An Operating System for Intelligent Workloads
CognOS
A fully distributed cognitive operating system for agentic systems: reactive, coordinated, and governed at any scale.
An Operating System for Intelligent Workloads
A fully distributed cognitive operating system for agentic systems: reactive, coordinated, and governed at any scale.
CognOS is a declarative platform on which AI workloads are specified entirely in YAML, without custom application code. Each workload is a domain application: a collection of agents, workflows, knowledge graphs, memory profiles, and event channels that run on the CognOS runtime.
Traditional enterprise software handles known processes. CognOS handles unknown ones. It gives autonomous agents the memory, coordination primitives, and governance controls to reason about complex, evolving situations, and to act within clearly defined boundaries.
Where conventional AI tools produce answers, CognOS produces governed outcomes. Every agent decision is traceable, every knowledge claim carries provenance, and every workflow stage is auditable... meeting the auditability requirements of regulated industries without custom compliance engineering.
CognOS is built around three independent functional modules, each addressing a distinct plane of cognitive system operation.
Coordinates long-running, stateful work across Processes, Workflows, and Tasks. Manages autonomous Agents with six cognitive memory systems: Context, Episodic, Semantic, Procedural, Spatial, and Prospective... so agents retain knowledge across runs and improve over time.
A live, structured knowledge graph that stores Entities, Claims with confidence and provenance, Artifacts, and Embeddings. Exposes three query surfaces: Graph for relationships, Search for text and vector queries, and Cube for analytics... each backed by pluggable cloud-native stores.
Manages real-time data movement through typed Streams of Frames, Pipelines with composable operators (Filter, Transform, Window, Join), Subscriptions for delivery, and Triggers for reactive agent execution. Bridges internal coordination with external system integration via webhooks, SignalR, and Kafka.
All three pillars are unified by CognOS Core, which provides the shared infrastructure every module depends on: a sequential 11-guard admission chain (validation, policy, capability, dependency, gate, concurrency, priority, budget, human checkpoint, storm brake, and role), a double-entry budget ledger tracking tokens and compute cost, an append-only tamper-evident audit log, OpenTelemetry observability, and a 60+ Custom Resource Definition (CRD) system that gives every component its declarative configuration layer.
CognOS agents operate within a four-stage loop that turns raw signals into governed action.
Ingest signals, data streams, and context from internal systems and external sources via the Event Fabric.
Apply reasoning, structured knowledge from the Knowledge Mesh, and agent memory to understand what the signals mean.
Select actions based on goals, declared policies, budget constraints, and human-in-the-loop checkpoints when required.
Execute through agents, automated workflows, or integrations, with every action signed, logged, and traceable.
Multiple agents collaborate with shared memory, explicit delegation, and structured planning... coordinating across domain boundaries without central bottlenecks.
Agents are configured declaratively with role definitions, tool access, memory profiles, and budget constraints. Runtime discovery enables dynamic team formation for complex tasks.
Every workload seeds the Knowledge Mesh with domain-specific entity schemas. Agents traverse relationships, query embeddings, and update the graph as they work, knowledge compounds across runs.
Entire workloads: agents, workflows, knowledge schemas, memory profiles, event channels, are specified in YAML. No custom application code. GitOps-compatible and continuously drift-detected.
Structured multi-stage workflows with typed inputs and outputs, composable operators, and conditional branching... handling everything from daily scheduled sweeps to real-time reactive pipelines.
An 11-guard admission chain, human-in-the-loop checkpoints, tamper-evident audit logs, RBAC/ABAC policy enforcement, and budget governance: production-grade controls built into the platform, not bolted on afterward.
CognOS ships with a growing catalog of production-ready workloads... each a complete domain application built entirely from declarative YAML.
Full intelligence cycle: Collection, Processing, Analysis, Production, Dissemination: across OSINT, SIGINT, HUMINT, and IMINT. NATO-standard products with causal reasoning overlays.
Autonomous SOC with proactively scheduled threat sweeps, triage investigation, human escalation queues, and compliance audit workflows. Applicable to SOC 2, ISO 27001, and NIS2 environments.
Real-time fleet optimization tracking dozens of assets with continuous assignment, route compliance detection, and demand forecasting by zone. Adaptable to emergency services, military logistics, and field operations.
Five-layer architecture that takes a natural-language product idea and produces a fully functional, tested, deployed web application, with visual quality gates and autonomous repair loops.
Multi-domain crisis synthesis with runtime agent discovery and dynamic delegation across geopolitical, economic, cybersecurity, infrastructure, and public health specialist agents.
Complete systematic trading lifecycle; alpha research, portfolio construction, risk management, execution, and post-trade analysis; with 26,000+ lines of declarative YAML specification.
Closed-loop autonomous marketing with creative rotation via bandit optimization, statistical significance testing, and compliance monitoring; no human involvement in the daily optimization cycle.
Eight-agent operations platform managing inventory sourcing, listing creation, repricing, cross-platform synchronization, and fulfilment... event-driven from intake through sale.
Multi-tenant knowledge sharing with governed data marketplaces... publishers define shareable data and conditions; consumers subscribe to live-updated views without accessing internal agent configurations.
Tournament-grade probabilistic forecasting using superforecasting methodology: base-rate anchoring, inside/outside view synthesis, and extremised aggregation. Competes autonomously in Metaculus tournaments.
Meta-workload: agents running on CognOS monitor CognOS itself. Anomaly detection, structured incident summaries, and diagnostic hypothesis generation over the platform's own operational data.
Governed machine learning lifecycle from data collection through model serving, with formal human-in-the-loop approval checkpoints for model promotion, meeting AI governance requirements in regulated industries.
Total deployed YAML lines: ~67,500 across 13 complete workloads · 160 of 174 platform CRD kinds exercised