HuggingPlace is a control, quality, improvement, and lineage plane for production AI. Connect telemetry, evaluation, governed data, training, policy decisions, and staged deployment while keeping the evidence that explains each new release.
Every release answers what changed, why it changed, and where it runs.
Preview capabilities are feature-gated and may require a configured workflow executor. Available means the backend path is implemented—not that every external framework is automatically instrumented.
Connect production evidence to evaluation, governed data, release decisions, and deployments.
HuggingPlace coordinates improvement work and preserves evidence. It is not presented as a replacement for a durable agent execution runtime.
Ingest OpenTelemetry traces, metrics, and logs over authenticated OTLP/HTTP.
Protobuf and JSON ingestion, org isolation, redaction, and query APIs are available behind the OTLP feature flag. Framework-specific auto-instrumentation remains the application's responsibility.
Run resumable, auditable improvement workflows in control-plane, workflow-runner, or agent mode.
The workflow engine provides checkpoints, approvals, events, leases, retries, and resumability. Full automation depends on configured executors and remains a preview capability.
New training promotions fail closed on schema, validation, lineage, and training-policy evidence.
Legacy promotions are preserved and flagged for review. New attempts require an immutable approved policy decision before model or runtime promotion.
Shift deterministic traffic through observable canary stages with automatic rollback.
Route revisions advance only after minimum evidence and configured quality, error-rate, and latency checks pass.
HuggingPlace governs what happens next.
A production failure should not die in a dashboard. Carry it into evaluation, governed data, improvement work, release evidence, and a staged deployment. Workflow and agent execution modes remain explicit, selectable preview capabilities.
Connect model and agent systems to a shared control and evidence plane.
Ingest OTLP traces, metrics, and logs, plus native interaction signals.
Score captured behavior with shadow judges, suites, and operator review.
Coordinate governed curation, optimization, and training through resumable workflows.
Evaluate release readiness from schema, validation, policy, and lineage evidence.
Stage model traffic, observe health, and roll back unsafe route revisions.
Trace every running behavior back through the evaluations, data, transformations, policies, builds, artifacts, reviews, and people that produced it. Move forward quickly without losing the ability to explain the result.
Connect sessions, model calls, tool activity, runtimes, telemetry, and human decisions in one evidence model.
Use durable workflows to move from a failure to a versioned test, governed dataset, optimized artifact, or trained candidate.
Preserve the causal chain across data, transforms, evaluation, builds, approvals, releases, and deployments.
Use the control plane across supported managed, private, and registered device environments. Runtime execution remains separate from the durable improvement workflow and lineage services described here.
Coordinate managed deployments with staged routing and live quality evidence.
Keep runtimes and evidence inside infrastructure you control while sharing release policy and lineage.
Track registered packages, adaptations, and devices back to their source artifacts.
