Stack: .NET 10 · ASP.NET Core · Next.js 15 · Azure · Kubernetes
Enterprise-grade multi-layer AI platform — structured reasoning, multi-agent orchestration, zero-trust security, knowledge management (RAG), plugin-based UI, and full NIST RMF compliance in a single .NET 10 + Next.js solution.
cognitive-mesh is the intelligence and governance core of the NeuralLiquid/PhoenixVC platform. It is a complete enterprise AI platform structured as seven interdependent layers, each with its own domain logic, ports/adapters, and contributing agents. Think of it as the full-stack operating system for intelligent, governed AI workloads — from raw infrastructure (data stores, secrets, audit logs) up to a user-facing plugin dashboard.
It is the "thinking layer" that sits between model-routing infrastructure (via sluice) and the products that need intelligent, multi-step results. Platform services either feed it data, route work through it, or consume its outputs.
Everything the upper layers depend on, all behind ports so implementations are swappable.
- Zero-Trust Security: JWT auth, RBAC/ABAC policy enforcement, secrets management (Azure Key Vault), audit logging
- Data persistence: Azure Cosmos DB adapter, Azure Blob Storage adapter, Vector Database abstraction (Azure AI Search / Redis)
- Knowledge & RAG: Semantic search via
EnhancedRAGSystem, Knowledge Graph, Document Processing / ingestion pipeline - Enterprise integration: Microsoft Fabric OneLake adapter, Feature Flag manager, Enterprise Connectors
Modular reasoning engines, each exposed via a typed port interface. Higher layers call these engines without knowing their implementation.
- StructuredReasoning (ConclAIve): Converts raw LLM outputs into auditable structured reasoning via Debate & Vote, Sequential Reasoning, and Strategic Simulation recipes
- AnalyticalReasoning: Data-driven analysis, trend identification, structured insight generation
- SecurityReasoning: Threat intelligence engine, anomaly detection, dynamic risk scoring
- EthicalReasoning: Normative agency (Brandom), information ethics (Floridi) — validates actions against ethical frameworks
- CreativeReasoning / CriticalReasoning: Idea generation, logical consistency evaluation, bias detection
- DomainSpecificReasoning: Pluggable industry-specific logic (finance, healthcare, etc.)
- SystemsReasoning: Complex system analysis — feedback loops, interdependencies, leverage points
The layer that monitors the platform's own cognitive processes.
- SecurityMonitoring: Real-time security event aggregation, threat correlation, automated incident response
- ContinuousLearning: Feedback loop — collects operational outcomes, generates learning insights, adapts system behaviour
- PerformanceMonitoring: KPI tracking (latency, throughput, resource utilisation) across all cognitive tasks
- SelfEvaluation:
MetacognitiveOversightComponent— quality, accuracy, and ethical alignment auditing of reasoning outputs - ReasoningTransparency: Generates explanations and justifications for AI decisions (explainability layer)
- CulturalAdaptation: Applies Hofstede's cultural dimensions to tailor agent interactions for global deployments
- Protocols: Manages ACP (AI Communication Protocol) and MCP (Metacognitive Protocol) for reliable inter-component communication
Where cognitive plans become real actions.
- MultiAgentOrchestration: Core engine for coordinating teams of specialised agents — task decomposition, agent selection, workflow execution, result synthesis
- ToolIntegration: Extensible tool framework (web search, data analysis, code execution, classification) — new tools added as adapters
- SecurityAgents: Automated incident response agents — immediate containment and remediation triggered by MetacognitiveLayer
- ActionPlanning: Multi-step plan generation for complex goal achievement
- DecisionExecution: Step-by-step plan execution with tool calls and state management
- HumanCollaboration: Human-in-the-loop infrastructure — agents pause for human review, approval, or intervention
- ConvenerAgents: Coordination agents that facilitate structured multi-party deliberation
The "front door" — all external clients (web, mobile, enterprise services) enter here.
| Application | What it exposes |
|---|---|
| Security | Zero-trust auth, risk scoring, compliance reporting |
| Customer Intelligence | Inquiry handling, conversation management, troubleshooting |
| Decision Support | Situation analysis, options generation, causal modelling |
| Knowledge Management | Document ingestion, knowledge base querying |
| Process Automation | Complex business process automation via agent workflows |
| Research & Analysis | Automated research, document synthesis, content generation |
| NIST Compliance | NIST RMF evidence collection, maturity assessment |
| Value Generation | Business value tracking and reporting |
- AIGovernance: Policy-as-code for AI operations, compliance dashboards
- CommunityPulse: Aggregates team/community signals for adaptive management
- LearningCatalyst: Active learning triggers based on system performance patterns
- UncertaintyQuantification: Surfaces confidence and uncertainty in AI outputs
A full-stack dashboard system (Next.js 15 frontend + .NET BFF).
