions, you must shape how it behaves. This means that the traditional software development lifecycle (SDLC) no longer fits because it was designed for logic-based, human-controlled systems.

Zones of intent for agentic AI

Instead of rigid stages, such as define, build, test, and release, we need a model that embraces autonomy, uncertainty, and emergence. Instead, you use zones of intent. A zone of intent defines a bounded space where an agent can operate with autonomy, within constraints. The goal is to shift from micromanaging every task to designing environments where agents can act, learn, and collaborate safely. You specify the what (the desired outcome), the why (the intent), and the guardrails (the constraints, policies, and trust boundaries). Given those boundaries and this information, the agent figures out the how.

Instead of an assembly line, think of the environment as an airspace. You control who can enter, what they can do, and where they can go. But once inside, they're free to navigate as needed. That's how agentic systems scale without chaos.

This isn't just a philosophical shift; it's a practical one. The non-deterministic output of agent-based systems can't be fully tested through unit tests. It can't be versioned like static binaries. Agents change over time, adapt to new data, and interact with other systems in unpredictable ways. Trying to deliver them using traditional models leads to fragile, unscalable architectures. At worst, it leads to false confidence in systems that you can't actually govern.

When teams embrace intent-based delivery, they gain two advantages:

• Control where it matters most – They define boundaries instead of outputs.

• Scalability through delegation – They enable agents to handle complexity humans can't hardcode.

This is how you move from isolated prototypes to real, production-grade agentic systems that can repeatedly and reliably deliver value.

Evolving the delivery lifecycle for agentic AI

To support intelligent, adaptive behavior, the SDLC must be reframed from deterministic control to adaptive intent. The following are the changes necessary to evolve the traditional SDLC for agentic AI:

• Planning becomes intent design. Teams define goals, constraints, and expected agent behaviors. Policies and success criteria are framed in terms of alignment, not logic.

• Architecture becomes scaffolding. Teams focus on defining roles, interfaces, guardrails, fallback mechanisms, and observability rather than scripting every decision path.

• Testing becomes behavioral evaluation. Rather than asserting specific outputs, teams validate whether agents stay within acceptable bounds and fulfill intent under varied inputs.

• Deployment becomes continuous orchestration. Agentic systems are deployed with runtime controls, live monitoring, and feedback channels that enable real-time tuning.

• Iteration becomes feedback and adaptation. Instead of traditional code-change patch cycles, teams observe how agents evolve, where they succeed, or when they drift. As necessary, teams intervene through updated constraints, retraining, and adding or modifying control mechanisms.

Existing practices that focus on iteration, experimentation, and rapid feedback are halfway there. The shift to agentic systems isn't a rejection of Agile principles. In fact, it's a natural evolution of them. Agile thinking emphasizes adaptability, feedback, and working solutions over rigid plans. That aligns perfectly with the nature of agentic systems, which learn, adapt, and respond to context in real time. If you're already running short cycles, validating assumptions quickly, and managing uncertainty through continuous delivery, you're well equipped to lead this transition.

But there are key differences. The traditional Agile approach assumes that the thing being delivered is deterministic. It assumes that, once built, the thing will behave consistently and predictably, with repeatable outcomes for the same inputs. This repeatability helps you debug, test, and iterate with confidence. Agentic systems break that model. They're probabilistic, context-sensitive, and capable of evolving independently. That means some Agile practices become less useful, such as velocity tracking based on story completion, strict acceptance criteria, or deterministic sprint planning.

The following aspects of the traditional SDLC apply to agentic AI:

• Iterative development and delivery

• Customer feedback as a primary signal

• Cross-functional collaboration

• Continuous integration and deployment

The following aspects of the traditional SDLC must evolve for agentic AI:

• Redefine done as aligned to intent. Focus on whether the agent's behavior satisfies its intended goal within the defined constraints.

• Shift from acceptance criteria to behavioral guardrails.

• Expand the definition of done to include runtime readiness, which includes observability, explainability, and feedback mechanisms that support continuous learning and trust.

• Prioritize real-time feedback loops and behavior tracking over upfront planning

The good news is you don't need to throw out the SDLC playbook. You just need to evolve it from managing code to shaping conduct. In agentic systems, success isn't just about whether software runs, but how it behaves.

Preparing teams for agentic AI

Software engineering isn't going away. It's evolving. The job shifts from writing functions to shaping frameworks and control mechanisms for intelligent behavior. In the world of agentic AI, building is no longer the hard part—managing emergence is. For most engineering teams, the evolution feels like a mindset shift rather than a technical leap. Instead of asking "What will the system do?" the question becomes "What have we empowered it to pursue, and how will we know if it's staying on course?"

For engineering teams, the evolution toward agent AI requires the following changes:

• A cultural shift – Teams must become comfortable with uncertainty and autonomy in systems they don't fully control.

• New roles – Intent designers, behavioral testers, and observability engineers become core to delivery.

• Shared language – Teams need a clear, shared understanding of goals, guardrails, and success signals, just like how they once needed specs and test cases.

As generative AI matures, we'll see more agentic systems interacting with customers, products, and operations. The organizations that succeed won't be the ones with the best models. It will be the ones that can integrate agents into real-world workflows with confidence, control, and velocity. That means that the delivery models and engineering teams must evolve together. Zones of intent give you the abstraction to do that. They help you operationalize autonomy without surrendering accountability. They also offer a shared framework across teams to help govern systems that can't be hard-coded.

For more information about preparing teams for agentic AI, see the Preparing the business for agentic AI at scale section of this strategy.