Where PlanVault™ fits
This page explains how PlanVault™ sits alongside common stack layers—without claiming to replace specific vendor products or open-source libraries. The focus is the governed API and event execution boundary.
Where PlanVault™ sits next to your agent stack
PlanVault™ is the governance and execution layer that connects to your agent stack — existing LangChain, LangGraph, CrewAI, AutoGen, DSPy, OpenAI agent API, or MCP agents can run behind a PlanVault™ tool boundary, with tool calls, secrets, approvals, and recovery handled outside the model and every run recorded for audit and replay.
PlanVault™ keeps tool calls, secrets, approvals, and recovery outside the model.
Tool routing
Centroid-based DB routing; shortlist built before the LLM
Managed by the relevant OpenAI API surface
Often implemented in application logic or through LLM/graph routing
Often policy, catalog, or app-level routing
Self-hosted · VPC-ready
External API dependency
Self-managed infra, cloud LLM
Often SaaS-oriented; options vary by vendor
Catalog scalability
Dynamic routing from catalogs of thousands of tools; adaptive shortlist per step
128 tools (Assistants API); context-bounded (Responses API)
Depends on agent design and available context
Often manually or policy-managed catalog
Crash recovery
State and recovery depend on the provider-managed API surface
Checkpointing is possible; recovery semantics often stay app-owned
Often tied to the vendor conversation or workflow model
Large responses
Schema flattening, JSONPath extraction, stdlib tools, depth truncation
Depends on token/context limits and API pattern
Usually handled at the application layer
Depends on product focus and integrations
Three different problems, often confused under one name
The AI agent governance market in 2026 has split into three fundamentally different categories that are not direct competitors to each other.
Governance Overlays
Added on top of existing frameworks (LangChain, CrewAI, OpenAI SDK). Intercept actions after the agent has already decided what to do. Examples: Microsoft AGT, Lakera, NeMo Guardrails, Galileo.
Cloud-native Managed Runtimes
Managed agent execution from a cloud provider. Governance via cloud IAM and provider-native audit. Deployment, residency, and contractual terms vary by provider and configuration. Examples: Google Vertex AI Agent Engine, Amazon Bedrock Agents, Azure AI Foundry.
Governed Execution Runtimes
The execution environment itself. Governance is built into the execution pipeline—not added on top. Includes planning, execution FSM, per-tenant encryption, and customer-controlled deployment. Examples: PlanVault (purpose-built). Adjacent tools with partial overlap: Temporal (durable execution, no AI governance), Orkes Conductor (workflow runtime, no per-tenant controls).
Most media attention in 2026 focuses on Governance Overlays and Cloud-native Runtimes. PlanVault is a Governed Execution Runtime—deployed in a customer-controlled environment with deployment posture defined by the customer.
Customer-controlled deployment vs. cloud-managed execution
Vertex AI Agent Engine, Bedrock Agents, and Azure AI Foundry are managed runtimes operated by the cloud provider. They support CMEK for encryption, but deployment posture, data residency, and governance configuration depend on provider-specific settings and contracts. PlanVault runs entirely within your own infrastructure.
Customer-controlled deployment
✅ Full VPC / on-prem
❌ GCP-managed
❌ AWS-managed
❌ Azure-managed
Per-tenant BYOK (customer-operated KMS)
✅ AES-256-GCM + KMS
⚠️ CMEK via Cloud KMS (keys managed in provider control plane)
⚠️ CMEK via Cloud KMS (keys managed in provider control plane)
⚠️ CMEK via Cloud KMS (keys managed in provider control plane)
Compliance documentation model
Customer-operated documentation
Provider-specific documentation varies
Provider-specific documentation varies
Provider-specific documentation varies
LLM provider independence
✅ LiteLLM proxy
❌ Gemini-primary
❌ Bedrock models
❌ Azure OpenAI
Prompt-safe secrets
✅
❌
❌
❌
OpenAPI tool import
✅ Any spec
❌ Custom integrations
❌ Custom integrations
❌ Custom integrations
Self-hosted deployment
✅ Self-hosted / VPC
❌
❌
❌
HITL native FSM gates
✅
❌ Callbacks only
❌ Callbacks only
❌ Callbacks only
Governed execution runtime vs. overlay tools
Governance Overlays solve a different problem: protecting existing agent frameworks. They are not direct alternatives to PlanVault, but are often evaluated side by side. The key distinction: an overlay intercepts actions; a runtime governs all execution from start to finish.
