No continuous ownership
Tickets, specs, pull requests, test results, and releases drift apart until the semantic link is lost.
AI-native engineering, with control
The control plane for AI-native software engineering.
Chizh coordinates AI agents, engineering tools, executable environments, and human approvals into one traceable software development process.
Not another coding agent. Not another CI tool. Chizh is the orchestration layer that keeps ownership, boundaries, policy, and accountability intact while AI does more of the work.
One Change
Stable identity across the full lifecycle
Policy-driven
Human approvals where needed, automation where earned
Provider-neutral
Works with your stack instead of replacing it
The problem
The tools are multiplying. The process is fragmenting.
Teams now have LLMs, coding agents, code review systems, CI pipelines, deployment tools, and observability platforms. Each tool sees a slice. None owns the end-to-end engineering process.
No unified policy
Approval rules, environment access, model choice, and risk controls live in disconnected systems.
No shared traceability
You can see what happened inside each tool, but not why a Change advanced across the whole lifecycle.
The model
Chizh orchestrates roles around shared reality.
The product and its executable environments sit at the center. Specialized roles sit around the rim. Each role should inspect reality directly, and communicate deliberately.
Product
+ executable environments
Product
Architecture
Development
QA
Operations
Support
Prefer observation over communication
Roles should inspect the running system, the codebase, the environment, and the evidence directly whenever possible.
Prefer local communication over global coordination
Collaboration usually flows between adjacent responsibilities, preserving boundaries and reducing noise.
Autonomy increases gradually
Start with AI proposes / human approves, then move toward policy-based automation and exception review.
One Change. One identity.
Everything revolves around the Change.
A Change is the canonical unit of engineering work. Product requirements, technical specs, pull requests, QA plans, QA reports, and deployment records are all projections of the same underlying Change.
Raw intent
→
Product requirement
→
Technical specification
→
Implementation / PR
→
Build & test evidence
→
QA report
→
Release record
Stable identity
Chizh keeps one durable work object from the first request to the final release.
Structured state + human artifact
Automation works from structured state, while humans review readable artifacts derived from it.
Traceable decisions
Every artifact, execution, approval, and transition can be traced back to who or what produced it.
Human-in-the-loop, by policy
Humans set the boundaries.AI does the work.
Chizh does not force a single autonomy model. It lets organizations move from supervised AI execution to higher levels of automation as trust is earned.
01
AI proposes
People inspect the output and approve the transition.
→
02
Policy governs
Approvals, tests, and tool access are evaluated systematically.
→
03
Exceptions escalate
Routine work flows automatically while humans review risk, anomalies, and samples.
Integrations
Works with your stack.
Chizh is capability-oriented and provider-neutral. It coordinates your existing tools instead of trying to replace them.
Source controlGitHub / GitLab
Code reviewPRs / Reviews
Build & testActions / CI
EnvironmentsPreview / Kubernetes
DeploymentsArgo / Native pipelines
ObservabilityLogs / Metrics / Traces
AI workersReasoning / Coding agents
PolicyApprovals / Access / Validation
Chizh
Bring structure to AI-assisted engineering.
If your organization is already experimenting with AI for product, architecture, development, and QA, Chizh is the missing control plane.