Humans define the consequential objective.
The organization still needs an accountable source of intent, scope, constraints, and decision rights. Delegating implementation does not delegate unlimited authority.
AGENTIC SOFTWARE DEVELOPMENT LIFECYCLE
Agentic SDLC: the lifecycle changes when agents can act.
An agentic software development lifecycle is a software-delivery lifecycle in which AI agents can perform multi-step work across planning, implementation, testing, review, release, or operations. The important shift is not simply that code is generated faster. It is that software work can now progress through the lifecycle without a human manually performing every step.
Market term, not a single standard. “Agentic SDLC” is used by multiple organizations with different definitions and operating models. Causance does not present one generic market definition as a ratified standard. This page explains the term and the governance questions that become important when agents participate as actors in software change.
DEFINITION
What is an Agentic SDLC?
An Agentic SDLC is a software development and delivery lifecycle in which autonomous or semi-autonomous AI agents can carry consequential work across multiple stages rather than merely suggest code inside one human-operated step.
Agents can carry multi-step work.
An agent may inspect a repository, modify files, run tools, respond to failures, request review, or prepare a release without a person manually performing each substep.
Verification has to survive higher throughput.
Tests, reviews, policy checks, security controls, evidence requirements, and release conditions have to operate as explicit gates rather than assumptions around a human-paced workflow.
Responsibility does not disappear.
Faster delegated execution increases the importance of knowing who or what was permitted to act, which evidence applied, what actually changed, and what happens after failure.
The exact division of labor varies. Some organizations use agents mainly for implementation and testing. Others extend agent participation into issue triage, review preparation, release operations, incident response, or maintenance. “Agentic” therefore describes a spectrum of delegated execution rather than one universal lifecycle diagram.
WHAT CHANGES
The bottleneck moves from producing code to governing change.
Traditional SDLC controls often assume that a person remains naturally present at each handoff. Agentic systems weaken that assumption. The control problem shifts toward explicit delegation, evidence, verification, reconstruction, and recovery.
GOVERNING QUESTIONS
An Agentic SDLC needs answers that remain explicit at machine speed.
Agent capability is only one part of the system. A serious operating model also has to preserve decision rights, evidence, failure behavior, and reconstructable history as work accelerates.
FAILURE MODES
Agentic delivery can fail even when the agent completed the task.
A successful tool call, merge, deployment, or test run is evidence of progress. It is not automatically evidence that the right authority existed, that the evidence remained applicable, or that later recovery is authorized.
Workflow progress is mistaken for authority +
An agent can complete work that it was technically capable of performing without proving that the organization intended to authorize that exact operation in that exact scope.
Evidence becomes stale while execution continues +
Security, review, dependency, policy, or source-state evidence can change after an earlier check. A robust lifecycle needs to know when evidence must be revalidated instead of treating “checked once” as permanently valid.
A later success hides an earlier failure +
Repeated agent attempts can eventually pass while obscuring the failure states that preceded them. Those failures can be decision-relevant evidence and should remain attributable.
Recovery silently revives permission +
Restoring code, context, or service is not the same as restoring authority. Recovery should reconstruct supported state without inventing permission for a later operation.
Provider changes alter governance semantics +
Switching models, agents, tools, or hosts can change identity, evidence, execution, and control assumptions. Portability matters only when the relevant semantics can be reconstructed and revalidated.
Human review becomes the throughput bottleneck +
When machine production scales faster than review capacity, organizations need explicit rules for what must be reviewed, what can be delegated, and which conditions force a stop.
CAUSANCE PERSPECTIVE
The hard problem is not “Can the agent code?”
The narrower question
- Was the exact software change authorized?
- Did the evidence that supported the decision still apply?
- Can the decision and observed effect be reconstructed later?
- Does failure remain visible rather than being overwritten by retries?
- Does recovery require a separately supportable condition?
The boundary
- Causance does not claim to define every use of “Agentic SDLC.”
- Causance does not claim that AI agents make software safe or correct.
- Customer systems retain credentials, enforcement, deployment, emergency authority, and operational effects.
- Current product and evidence status must be read from the public evidence boundary, not inferred from this definition page.
CAUSANCE LINEAGE
Causance is the current platform identity.
The reusable governed software-delivery platform now called Causance was historically developed under the Agentic SDLC name. Historical repository and task identifiers retain that name for traceability; they are not a separate current product.
That lineage matters for search, technical history, and source attribution. Causance does not claim ownership of Agentic SDLC as a generic market phrase. The term is now used across the industry for multiple approaches to agent-driven software development.
HOW TO EVALUATE AN AGENTIC SDLC
Evaluate the control contract, not the demo.
A useful evaluation gives installed tools and internal controls full credit, then asks whether any material authority, evidence, provenance, recovery, or substitution gap remains.
Separate permission from progress.
Can the environment represent who or what may perform the consequential operation independently of task completion?
Make eligibility evidence-dependent.
Can stale, missing, conflicting, superseded, or inapplicable evidence force revalidation or stop progression?
Reconstruct the decision.
Can a reviewer later determine what authority and evidence made the change eligible at the relevant moment?
Preserve meaning through failure.
Can the system recover supported context without silently restoring expired, consumed, or otherwise invalid authority?
GO DEEPER