Execution runtime
✅ FSM + Pekko
❌ Overlay only
❌ Overlay only
❌ Overlay only
❌ Overlay only
LLM-generated plans
✅
❌
❌
❌
❌
Crash recovery
✅
❌
❌
❌
❌
Policy enforcement
✅ FSM gates, hard-deny
✅ YAML / OPA / Cedar
✅
✅
✅ Colang
Per-tenant encryption
✅ AES-256-GCM + KMS
❌
❌
❌
❌
Prompt-safe secrets
✅
❌
❌
❌
❌
Residency / deployment control
Self-hosted / customer VPC
Deployment-dependent
VPC / on-prem options
Deployment-dependent
Self-hosted option
Self-hosted
✅ Full stack
✅ Container
✅ SaaS-primary
❌
✅
Certifications (SOC 2 / ISO)
❌ None yet
❌
SOC 2
SOC 2
❌
OWASP Agentic Top 10
Mapping documented
Published mapping
Partial
Partial
Partial
Framework integrations
Examples (open-source)
✅ 13+ adapters
✅
✅ API
✅ LangChain
Open-source
❌
✅ MIT
Partial
❌
✅
Purpose-built AI governance vs. general workflow runtimes
Temporal and Orkes Conductor are mature, battle-tested workflow runtimes. They provide excellent execution durability and can be extended to support AI agent use cases. PlanVault's advantage is not that it does things Temporal cannot — it is that it ships months of AI-specific infrastructure out of the box: OpenAPI/MCP tool import, LLM context management, per-org key isolation, streaming plan UI, and native HITL approval flows.
Customer-controlled deployment
✅ Full VPC / on-prem
✅ Self-hosted or Temporal Cloud
✅ Self-hosted or Orkes Cloud
Per-tenant envelope encryption (isolated key per org)
✅ AES-256-GCM, per-org key isolation + KMS
✅ Payload Codecs (worker-side encryption)
✅ Payload Codecs (worker-side, via Temporal SDK)
Built-in HITL approval UI + FSM gate
✅ Native FSM gates + approval UI (no code)
⚠️ Native Signals (primitive exists, build UI yourself)
⚠️ Native Signals (primitive exists, build UI yourself)
OpenAPI / MCP tool import
✅ Any spec, zero code
❌ Custom activity code required
❌ Custom worker code required
LLM-agnostic execution
✅ LiteLLM proxy
⚠️ Bring your own LLM calls
⚠️ Bring your own LLM calls
Audit trail & replay
✅ Full execution replay
⚠️ Workflow history only
⚠️ Workflow history only
Cost control & budget gates
✅ Per-run budgets
❌ Not supported
❌ Not supported
Multi-tenant org isolation
✅ Native org_id isolation
❌ Namespace-level only
❌ Namespace-level only
OSS community & ecosystem
⚠️ Early-stage community
✅ CNCF project, large OSS community
✅ Commercial support + partner ecosystem
SOC 2 Type II certification
⏳ In progress
✅ Temporal Cloud certified
✅ Orkes Cloud certified
General-purpose workflow support
❌ AI agents only
✅ Any workflow type
✅ Any workflow type
Categories next to PlanVault™
Workflow runtimes
Workflow engines excel at human-oriented processes and SLAs; PlanVault™ adds a layer for AI-initiated actions: bounded tool catalogs, execution policy, and a complete event trail.
Agent orchestration frameworks
Libraries and frameworks help describe agent graphs and logic; PlanVault™ focuses on what happens after planning: validation, FSM execution, journaled transitions, and side-effect policy.
Enterprise governance systems
Compliance and IAM platforms set access rules for people and services; PlanVault™ adds a boundary for AI runs: plan validation, tool execution, and reviewable evidence.
Tool routing and retrieval middleware
Retrieval and routing narrow context for the model; PlanVault™ holds the execution boundary: who may call which API, with which secrets, and with what audit trail after the answer.
Durable execution systems
Systems that guarantee long-running steps often do not define per-AI-run API call policy, human approvals, and audit evidence—that is PlanVault™’s boundary.
AI runtime safety controls
Moderation and guardrails reduce unsafe model outputs; PlanVault™ governs what happens in your systems: calls, approvals, idempotency, and recovery.
Contrast: planning versus governed execution
- An LLM or agent produces or proposes a plan of actions.
- PlanVault™ validates the plan and executes through runtime and FSM—rather than trusting unconstrained API calls.
- State transitions are journaled; side effects are policy-gated.
- Recovery, audit, replay, cost visibility, retention, and user-data operations are runtime-boundary concerns—not only model text.
Limitations and transparency
PlanVault™ is designed for customer-controlled deployment, network integration, automated SBOM publication, security review, and penetration-test readiness—see the security overview and technical security documentation. However, we do not yet hold or publish SOC 2 or ISO certifications.
Ecosystem and community are early-stage, but the platform already includes runnable examples, integration paths, and implementation docs—start with the documentation.
The buyer category for a governed AI execution layer is still forming. PlanVault™ is currently in closed customer testing, and qualified teams can request a guided evaluation through request access.
We do not publish unverifiable named customers or unsupported production-scale claims; data handling expectations are described in the Privacy Policy. The comparisons on this page are category-level, not a legal assessment of named vendor products; deployment models and contractual terms may vary.