Cloud foundations.
DevOps discipline.
Production AI.
KAGI is a production platform for cloud, delivery, and AI systems. It defines a standardized path from requirements to deployable, operable infrastructure and services.
Capabilities
KAGI defines a standardized execution surface for modern production systems. The platform accepts structured requirements and produces deployable, operable infrastructure and services.
Cloud infrastructure
Supported environments for running production workloads with clear ownership, security boundaries, and cost visibility.
- ✓Greenfield and migration scenarios
- ✓Multi-account / multi-project layouts
- ✓Containerized and VM-based workloads
- ✓Identity-aware network and access design
- ✓Cost attribution and budget guardrails
Delivery & operations
Execution paths for getting changes into production predictably, with visibility into impact and failure modes.
- ✓CI/CD pipelines with environment parity
- ✓Release workflows and rollback strategies
- ✓Logs, metrics, and distributed tracing
- ✓Incident response and post-incident hygiene
- ✓Secrets, policies, and configuration boundaries
AI systems
Production-grade AI components integrated as first-class services, not standalone experiments.
- ✓Model deployment and inference endpoints
- ✓Batch and real-time processing paths
- ✓Evaluation, monitoring, and drift detection
- ✓Throughput, latency, and cost controls
- ✓Governance and rollback strategies
Outputs produced: provisioned environments, delivery pipelines, services, and operational artifacts.
Execution
KAGI exposes a deterministic execution pipeline. Each phase captures constraints, produces artifacts, and narrows uncertainty before progressing downstream.
Requirement capture
Goals, constraints, ownership boundaries, and risk tolerance are formalized into an executable system definition.
System realization
Infrastructure, environments, and delivery pipelines are provisioned to produce the first operable system slice.
Operational hardening
Telemetry, access controls, cost guardrails, and failure handling are applied to reach production-grade behavior.
System handoff
Repositories, diagrams, runbooks, and operating assumptions are finalized for sustained ownership.
Outcomes
When inputs meet platform requirements, KAGI produces systems with the following observable properties.
Operational clarity
System behavior is explainable, inspectable, and repeatable.
- ✓Environments and deployment paths are unambiguous
- ✓Dashboards reflect real system state
- ✓Incidents result in permanent system improvements
Security boundaries
Access and policy models align with how systems are operated, not how they are diagrammed.
- ✓Least-privilege by default
- ✓Secrets and credentials are isolated and auditable
- ✓Changes are attributable and traceable
AI systems behave like software
Deployed models operate as managed services with explicit performance, cost, and rollback characteristics.
- ✓Inference endpoints with defined latency profiles
- ✓Batch and real-time execution paths
- ✓Evaluation hooks and rollback strategies
- ✓Cost and throughput visibility