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Agentic SDLC is an emerging way to organize software work in which AI agents take bounded goals, plan steps, use tools, and iterate on tasks such as editing code or running tests. It shifts some work from asking AI for suggestions to delegating multi-step tasks—while people still set constraints, review results, and control important decisions. It is an editorial umbrella, not a formally standardized lifecycle model or a reason to discard established engineering practices.
What makes a software workflow agentic?
An AI coding assistant may respond to a prompt with a code suggestion. A more agentic system can take a goal, inspect a repository, plan actions, edit files, run commands, inspect the results, and revise its work. The distinguishing feature is that feedback loop: the system can act through tools and react to what happens, rather than waiting for a person to direct every individual step.
Google Cloud defines agentic coding as “a software development approach where autonomous AI agents plan, write, test, and modify code with minimal human intervention.” The level of autonomy is not fixed by the label. It depends on what tools an agent can access, which actions it is allowed to take, and where people must approve or intervene.
In this article, agentic SDLC means using agents in one or more parts of the software development lifecycle. It does not mean that a team has adopted a new formal SDLC standard, automated every stage, or removed the need for accountable engineering decisions.
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How does agentic SDLC differ from Waterfall?
Waterfall is a useful contrast because it organizes work around planned stages and handoffs: requirements and design precede implementation, which precedes testing and release. Agent-mediated work can instead concentrate on a bounded task that moves through action and feedback—for example, changing code, running a check, and responding to its output. These approaches are not mutually exclusive: a team can retain staged planning and approval while using agents to carry out selected tasks iteratively.
| Dimension | Stage-oriented Waterfall | Agent-mediated workflow |
|---|---|---|
| Work unit | A phase or handoff | A bounded task and its feedback loop |
| Execution | People carry out planned work and pass it to the next stage | An agent may plan steps, use tools, change files, and react to check results |
| Feedback | Often concentrated at formal reviews and testing stages | Can occur during a task if the agent can run checks and inspect their output |
| Human role | Define requirements, design, implement, verify, and approve | Set goals and permissions; review work; handle exceptions and approve releases |
| Risk to manage | Problems may surface late at a handoff or test stage | Incorrect, insecure, or unauthorized actions may propagate quickly |
This is an explanatory comparison, not a claim that all Waterfall teams work identically or that every agent has end-to-end autonomy. Iteration can shorten the distance between an action and feedback, but it does not make planning, architecture, verification, or release controls unnecessary. NIST’s DevSecOps model includes security, automated build and test, packaging, distribution, release, and deployment management across the lifecycle.
Where can agents participate in the lifecycle?
NIST’s DevSecOps Practices documentation identifies code generation, testing, vulnerability remediation, documentation, and workflow orchestration as possible agent-assisted activities. Google Cloud also describes uses such as greenfield scaffolding and prototypes, and brownfield refactoring, test generation, and documentation. These are examples of possible workflows, not guarantees of quality or capabilities every system provides.
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Planning and requirements
An agent can help organize context or turn a task into proposed steps. Product and engineering owners remain responsible for deciding the intended behavior, priorities, and constraints.
Design and architecture
Agents may assist with analysis and documentation. People need to own decisions with material security, reliability, or business consequences.
Implementation
Within its granted access, an agent may inspect a codebase, edit one or more files, or update dependencies. The scope of the change should match the task and the permissions granted.
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Testing and assurance
An agent can generate tests or run existing ones. A passing result is evidence only for the behaviors those tests cover, so teams still need appropriate deterministic checks and security review.
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Release authority should be governed separately from the ability to edit code. Google Cloud’s guidance recommends preventing agents from pushing changes straight to a live production environment.
Maintenance
Agents may assist with upgrades, bug investigation, remediation, documentation, and repeated checks. Keeping records of agent actions and human decisions supports later review.
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How should a team govern agentic development?
NIST says teams should monitor and validate AI-generated content through humans and use verifiable processes to check accuracy and trustworthiness. For agent actions and outputs, its guidance also emphasizes governance, authorization controls, auditability, and human oversight. Those controls matter because an agent can act through its tools, not merely produce text for a person to consider.
Google Cloud’s agentic coding guidance recommends defined scope and guardrails, restricting dependency installation to trusted sources, recording actions and decisions, ordinary pull-request review, activity tracking, monitoring for prompt injection and faulty code paths, red-team scenarios, and layered static and dynamic application security testing (SAST and DAST).
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Practical control checklist
- Begin with narrow, reversible tasks and limit the repository or workspace in scope.
- Grant only the file, terminal, network, and service access required for the task.
- Keep secrets and production credentials out of agent context unless a controlled need is explicit.
- Require human review before merging, and keep merge approval separate from the agent’s ability to edit.
- Run deterministic tests, dependency checks, and security scanners in the normal delivery pipeline.
- Log inputs, actions, tool calls, outputs, and approvals to support investigation.
- Treat external text and repository content as possible prompt-injection vectors.
- Measure effects on delivery and quality at the team level rather than relying on task-completion speed alone.
NIST’s September 24, 2026 project update says its DevSecOps project is scoping a demonstration in which agentic AI develops, builds, and tests code, alongside work to demonstrate agent identification, authentication, and authorization within the SDLC. That update describes planned project work, not a completed standard or finalized NIST agentic-SDLC framework. NIST SP 800-218A, published in July 2024, is an AI-related profile of the Secure Software Development Framework (SSDF) and relates to SSDF 1.1; it is a relevant secure-development reference, not a specific standard for autonomous SDLCs.
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What evidence supports claims about speed or quality?
There is no broad independent quantitative evaluation in the cited material establishing that autonomous agent workflows universally improve productivity, software quality, or delivery performance. AI-assisted development findings should not be treated as proof about fully agentic, end-to-end automation.
Google Cloud’s September 18, 2026 account of its own infrastructure security work reports that it prevents “hundreds of vulnerabilities per month” through continuous scanning of code changes. It also reports false-positive rates of “3%” in some cases for a localized threat-model scanning approach, and says a specialized internal triage agent achieves “over 92% precision” and completes work in less than a minute. These are company-reported results for specific internal systems and tasks, not independent or cross-industry benchmarks.
The landing page for Google Cloud DORA’s 2025 State of AI-Assisted Software Development report describes a seven-practice AI capabilities model and frames adoption as a systems problem. The inspected page does not provide a numeric effect estimate, so it does not support a general productivity percentage.
How can you evaluate an agentic workflow?
Evaluate the workflow against the risks and outcomes that matter to your team, comparing it with a clear baseline. A task that finishes sooner may still add review burden, rework, defects, or security exposure. Track those effects alongside delivery time rather than treating speed as the only result.
- Access: What repository, terminal, network, secrets, and deployment permissions does the agent have?
- Evidence: Can it explain its plan and provide reviewable artifacts, logs, and test results?
- Approval gates: Which people must approve merges, dependency changes, security findings, and production actions?
- Integration: Does it fit the team’s version control, CI/CD, identity, and security tooling?
- Context safety: How does it handle untrusted repository content and possible prompt injection?
- Measured outcomes: What happens to delivery, defects, rework, review effort, and security compared with the team’s baseline?
These criteria help assess an operating model or tool without assuming one vendor or implementation is universally best. The central choice is not whether to automate everything, but which bounded work is suitable for delegation and what evidence and approvals are required before its results affect the product.
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