6
Pillar 01
6 Stages
From intent to release, each stage defines what humans own, what agents execute, and what must be proven before progress.
ADLC · Agentic AI Framework
Agentic Development Lifecycle
Build software where AI agents execute and humans steer. ADLC gives teams a practical lifecycle for intent, architecture, implementation, verification, release, and continuous improvement.
What is inside
Four pillars of the Agentic Development Lifecycle
The ADLC organizes agentic software delivery around stages, gates, autonomy levels, and an end-to-end operating model.
5
Pillar 02
5 Gates
Quality checkpoints stop weak assumptions from flowing downstream. Nothing passes until it proves it builds.
5
Pillar 03
5 Levels
Autonomy increases from human-led to autonomous execution based on evidence, risk, reversibility, and confidence.
E2E
Pillar 04
ADLC E2E
The full operating playbook connects stages, gates, roles, feedback loops, and governance into one lifecycle.
Core principle
Autonomy is earned, not given. It is proven through gates, bounded by controls, and expanded through strategic delegation.
ADLC lets humans stay accountable while agents perform meaningful work safely.
Human steering + agent execution
Use ADLC when software teams want AI agents to move beyond simple code suggestions and participate in the full delivery lifecycle.
Pillar 01 · 6 Stages
From intent to release
ADLC breaks delivery into clear handoffs. Each stage has an entry point, expected outputs, agent responsibilities, human responsibilities, and exit criteria.
A
Intent & ScopingDefine the problem, constraints, and success criteria.
B
Architecture & DesignShape the solution; agents propose, humans approve.
C
ImplementationAgents execute with checkpointed human review.
D
Verification & TestingAutomated and human validation before release.
E
Deployment & ReleaseControlled rollout, monitoring, and approval.
F
Operate & ImproveFeedback loops improve future delivery.
G1
Intent Claritymust passG2
Design Integritymust passG3
Build Verificationmust passG4
Release Readinessmust passG5
Production Healthmust passPillar 02 · 5 Gates
Nothing passes until it proves it builds
Gates are not bureaucracy. They are how high autonomy stays safe. If a gate fails, the agent receives structured feedback and the work loops back before downstream risk compounds.
Pillar 03 · 5 Autonomy Levels
From hands-on to hands-off
Delegation should change by task, stage, confidence, reversibility, and risk. ADLC makes autonomy a managed variable instead of an all-or-nothing decision.
L1Human-led
L2Agent-assisted
L3Co-piloted
L4Agent-led
L5Autonomous
Why ADLC
Traditional SDLC was not designed for non-deterministic agents
ADLC keeps what works from traditional software delivery and adds the governance layer needed when agents perform meaningful engineering work.
Agents are non-deterministic
Outputs vary, so checkpoints must catch issues before they compound across the lifecycle.
Autonomy must be earned
ADLC expands delegation only when task performance, risk, and confidence support it.
Humans stay accountable
People continue to define intent, own outcomes, approve gates, and manage boundaries.
Feedback is structural
Operate and Improve feeds production learnings back into design, implementation, and testing.
Works with existing SDLC
ADLC complements Agile, waterfall, and continuous delivery by adding agentic governance.
Scales with capability
Teams can start conservatively and increase autonomy as agents prove reliability.