Transition your delivery pipeline from AI-assisted to AI-native.
AI-assisted means humans coordinate, AI assists. AI-native means AI coordinates, humans participate strategically. LoomStack provides the orchestration infrastructure to make that transition safely and incrementally.
Stripe ships with AI — built on years of coordination infra
Typical SDLC touchpoints that can shift from human to AI
Per developer with uncoordinated AI tools (GitClear 2026)
Org metrics despite individual AI gains (DORA 2025)
The shift
AI-Assisted vs AI-Native.
Humans coordinate. AI assists.
- Engineers manually gather context before prompting
- Each AI session is isolated — no shared memory
- Review, testing, deployment remain fully human-driven
- No risk classification — everything gets the same process
- AI speeds up typing, not the delivery pipeline
AI coordinates. Humans participate strategically.
- Context injected automatically from organizational memory
- Shared context graph across all agents, teams, and services
- Review routing, testing, deployment driven by policy engine
- Risk classified per-change — process scales with criticality
- AI drives the pipeline end-to-end within governed boundaries
Lifecycle
The AI-Native SDLC Stack.
Five phases, fully orchestrated. Context flows forward. Policy gates fire at every transition. Human involvement scales with risk.
Ideation → Spec
Context Layer · Spec Agent
Signals arrive from Jira, Slack, or monitoring. LoomStack enriches them with organizational context — architecture, ownership, constraints — and generates a structured spec that downstream agents can execute against.
Spec → Implementation
Orchestration Engine · Code Agent
The spec decomposes into execution tasks. Code agents operate with full service context, dependency awareness, and coding standards. Parallel execution where safe, sequential where dependencies demand it.
Implementation → Validation
Test Agent · Policy Engine
Tests derive from the spec, not from the generated code. Security scans, semantic conflict detection, and risk re-evaluation run automatically. Policy gates determine what ships and what needs review.
Validation → Deployment
Deploy Agent · Governance Layer
Risk classification drives deployment strategy. Low-risk changes deploy directly. High-risk get canary rollouts. Approval chains fire dynamically based on service criticality and change scope.
Deployment → Production
Observability · Feedback Loop
72-hour observation window correlates production signals to the originating change. Incident patterns feed back into policy calibration. The system learns which changes are safe over time.
Human involvement
Where humans stay. Where AI takes over.
Not every step needs a human. But some always will. The AI-native model makes that distinction explicit and enforces it through policy.
DevOps capabilities
Infrastructure-aware delivery orchestration.
Pipeline-aware orchestration
LoomStack integrates with your existing CI/CD — GitHub Actions, GitLab CI, Jenkins. It doesn't replace your pipeline; it coordinates what enters it, monitors what exits, and correlates outcomes back to the originating signal.
Risk-driven deployment strategy
Deployment strategy selected per-change based on service criticality, blast radius, and historical reliability. Canary, blue-green, or direct deploy — chosen by data, not by default.
Automated rollback orchestration
Production anomalies detected within the observation window trigger automatic rollback for changes below the confidence threshold. No human needed for known failure patterns.
Incident-driven execution loops
Production alerts trigger diagnostic workflows automatically. Root cause analysis, fix generation, and deployment of the patch — all orchestrated with appropriate human checkpoints based on severity.
Move from AI-assisted to AI-native.
Our Co-Build embeds the LoomStack team directly with your org for 12–16 weeks to build and deploy AI-native SDLC orchestration on your infrastructure.