The Agentic Delivery Lifecycle (ADLC) is a framework that describes how software work moves from an intended outcome to a delivered result when people and AI agents share the work.
It includes more than code generation: teams must define what they want to achieve, guide and review agent work, make decisions about risk, and learn from what happens after release.
The term is still taking shape. Some organizations use “ADLC” to mean Agentic Delivery Lifecycle; others use it for Agentic Development Lifecycle.
There is no single agreed framework or set of stages. In this article, ADLC refers to the broader delivery process: how an organization directs, executes, reviews, and learns from software work involving AI agents. This distinction is important.
An AI coding assistant can speed up implementation without changing how a team sets priorities, clarifies requirements, reviews work, or decides whether a release is ready. ADLC is useful as a way to examine those wider changes—not as a prescribed methodology every team must adopt.
The delivery process has barely changed in twenty years — until now
Through 2025 and into 2026, established engineering organizations started publishing their own models for it: IBM, EPAM, Arthur.ai, Cognizant, Salesforce, and Cloudflare have all put out framings, alongside independent practitioners like Asaf Saar and Casey West. The volume of activity is the signal. The lack of agreement on what any of it means is the catch.
To see why the moment feels different, it helps to remember how little the underlying process has moved.
For two decades, the Software Development Lifecycle ran the same way: requirements, then specifications, then code, then testing — each phase waiting on the clarity of the one before it.
Waterfall and Agile changed the cadence of that sequence. Waterfall ran the phases once, in a long line. Agile ran them in two-week loops. But the phases themselves, and the fact that a human had to hand-produce the artifact at each one, never fundamentally changed. Agile made the loop shorter; it didn’t remove the manual work inside it.
Agentic delivery aims at the manual work itself. Instead of a person authoring every spec, story, and test from a blank page, an agent drafts them and a person directs and approves. That’s the shift people are reaching for a name for. Right now, the name they’ve landed on is a mess.
Why the delivery constraint may be moving
Imagine this.
A senior business analyst spends six weeks turning interviews with the claims team into a detailed specification for a claims-processing feature. Down the hall, a colleague feeds the same source documents to an AI agent, points it at the team’s process standards, and reviews a first-draft specification the next morning.
That gap — six weeks versus one review cycle — is why “agentic delivery” has gone from a fringe idea to a crowded conversation in under a year.
One hypothesis is that as implementation gets faster, two other parts of delivery become more visible constraints: specifying the work clearly enough for an agent to act on, and reviewing the output carefully enough to trust it.
This is not true of every task or team, and it does not mean implementation has stopped being difficult. The amount of work that can be delegated reliably varies. An agent may produce a first draft quickly, but people still need to supply context, check assumptions, test the result, and decide whether it meets the real need. When those steps are not planned for, faster output can create more work downstream.
The practical question is therefore not only, “How much can an agent build?” It is also, “Can the team define the work, evaluate the output, and make decisions at the pace the new workflow requires?”
What the emerging models actually agree on
Strip away the competing acronyms and the various manifestos, and a common shape does emerge across most of these framings. It’s less a fixed methodology than a division of labor:
Agents execute the repeatable work. People govern intent, priorities, and risk. Delivery signals feed the next cycle.
That’s the through-line worth taking seriously — not any one group’s six-box diagram. It positions agentic delivery as a hybrid model, which is the part most worth understanding.
It is not SDLC with AI tools bolted on. Adding a code copilot to an unchanged process speeds up typing, not delivery. The interesting claim is that the shape of the work changes: fewer sequential handoffs, more parallel draft-and-review.
It is also not “hand everything to autonomous agents.” The models that hold up all keep humans firmly in control of intent, exceptions, and accountability. The goal isn’t to remove people from delivery; it’s to move their attention up a level — from producing every artifact to deciding what the artifacts should achieve and whether the results are right.
Between those two poles — unchanged process on one side, full autonomy on the other — is where the emerging practice actually sits.
SDLC and ADLC: what may change?
ADLC does not make the Software Development Lifecycle or Agile obsolete. Teams can use agents within their existing methods. The shift is in how work is produced and where people may need to focus their attention.
| Area | SDLC | ADLC |
|---|---|---|
| Intent and requirements | People define the need and create specifications, often refining them through discussion during implementation. | People remain accountable for the intended outcome and constraints. Agents may help analyze source material or draft requirements, which people review and refine. |
| Implementation | Developers produce most implementation artifacts directly. | Agents may generate or modify some artifacts. The degree of delegation depends on the task, context, and risk. |
| Verification | Testing and review may be concentrated later in the flow, even when teams work iteratively. | Checks can happen alongside generation. People still need to assess correctness, security, fit, and the assumptions behind the output. |
| Team attention | Much effort goes into producing and coordinating work. | Some effort may shift toward clarifying intent, reviewing output, resolving exceptions, and managing dependencies. |
| Learning | Teams use feedback and delivery results to inform future work. | The same feedback loop remains important. Agents may help summarize signals, while people decide what those signals mean and what to change. |
The key idea is:
“Agents execute. Humans govern. Delivery continuously learns.”
With that, let’s look in detail on the stages of the agentic delivery lifecycle.
How does ADLC work? And how does NimbleWork support it?
Agentic delivery is not a fixed sequence. Teams define an outcome, prepare the context and boundaries for the work, use agents where they can help, and review the results. Some activities overlap: for example, teams can check generated work as it is created rather than waiting until the end.
NimbleWork’s AI agents can assist with parts of this delivery work inside the work management environment. They can help teams turn documents and work-item context into structured plans and artifacts, and help people find or summarize information.
People still set priorities, review outputs, manage risk, and make consequential decisions.
1. Discover the opportunity
It’s about discovering and defining the opportunity.
