Agentic Runtime

Run and orchestrate AI agents as first-class engineering entities. Execution, scheduling, state, and observability in one runtime layer.

Agentic Runtime Capabilities

A unified capability model defining the essential primitives of enterprise-grade agent platforms.

Explore Capabilities
System Idle
App DevDeploy DAGPlatform EngScaleModel EngWeightsUserSystem LayerAPI Gateway / AuthReact FrontendRuntime / AgentLangGraph OrchestrationTool Execution / HistoryKubernetes ClusterKServe / Kueue / Auto-scalingAI RuntimevLLM Engine / PyTorchNVIDIA H100

01Core Components

  • Execution Engine

    Runs prompts, tools, and model calls with observability hooks.

  • Orchestrator

    Graph-based workflows (LangGraph) with stateful turns and contracts.

  • Scheduler

    Queues, priorities, and rate limits to keep agent workloads predictable.

  • State & Logs

    Durable events and replayable state for agents as processes.

  • Observability

    Metrics, traces, and audits for every action in the agent loop.

02Lifecycle & Integrations

  • Lifecycle

    PLAN -> ACT -> OBSERVE -> UPDATE STATE -> LOOP / EXIT. Agents behave like long-lived processes, not fire-and-forget calls.

  • Infra Integration

    Kubernetes, Ray, and KServe power serving, autoscaling, and distributed compute for runtime workloads.

  • OSS Integration

    LangGraph, RAGFlow, and toolchains plug into the runtime with clear positions in the stack.