VertexStudio Runtime unifies model execution, agent orchestration, and observability into a single programmable surface.
# VertexStudio Unified Router v3 router: strategy: adaptive_latency nodes: - id: edge-npu-cluster type: edge latency_p99: 6ms cost_per_token: 0.00001 - id: on-prem-h100 type: gpu_cluster latency_p99: 42ms cost_per_token: 0.00008 - id: cloud-burst type: cloud latency_p99: 180ms cost_per_token: 0.00020 rules: - if: latency_sla < 10ms route_to: edge-npu-cluster - if: batch_size > 32 route_to: on-prem-h100 - default: cloud-burst # ✓ Routing 2.4M req/day | Avg cost: $0.00003
# VertexStudio Agent Orchestrator from vertexstudio import AgentGraph, Memory graph = AgentGraph("research_agent") @graph.node async def planner(state): plan = await llm.plan(state.task) return {"steps": plan.steps} @graph.node async def executor(state): results = [] for step in state.steps: result = await tools[step.tool]( step.args, memory=Memory.get() ) results.append(result) return {"results": results} @graph.node async def synthesizer(state): return await llm.synthesize(state.results) graph.edge("planner" → "executor" → "synthesizer") # ✓ 25 max steps | Persistent memory | Auto-retry
# VertexStudio Observability Stack telemetry: traces: backend: opentelemetry sampling_rate: 1.0 token_level: true agent_step_tracing: true metrics: backend: prometheus dashboards: grafana alerts: - name: latency_spike threshold: p99 > 50ms action: page_on_call - name: cost_overrun threshold: hourly_tokens > 10M action: auto_throttle cost_tracking: per_team: true per_model: true anomaly_detection: ml_based # ✓ 99.97% uptime | <5min MTTR
# VertexStudio ML CI/CD Pipeline pipeline: trigger: push stages: - name: train runner: h100-cluster script: python train.py artifacts: model_checkpoint - name: evaluate gates: - metric: accuracy > 0.94 - metric: latency_p99 < 10ms - metric: regression_delta < 1% - name: canary_deploy traffic_split: 5% duration: 30min auto_promote: on_success - name: production strategy: rolling zero_downtime: true # ✓ Avg deploy time: 8min | 0 regressions
# VertexStudio Guardrails Engine from vertexstudio.guardrails import Pipeline guards = Pipeline([ PII_Detector( entities=["NAME","EMAIL","SSN","PHI"], action="redact", confidence=0.92 ), ContentFilter( categories=["harmful","illegal","bias"], model="vertex-guard-v2" ), PromptInjection( scan="jailbreak|override|ignore", action="block_and_alert" ), AuditLogger( immutable=True, compliance=["SOC2","HIPAA","FedRAMP"] ) ]) # ✓ <0.5ms overhead | 99.3% precision
The VertexStudio stack from raw input to autonomous agent action — every hop engineered for speed, reliability, and cost efficiency.
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