Runtime Governance for Multi-Agent AI Workflows
The missing layer between AI agent frameworks and enterprise systems. Enforces business policies, manages shared state, and auditable lineage.
Why enterprise AI agents fail
AI agents excel at reasoning and decision-making but struggle with enterprise coordination. Without runtime governance, multi-agent systems break down in predictable ways.
State inference failures
Agents guess workflow state from conversation history rather than querying authoritative sources. The result: invalid actions, duplicate work, and "amnesia" between turns.
Coordination breakdown
Multiple agents operate in silos without shared context. The result: infinite loops, agent ping-pong, and 50+ tool calls per turn with no meaningful progress.
Compliance and audit gaps
No deterministic record of who did what, when, or why. The result: SOX violations, failed audits, and zero accountability.
Root Cause
Agents infer workflow and state from conversation history rather than accessing authoritative, persistent workflow state. This architectural gap causes the majority of multi-agent failures in production environments.
NPL: the runtime layer for AI agents
NPL sits between your agents and your systems, providing deterministic workflow state, enforcing business rules at runtime, and logging every action with cryptographic identity.
Runtime enforcement
- Protocols define valid state transitions and permissions
- Business rules execute at runtime, not only at design time
- Agents perform only the actions the protocol permits
Multi-party state
- Single source of truth for all participating agents
- Agents query NPL for current state and never infer it
- Eliminates amnesia, loops, and coordination failures
Complete audit trail
- Every action logged with party identity and timestamp
- Cryptographic verification for regulatory compliance
- Ready for SOX, GDPR, and industry-specific audits
Separation of concerns
NPL enforces a clean boundary between agent reasoning and workflow governance.
| Agents Handle | NPL Handles |
|---|---|
| Goals & reasoning | Workflow state & transitions |
| Decision-making | Permission enforcement |
| Exception handling | Access control & validation |
| Natural language processing | Business rule execution |
| Tool selection & orchestration | Audit logging & compliance |
Framework agnostic. Enterprise ready.
NPL integrates with your existing AI stack via the Model Context Protocol (MCP) standard.
Supported Agent Frameworks
Google ADK
LangChain / LangGraph
AutoGen
Anthropic Claude
CrewAI
Custom Frameworks
Enterprise Systems
SAP
ERP
Salesforce
CRM
ServiceNow
ITSM
Custom APIs
Integration
The Integration Architecture
NPL sits between your AI agents and enterprise systems, providing governed state and enforcing business rules via the MCP standard.
AI Agent Layer
NPL Runtime
Enterprise Systems
Where multi-agent orchestration delivers
See how NPL enables reliable multi-agent coordination in enterprise workflows.
Procurement and Sourcing
Enable multi-agent negotiation across supplier networks with enforced approval thresholds and business rules.
Workflow
Create PR, Source Suppliers, Send RFQ, Evaluate Quotes, Approve, Create PO
NPL Enforces
Budget limits, approval chains, vendor validation
Tailored insurance
Automate bespoke insurance coverage, e.g. for freight. Agents on both sides follow a strict negotiation process and formalize the outcome in a binding contract.
Workflow
Assess Risk, Negotiate Terms, Validate Coverage, Formalize Contract, Issue Policy
NPL Enforces
Underwriting rules, coverage limits, contract formalization, regulatory compliance
Trade finance
Agents representing importer, exporter, and issuing bank coordinate document exchange, goods verification, and payment release through strict sequential steps formalized in a letter of credit.
Workflow
Apply for LC, Issue LC, Ship Goods, Present Documents, Verify Compliance, Release Payment
NPL Enforces
LC terms, document requirements, UCP 600 rules, payment conditions
Regulated onboarding
An onboarding agent guides clients through documentation submission, reviews and prepares materials per policy, requests additional information when needed, and recommends approval or decline for human review.
Workflow
Collect Documents, Verify Completeness, Review Per Policy, Request Clarifications, Recommend Decision, Human Review
NPL Enforces
KYC/AML rules, documentation requirements, escalation to human reviewer
What makes NPL different
NPL fills the gap between process oriented design tools, automation and AI reasoning.
| Capability | BPM Tools | RPA | LLM Agents | NPL + Agents |
|---|---|---|---|---|
| Runtime Enforcement | Design-time only | Script-based | - | Deterministic |
| Judgment & Reasoning | - | - | Yes | Yes |
| Multi-Party State | - | - | - | Shared & queryable |
| Complete Audit Trail | Partial | - | - | Cryptographic |
| Dynamic Coordination | - | Brittle | Ad-hoc | Protocol-defined |
The Gap NPL Fills
- Runtime enforcement: Process tools define workflows at design time but fail to enforce them when code runs. NPL guarantees policy at execution time.
- Reasoning + rules: RPA automates but can't reason. LLM agents reason but can't coordinate reliably. NPL provides the shared state that makes both work together.
- Auditable by default: Every state change, decision, and agent action is recorded with cryptographic identity, no extra instrumentation needed.
Why It Matters for AI
As AI agents take on more enterprise responsibility, the gap between what they can do and what they should do widens. NPL closes that gap.
Agents stay in bounds even when their reasoning is creative or unexpected. Instead of brittle message passing, multiple agents coordinate through shared, governed state. And because every action is recorded with cryptographic identity, compliance is built in from day one rather than bolted on after deployment.
Technical specification
NPL Capabilities
- Protocol-defined state machines
- Compiler-enforced authorization
- Transactional execution guarantees
- Automatic REST API generation
- Standard MCP exposure of services
Deployment Options
- NOUMENA Cloud (managed)
- Hybrid (your data, our ops)
- On-premise (full control)
See multi-agent orchestration in action
Start with a proof-of-concept using mock integrations. Then connect to your production systems when you are ready.