Why it matters
Most AI lifecycle frameworks bolt process onto a generic tool, which makes the result easier to supervise but no more capable. Engineering needs structure, context, memory, policy, testing and integration built in from the start.
What is the ADLC (AI-Driven Development Lifecycle)?
Definition
The ADLC, or AI-Driven Development Lifecycle, is OpenAna’s methodology for how an autonomous AI engineer does work: plan, understand, clarify, implement, test, approve, audit and remember. Unlike a classic SDLC with blanket stage gates, it applies risk-aware approvals - speed where it is safe, human control where it is needed.
In short
Key takeaways.
- 01
The discipline lives in the engineer, not in a checklist the team has to configure.
- 02
Approvals are risk-aware: sensitive or destructive actions stop for explicit sign-off.
- 03
Memory carries decisions and history across sessions, so work compounds.
How it works
ADLC (AI-Driven Development Lifecycle), step by step.
- 01
Plan
Build a structured plan from the task context before touching code.
- 02
Understand
Read the existing codebase, architecture and conventions - including brownfield systems.
- 03
Clarify
Resolve ambiguous requirements and capture context.
- 04
Implement
Make the change with validation, handling security and non-functional requirements inline.
- 05
Test
Generate and run tests to prove quality.
- 06
Approve
Apply policy-aware approval: proceed when safe, stop for a named approver when not.
- 07
Audit
Summarize what changed, why, and what is left.
- 08
Remember
Keep decisions, incidents and history for the next task.
How OpenAna does it
Every Ana engineer - software, security, SRE / DevOps, AI / ML and embedded - follows the ADLC. It runs on the Echelon Network, which provides episodic memory, governance at the Gate, expert routing and integrations across your stack.
FAQ
Common questions about adlc.
The SDLC describes stages a team moves work through, usually with fixed gates. The ADLC describes how an AI engineer works on each task, with approvals triggered by risk rather than by stage.
Related terms
Keep learning.
Learn
Policy-as-code
Policy-as-code is the practice of writing an organization’s security, cost, compliance and operational rules as version-controlled, machine-readable code - for example in OPA (Rego), HashiCorp Sentinel, or YAML/JSON - so they can be tested, reviewed and enforced automatically every time a change is proposed.
Learn
Autonomous remediation
Autonomous remediation is the practice of software agents fixing problems - vulnerabilities, misconfigurations, failing deployments or production incidents - rather than only detecting and reporting them. The agent diagnoses the issue, writes the change (a code patch, dependency upgrade or infrastructure fix), validates it with tests, and routes it through the organization’s approval and deployment process.
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AI vulnerability remediation
AI vulnerability remediation uses AI agents to fix security vulnerabilities end to end: triaging findings by real exploitability, writing the patch or dependency upgrade, adding tests that prove the fix, and producing evidence for auditors. It closes the gap between how fast vulnerabilities are found and how fast teams can fix them.
See it in practice
Watch Ana do it in your stack.
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