- Widget marketplace: All UI functionality is a self-contained widget submitted through a governed review process (security scan → code signing → admin approval → registration)
- Personalised dashboards: Users compose their own layout from approved widgets;
DashboardLayoutServicepersists per-user configurations - PluginOrchestrator: "Sandwich pattern" gateway — every widget call is wrapped with auth, validation, and audit logging before reaching inner layers
- AgencyWidgets: Pre-built widgets for core mesh capabilities (Adaptive Balance, NIST RMF, agent status, etc.)
- Next.js frontend (
src/UILayer/web): Next.js 15 + React 19, Tailwind CSS, shadcn/ui, Storybook 8, D3 visualisations, i18n (en-US, fr-FR, de-DE), WCAG 2.1 AA accessibility, offline service worker
External clients (browsers, mobile, enterprise services)
|
v
UI Layer (Next.js 15 + .NET BFF)
|
v
Business Applications Layer (REST API — the platform's public surface)
/ | \
AgencyLayer MetacognitiveLayer ReasoningLayer
\ | /
FoundationLayer
(security, data, RAG, audit, secrets)
|
v
sluice gateway (production model egress configured here)
|
v
Azure OpenAI / model backends
All layers follow Hexagonal (Ports and Adapters) architecture: inner layers define typed ports (interfaces), outer layers provide adapters (implementations). Every infrastructure dependency is swappable without touching business logic.
cognitive-mesh/
├── src/
│ ├── FoundationLayer/ # Infra: security, data stores, RAG, audit
│ ├── ReasoningLayer/ # Cognitive engines: analytical, ethical, security, creative
│ ├── MetacognitiveLayer/ # Self-monitoring, learning, incident response
│ ├── AgencyLayer/ # Multi-agent orchestration, tools, automation
│ ├── BusinessApplications/ # REST API surface — the platform's front door
│ ├── UILayer/ # Plugin dashboard (Next.js + .NET BFF)
│ │ └── web/ # Next.js 15 frontend application
│ ├── MeshSimRuntime/ # Simulation runtime for testing mesh behaviour
│ └── ApiHost/ # ASP.NET Core hosting entry point
├── api/ # OpenAPI specs (convener-api.yaml)
├── tests/ # Unit + integration tests
├── cypress/ # E2E tests (cypress.config.ts)
├── k8s/ # Kubernetes manifests
├── infra/ # Infrastructure as code
├── docs/ # Architecture docs, runbooks, API versioning
├── examples/ # Usage examples per layer
├── .claude/ # Claude agent configuration (commands, hooks, rules)
├── AGENT_TEAMS.md # Agent team structure and responsibilities
├── CognitiveMesh.sln # .NET solution
├── Directory.Build.props # Shared build properties (version, targets)
└── docker-compose.yml # Local development stack
- .NET 10 SDK
- Node.js 20+ with npm (for UILayer/web)
- Docker (for local containerised run)
- Azure CLI (for cloud deployment)
- Access to sluice gateway endpoint (for LLM calls)
# Build the .NET solution
dotnet build CognitiveMesh.sln
# Run tests
dotnet test
# Run the API host locally
dotnet run --project src/ApiHost
# Run the UI frontend
cd src/UILayer/web
npm install
npm run dev # http://localhost:3000
npm run storybook # http://localhost:6006 (component docs)| Repo | Role |
|---|---|
| sluice | AI data plane — production cognitive-mesh model egress is configured to route through sluice; current evidence verifies routing readiness and gateway auth, with live workload attribution still being hardened |
| docket | Cost observability — cognitive-mesh can forward model-usage events to docket; synthetic ingestion smoke is verified, while funding-grade cost evidence still requires accumulated real workload volume |
| phoenix-flow | Project tracker — proxies task-routing decisions to cognitive-mesh for AI-assisted triage and planning |
| deck | Desktop ops tool — invokes cognitive-mesh agent teams for complex multi-step operations; surfaces agent status |
| retort | Agent scaffold — retort-based projects register their agent configs into the mesh; retort generates the .agentkit/ overlay |
| org-meta | Org intelligence — cognitive-mesh reads org-meta MCP context at session start; org-meta documents cognitive-mesh's cross-repo contracts |
| mystira-workspace | Mystira StoryGenerator delegates complex narrative planning to cognitive-mesh (AnalyticalReasoning for story coherence, CreativeReasoning for plot generation) |
- microsoft/semantic-kernel — .NET AI orchestration patterns
- microsoft/autogen — multi-agent conversation patterns
- mcowger/plexus — unified AI provider gateway (study reference for sluice integration patterns; note: no licence file — study only, do not copy code)
cognitive-mesh — a mesh network has no single point of failure; every node connects to every other. A cognitive mesh applies the same topology to AI: no single agent is the bottleneck, reasoning is distributed across specialised nodes, and the network routes around failures. The name deliberately avoids vendor lock-in — it describes the architecture, not the model or provider underneath it.
This project has not yet had a public release. Current development version: 0.0.1.