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PRODUCT
Vira Behavior sits between human judgment and runtime execution. It turns corrections into evidence, exact revisions, evaluated releases, and explainable runtime decisions.
The goal is not to create a larger prompt editor. The goal is to know what behavior is authoritative, why it applied, and how to reverse it.
WHAT VIRA CONTROLS
A behavior revision is an immutable, versioned representation of governed AI behavior. Runtime deployments reference exact revisions rather than a moving “latest” state.
That identity lets teams evaluate a change before release, trace a runtime answer back to its authority, and roll back deliberately when evidence changes.
THE LIFECYCLE
Capture a correction from an exact runtime trace or reviewed human instruction. Teaching creates evidence; it does not silently change production.
Preserve reviewed knowledge with version history, scope, evidence lineage, and explicit relationships instead of letting corrections disappear into chat history.
Candidate behavior becomes an immutable revision only through the governed path. Runtime does not point at a moving latest prompt.
Compare candidate and baseline behavior on exact datasets, surface critical regressions, and seal the evidence used for a release decision.
Resolve project, application, workspace, user, and session authority into a bounded effective program and canonical artifact.
Model-target projections may be optimized, but they remain tied to exact behavior identity and can become stale. Canonical execution remains the fallback.
Use promotion evidence, shadow traffic, canary health, explicit promotion, stop, and rollback rather than treating healthy telemetry as automatic authority.
Runtime receipts connect the answer to the Behavior Point, applied and skipped layers, signals, revision set, projection, and model execution evidence.
GO DEEPER
TEACH
Capture reviewed corrections as governed evidence with scope, registered facts, counterexamples, and provenance before anything can change production.
Explore →TRACES
Trace an AI response back to its Behavior Point, revision set, authority layers, signals, projection, model target, and immutable execution receipt.
Explore →EVALUATIONS
Compare an exact candidate with an exact baseline, surface critical regressions, preserve provider execution evidence, and seal what a release decision actually evaluated.
Explore →DEPLOYMENTS
Promote immutable behavior revisions through explicit release states where online health produces evidence but never silently becomes promotion authority.
Explore →OPTIMIZATION
Use model-target optimization such as DSPy or GEPA behind canonical behavior identity, independent evaluation, staleness rules, explicit promotion, and canonical fallback.
Explore →GOVERNANCE
Govern project, application, workspace, user, and session behavior with explicit hard authority, delegation, conflicts, approvals, audit, privacy, and retention.
Explore →CANONICAL VS OPTIMIZED
Optimized projections are optional execution artifacts. They are eligible only for the exact behavior identity, composition, and model target they were evaluated against.
SCOPE
Authority composes across project, application, workspace, user, and session layers. Lower scopes can customize only what higher authority permits; hard conflicts do not silently become “latest wins.”
EXPLAIN
A runtime receipt can show why a behavior module applied, why another layer was rejected, which Lessons contributed, and whether canonical or governed optimized execution was selected.
RELEASE
Shadow and canary health produce evidence. Promotion remains an explicit governed action with exact artifact and model identity, plus a rollback path.
ONE CONTROL PLANE
Start with one Behavior Point and one correction. Keep the behavior identity portable as the application, framework, or model changes.