Teams consider the business outcome, strategic fit, dependencies, capacity, and constraints before deciding how to proceed.
NimbleWork’s Intelligent Planning can read documents such as an RFP, statement of work, contract, or requirements specification and generate a structured draft plan. The team can review and refine the proposed work. The agent can help organize documented scope; people remain responsible for deciding whether the initiative is strategically valuable and should be prioritized.
2. Design the intent
People define the problem, scope, success measures, relevant context, and constraints. They also decide which actions an agent may take and which decisions require human approval.
NimbleWork’s Elaborator can draft a work-item description from its title and available context.
Smart-Acceptance can suggest acceptance criteria. These can give a team a more complete starting point, but the people who own the work need to check that the draft reflects the intended outcome and the relevant business context.
3. Prepare the agent to work
Before using an agent, teams need to make relevant context available and consider permissions, data boundaries, and required checks. The setup will depend on the tools and risk level involved.
NimbleWork’s agents use context available in and around a work item to generate drafts. NimbleWork can help teams organize that work and its details in the delivery workflow. This does not mean NimbleWork configures or authorizes every external coding agent an organization may use; agent access and technical controls need to be managed through the appropriate systems and policies.
4. Carry out the work
Agents may help produce or advance parts of the work. Within NimbleWork, Smart-Task can suggest To-Dos based on a card’s details, while Nimble Buddy can help users find information or carry out supported actions in the application.
These capabilities help with work-item details and workflow activity. They should not be described as agents that build, commit, or deploy software. The team decides where other tools, such as coding agents, fit into its delivery process.
5. Review and govern the output
Generated work needs to be checked against the intended outcome, requirements, and relevant quality or policy controls. Review can happen throughout the work, not only at a final approval point.
NimbleWork’s Smart-BDD can draft behavior-driven development scenarios, and Test-Gen can draft test cases. Summarizer can condense work-item discussions to help people catch up on decisions and next steps.
These agents can support preparation and review; their output is not proof that code is correct, secure, or ready for release. People and the organization’s testing and security controls remain responsible for those checks.
6. Deploy and learn
Verified changes move into use through the organization’s release process. Teams then review delivery information and real outcomes to decide what to adjust in future work.
NimbleWork can help teams track delivery and interpret project information. Its Executive Summary agent analyzes dashboard data and produces a written summary of metrics, patterns, and deviations.
That can help people see where attention may be needed; it does not replace a deployment platform or make release decisions on their behalf.
Across these activities, NimbleWork’s role is to help teams organize and manage delivery work, and to use AI for selected planning, documentation, and review tasks. The agents provide drafts and summaries within the workflow. People provide direction, check the results, and remain accountable for decisions.
Nimble’s AI Agents can help generate descriptions, acceptance criteria, To-Dos, BDD scenarios, comment summaries and test cases within the normal workitem flow. Explore Nimble’s AI Agents
NimbleWork’s role across ADLC is to connect intent, agent-generated work, human governance and delivery intelligence in one managed environment.
The goal is not to accept every generated artifact automatically. It is to reduce blank-page work while preserving human review where context and judgment matter.
Final thoughts
The shift from SDLC to ADLC moves the work people do up a level — from writing every spec, story, and test to setting intent and deciding what the delivery signals mean. Agents draft; humans direct.
The change is structural. Development and QA stop running in sequence and become a parallel Create/Verify band. Governance becomes a continuous layer, not an afterthought. And a Learn stage — the one thing SDLC never had — turns real outcomes back into sharper intent for the next cycle.
No tool installs this for you. ADLC rewards teams that define their process clearly enough for agents to work inside it. That’s the model NimbleWork is built around: agents execute, humans govern, delivery keeps learning.
FAQs on ADLC
The Agentic Delivery Lifecycle is an emerging way to describe software delivery in which people and AI agents share work. It covers the path from defining an outcome through generating and reviewing work, releasing it, and learning from the result. The term does not yet refer to one standardized framework.
ADLC draws attention to how AI agents may participate across delivery—not only in coding, but also in planning, documentation, testing, and coordination. People remain responsible for intent, judgment, risk, and approval. Teams may incorporate agent-supported practices into an existing SDLC or Agile approach; ADLC does not require replacing either one.
No. Agents can assist with some implementation and testing tasks, but people still need to understand the problem, challenge assumptions, evaluate quality, handle exceptions, and take responsibility for consequential decisions. As agent use grows, some roles may spend more time directing and reviewing work, but the changes will depend on the organization and type of work.
Common challenges include unclear or incomplete requirements, limited context available to agents, output that is difficult to review, review queues that grow as generation speeds up, and uncertainty about who can approve or release agent-produced work. Teams also need to consider data access, security, compliance, and how agent activity fits into existing tools and controls.
A sensible starting point is a bounded use case with clear acceptance conditions, an identified reviewer, and a way to learn from errors and rework. Teams can expand the use of agents as they build confidence in the workflow.
There is no reliable universal timeline or ROI figure. Adoption depends on the work being automated, the quality of available context, existing review and release practices, and the organization’s risk requirements. A pilot can help a team measure its own results—for example, review effort, rework, cycle time, defects, and time spent preparing or updating delivery information—before making broader claims.
The term is most commonly used in discussions about software delivery. Some of its ideas—setting clear intent, delegating bounded work to agents, reviewing outputs, and learning from results—may also apply elsewhere. Whether “ADLC” is the right label depends on the organization’s context.
NimbleWork helps teams organize and manage delivery work, maintain visibility across initiatives, and use AI assistance for selected work-item activities. Its role is to help people coordinate and oversee delivery. It does not replace coding agents, testing infrastructure, deployment systems, or enterprise AI governance platforms.