One runtime for governed AI execution.
JintellarCore sits beneath DAG Studio, DAG Workbench, Coding Workspace, and Skill Hub to route models, enforce guardrails, isolate execution, and preserve audit evidence across real business workflows.
Runtime operations console
One control surface for routes, tools, governed workflow activity, evidence, and operational state.
Platform Map
A runtime architecture built around control, execution, and proof.
Instead of splitting AI governance across separate gateways, policy tools, tracing layers, and deployment wrappers, JintellarCore keeps model routing, tool control, execution containment, and audit inside one runtime.
Jintellar AI Gateway system diagram
A runtime map from native surfaces and external AI tools into governed routing, execution targets, and proof.
AI Gateway
Run one workflow across a mixed model runtime.
JintellarCore does not lock a workflow to one provider or one model class. It routes work across local, private, cloud, and fine-tuned model slots while preserving route reason, policy state, and fallback history inside the same governed run.
- Mix of Model routing across local, cloud, private, and fine-tuned model lanes
- Automatic local or private routing for sensitive requests before cloud access
- Provider fallbacks, route reasons, and model attempts attached to the same runtime trace
- One control point for model access, usage policy, and governed workflow execution
Mixed-model orchestration
Route tasks to different model lanes while the runtime governs execution, memory, sandboxing, and audit.
Guardrails and Hard Deny
Stop risky actions before they touch production systems.
The runtime evaluates AI actions before execution. It can allow, deny, require approval, quarantine, or hard-block destructive work regardless of the underlying account permissions.
- Hard-deny destructive actions such as repository deletion, database drops, VM removal, or operating-system destruction
- Apply approval gates, workspace boundaries, and capability checks before a tool call or side effect is allowed
- Classify sensitive context before cloud routing, not after an external model already saw it
- Tie every decision to the same runtime lineage for review, escalation, and audit replay
Guardrail center
Define route controls, action rules, and hard-deny policies from the same governance surface.
Skill Hub
Bring company tools into the runtime with governed custom packs.
Starter packs get teams moving quickly, but real business workflows need company-specific tooling. Skill Hub lets you register governed tools, MCP endpoints, and SDK-built skills without letting those integrations bypass runtime policy.
- Starter packs for common workflow patterns, then custom packs for company systems
- MCP registry support for bringing managed tool endpoints into governed execution
- Skill SDK support for building internal actions, adapters, and workflow-specific capabilities
- Capability-scoped access so a workflow only gets the tools it was explicitly allowed to use
Starter Packs
Custom Runtime Tooling
Only approved capabilities are available to the workflow.
Custom packs enter the same governed execution path as every other step.
No custom integration bypasses runtime routing, approvals, or audit.
DAG Workbench
See workflow execution as it runs, not after it breaks.
The runtime is not just a gateway. It executes real multi-step work. DAG Workbench gives operators node-level visibility into progress, retries, approvals, outputs, exceptions, and evidence while the run is live.
- Governed workflow execution across agent steps, tool calls, approvals, and outputs
- Node-by-node visibility into what ran, what failed, what retried, and what needs review
- Clear separation between orchestration, technical execution, and evidence capture
- Built for operational workflows, research flows, filing tasks, coding runs, and controlled automations
Live Run Review
DAG WorkbenchSource documents accepted and run scope established.
Sensitive context classified and route approved.
Workspace step executing in Firecracker boundary.
Operator approval required before publish action.
Workbench is the live review surface for governed execution, approvals, outputs, and exception handling.
Observability and Audit
Trace every agent action as one runtime case file.
JintellarCore connects prompts, model attempts, tool actions, workspace events, approvals, outputs, and evidence into one reviewable record. That gives teams observability for operations and auditability for compliance from the same system.
- Agent observability across routes, tool calls, workspace events, retries, and outputs
- Replayable audit history with evidence references and event-level provenance
- One case file instead of scattered logs, detached traces, and separate approval records
- Built for operators, security teams, and compliance reviewers who need to reconstruct what happened
Unified audit timeline
Review execution history, evidence, and replay context from the same governed runtime record.
Deployment and Isolation
Deploy air-gapped and contain execution with Firecracker.
The runtime is designed for real enterprise deployment boundaries. Run it on-prem, in private cloud, or inside fully air-gapped environments, and isolate technical execution inside governed Firecracker-backed workspaces.
- Air-gapped deployment path for regulated or isolated environments
- On-prem and private-cloud friendly runtime architecture
- Firecracker isolation for technical steps that need code, files, notebooks, tests, or transforms
- No forced dependency on external model providers when the deployment boundary does not allow it
JintellarCore
RuntimeGoverned execution with isolated workspaces and controlled routes.
Dream Cycle
Improve the runtime nightly without leaving the governance boundary.
Dream Cycle turns governed execution history into controlled improvement. Routing outcomes, failures, evidence, and operator feedback can be used to tune prompts, routes, workflows, and model behavior without turning the platform into an uncontrolled learning loop.
- Nightly improvement loop for routes, prompts, workflow recovery, and execution patterns
- Deploy, scale, and improve model behavior with governed operational feedback
- Use runtime evidence instead of disconnected experimentation data
- Keep the improvement loop tenant-scoped, reviewable, and policy-aware
Nightly Improvement
Dream Cycle loopRuntime evidence, routes, failures, approvals, and operator feedback.
Improve prompts, routes, workflow recovery, and model selection.
Review changes inside the same governed boundary before rollout.
Send the next cycle through the upgraded runtime with evidence attached.
Runtime Outcome
Govern the models, tools, workspaces, and workflows from one runtime layer.
JintellarCore brings model routing, guardrails, Skill Hub tooling, Firecracker isolation, observability, audit, and Dream Cycle improvement into one governed runtime so AI workflows can run in production without losing policy control or traceability.