Who Dokima suits
Dokima vs general-purpose AI audit agents
A capable model helps. A repeatable review process makes it useful next month too.
General-purpose agents are flexible and can explore a codebase quickly. Dokima adds a durable security workflow around the runner: persistent context, bounded roles, accepted state, follow-up planning and finding governance.
Where it works well
General-purpose AI audit agents has a useful job to do.
- Starting a flexible investigation with minimal setup.
- Adapting quickly to an unusual question or repository.
- Combining analysis with other agent capabilities.
- Producing a useful one-off exploration or explanation.
Where Dokima differs
Context and review, not just another pass.
- 01Dokima owns the review lifecycle instead of relying on one long audit prompt.
- 02Runner output must match expected structured state before work is accepted.
- 03Coverage, finding state, annotations and run history persist in the workspace.
- 04Different supported runners can execute the same bounded Dokima roles.
Capability comparison
CapabilityDokimaGeneral-purpose AI audit agents
WorkflowDefined multi-stage security lifecycleUser-defined or tool-specific autonomous task
StateCanonical local review and coverage stateOften session or run oriented
Quality gatesStructured validation before schedule advancesVaries by agent and prompt
SchedulingDue work, stages, follow-ups and cooldown handlingTypically invoked for a requested audit
OutputNormalised findings and static HTML reportConversation, patch, comments or custom report
Use them together
Use a general agent for unusual investigations. Use Dokima for the repeatable review lifecycle and the long-lived record your team works from.
See what Dokima finds
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