The DarkNinja delivery system
Faster software delivery without blind automation.
DarkNinja uses specialized AI agents as a controlled engineering workforce. Agents parallelize bounded tasks. Senior engineers design the system, review the work and own the production result.
Discuss your requirement ↗Repository access is not a delivery system
An agent can read code and still misunderstand the product, authority model, shared contracts, release boundary, or evidence required for completion. Delivery begins by making those constraints explicit.
Specialized agent roles
Each agent receives one bounded responsibility, the context needed for that responsibility, and an observable completion condition.
- Research
- Architecture support
- Backend implementation
- Frontend implementation
- Test and QA
- Security review support
- Documentation
A human control plane
Senior engineers interpret requirements, approve architecture, control permissions, review changes, integrate the complete system, and own release and rollback decisions. Agents do not replace developers or approve architecture.
Task decomposition
Work is divided by dependency and contract ownership. Independent tasks may run in parallel; shared boundaries remain under one explicit owner.
Shared contract protection
Request fields, response structures, permissions, data ownership, and error behavior do not drift silently. A contract change updates every consumer and its verification evidence.
Parallel execution rules
Agents receive narrow scopes, do not edit the same files concurrently, disclose assumptions, and return reviewable changes. Integration happens through a human-owned main context.
Evidence before completion
Output remains untrusted until reviewed. Tests, browser evidence, security findings, performance profiles, deployment checks, and operating documentation determine whether work is complete.
Data and model-provider controls
Production secrets are not exposed. Client code follows an agreed provider, retention, access, and logging policy before any model receives context.
Where efficiency comes from
Efficiency comes from parallel independent work, smaller review units, reusable context, and earlier verification. It is not an automatic cost-reduction promise.
What this model does not promise
Agents do not deploy autonomously, guarantee correctness, replace product decisions, or remove the need for experienced review. Human owners approve release and rollback.
Can agents access production?
Not by default. Production authority is minimized, auditable, time-bounded when necessary, and separated from implementation work.
Can the provider policy change?
Yes, through an explicit client decision and an updated data boundary. Provider and retention choices never drift as an implementation detail.