What is agentic development? It is a software delivery approach in which AI agents use context, tools, and permissions to complete multistep engineering work under human oversight.
Agentic development is also changing how teams organize software delivery.
In the traditional software development lifecycle (SDLC), people coordinate work across planning, implementation, testing, and release.
In the agentic development lifecycle (ADLC), agents can carry approved work across those stages, respond to feedback, and escalate decisions that require human judgment.
The familiar delivery concerns remain, including quality, security, accountability, and release readiness. What changes is the speed of the work, who coordinates each step, and where teams need controls.
As agents gain broader responsibilities, teams need clear permission boundaries, approval paths, independent verification, and traceable evidence of releases. Building those controls into the ADLC helps teams increase development speed without losing oversight.
The SDLC organizes software delivery around stages such as planning, implementation, testing, release, and maintenance. Work typically moves between people, teams, and automated systems through defined handoffs.
The ADLC changes that operating model. Agents can interpret a goal, inspect relevant context, use approved tools, make changes, run tests, respond to results, and prepare work for review within a continuous workflow.
People remain responsible for setting goals, defining permissions, establishing quality standards, and approving consequential decisions. The distinction lies in how the work is carried out: agents coordinate more of the operational sequence, while people govern the system and apply judgment at defined checkpoints.
A typical ADLC workflow may include these steps:
Agentic development examples can be as focused as adding a new checkout field.
The agent reads the ticket, finds related components, updates validation, and runs the appropriate tests. An API test fails because the service rejects the new field.
The agent reviews the failure, adjusts the request, and reruns the tests. It then prepares a pull request with the change summary and supporting evidence.
A person reviews the higher-risk decisions before approval.
Your team sets the goals, permissions, quality standards, and approval points guiding each workflow. The agent handles the approved sequence and escalates uncertainty.
Task-level AI coding assistance usually supports a focused request. It may complete code, generate a function, explain an error, or answer a question.
Agentic development coordinates broader workflows. Agents can plan work, use tools, maintain context, run checks, and revise their approach.
|
Capability |
Task-Level AI Coding Assistance |
Agentic Development |
|
Scope |
Supports a prompt or coding task |
Coordinates multi-step engineering work |
|
Context |
Often centers on a file, prompt, or chat |
Uses codebase, application, and workflow context |
|
Tool use |
Suggests commands or changes |
Calls approved tools and reads their results |
|
Execution |
Produces code or guidance |
Can edit files, run checks, and prepare pull requests |
|
Feedback |
Responds within the current request |
Uses results to continue or revise the workflow |
|
Human role |
Reviews output and chooses the next step |
Sets goals, boundaries, checkpoints, and approvals |
The categories can overlap. A coding assistant may offer agentic features when it receives tools, context, and authority to continue working.
As agents gain wider responsibility, teams must define ownership for permissions, review, testing, and release decisions.
The “risks” of agentic development increase as agents receive wider access and greater responsibility. Clear limits and review paths help teams control that risk.
Common concerns include:
A coding agent may also help generate the tests used to assess its work. That creates a structural trust problem. The author cannot be the only verifier.
Governance and testing protect different parts of the workflow.
Governance defines what an agent can access, change, execute, or approve.
Testing verifies whether the resulting software behaves correctly.
Independent verification provides a quality signal outside the coding workflow.
As agents take on more work, these controls need to scale with them.
A pull request may look complete while a user journey fails elsewhere. Local checks may pass while an API contract or mobile workflow breaks.
Agentic testing helps teams verify more change without making the coding agent responsible for grading its own work. An agentic testing platform can provide coverage across web, mobile, APIs, and AI-powered applications.
mabl provides independent verification through two differentiators that work together. Active Coverage brings test creation, execution, failure analysis, and recovery together in a single integrated platform, with shared context throughout the testing workflow.
Deep Quality Context combines accumulated knowledge of an application’s business logic, user workflows, and failure history with quality-specific instructions the team controls. That context carries into every run, helping mabl evaluate changes against application behavior and team standards rather than the current file or pull request alone.
People remain responsible for quality strategy, risk tolerance, approvals, and release decisions.
Integrating agentic software testing into the workflow gives teams reliable evidence before merge or release.
Agentic development depends on several technologies working together around a defined workflow:
A useful technology stack connects reasoning, approved actions, reliable context, traceability, and verification around each agent workflow.
Choose each layer according to the work agents will perform and the consequences of failure.
Start with a narrow workflow where the expected result and potential impact are easy to evaluate.
Choose an agent harness that matches the job’s complexity and risk. The harness determines which tools the agent can use, when it must request approval, and how its work is tested and recorded.
Use this checklist to configure the workflow:
Define the task boundary: Choose work with clear inputs, acceptance criteria, and manageable consequences.
Set approval paths: Require human review for sensitive code, data access, permissions, and release actions.
Limit tool access: Give agents only the systems and commands required for each assignment.
Track agent activity: Record tool calls, code changes, failures, test results, and approvals.
Add independent testing: Verify product behavior before expanding the agent’s scope or release authority.
Review quality signals: Monitor failure rates, coverage gaps, recovery actions, and repeated blind spots.
Expand gradually: Add workflows or permissions after results show reliable testing and clear audit trails.
The result is a testing workflow that produces clearer quality signals.
Teams can see what changed, where failures appeared, and what needs review before release. When those signals appear in the tools developers already use, teams can move faster from failure detection to root cause investigation.
Agentic development can shorten the path from a ticket to a reviewed pull request. It can also increase the volume and pace of machine-generated changes.
Your testing and governance processes need to keep pace with that change. Independent verification gives teams evidence from outside the workflow that produced the code.
mabl brings this verification into software delivery through Active Coverage, guided by Deep Quality Context.
Active Coverage provides coverage that builds, runs, and fixes itself. Your team defines quality standards, reviews important changes, and controls release decisions.
This approach helps engineering and quality teams move faster with a shared view of product behavior and release risk.
See how mabl supports agentic testing for software development with a free 14-day trial.