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Plain answers toautonomous engineering.

Short, definitive explainers on the ideas behind autonomous engineering - what they mean, how they work, and where they show up in practice.

What is 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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What is 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.

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What is the ADLC (AI-Driven Development Lifecycle)?

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.

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What is 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.

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What is technology due diligence?

Technology due diligence is the assessment of a target company’s software, architecture, security, infrastructure and engineering organization to identify risks and investment needs before a transaction. Modern technology due diligence inspects the systems directly - code, cloud, pipelines, runtime and operational data - instead of relying mainly on interviews and documents.

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