Layer 01Understand the goal
Objective Layer
Receives user goals, tasks, constraints, context, and desired outcomes as the starting point for system activity.
User objectivesNatural languageTask constraints
Technical ArchitectureARCHITECTURE / 002
The architecture ofintelligent execution.
A layered system model connecting objectives, intelligence, coordination, orchestration, and computation.
This architecture represents the current technical direction being explored for KryomAI Intelligence OS. Individual components and implementation details will evolve through engineering and research.
Architecture Map
FLOW.00201ObjectiveIntent
02IntelligenceReasoning
03CoordinationConnect
04OrchestrationControl
05ComputationExecute
Input
Objective
Output
Computation
System FlowPIPELINE / 005
From objective tocomputational action.
KryomAI is designed around a progressive flow in which a high-level objective is transformed into coordinated, observable, and executable system operations.
Layer 02Interpret and plan
Intelligence Layer
Interprets objectives, reasons over available context, creates plans, evaluates decisions, and selects appropriate actions.
PlanningReasoningDecision-making
Layer 03Connect capabilities
Coordination Layer
Connects agents, models, tools, APIs, services, data sources, and task dependencies into coordinated workflows.
Agent coordinationTool routingTask dependencies
Layer 04Manage operations
Orchestration Layer
Controls execution state, scheduling, retries, permissions, monitoring, feedback, and workflow progression.
SchedulingState managementExecution control
Layer 05Perform the work
Computation Layer
Connects workloads with local, remote, specialized, and heterogeneous computational resources.
CPUGPU / NPUCloud / specialized compute
Computation Fabric
Intelligence should connectto computation.
The computation layer is intended to abstract the resources required to execute intelligent workloads across different environments.
The goal is not to replace every compute system, but to intelligently coordinate the resources available to the workload.
01
CPU Systems
General-purpose computation, operating system tasks, and local execution.
02
GPU / NPU
Accelerated inference, parallel workloads, and specialized AI processing.
03
Cloud Compute
Remote scalable resources, external model providers, and distributed execution.
04
Data Systems
Memory, storage, retrieval, databases, context, and structured information access.
05
Future Accelerators
Potential interfaces for emerging specialized and non-traditional processors.
06
Distributed Systems
Connected machines, clusters, services, and geographically distributed resources.
Design Principles
Built to evolve withchanging intelligence capabilities.
The architecture is intended to remain flexible as models, hardware, execution environments, and research capabilities continue to evolve.
01
Modularity
Each layer should evolve independently without tightly coupling the entire system.
02
Control & Observability
Execution should remain inspectable, permission-aware, measurable, and controllable.
03
Interoperability
The system should connect different models, tools, services, and compute environments.
04
Extensibility
New capabilities, agents, tools, and execution providers should be addable over time.
Illustrative Execution Flow
One objective.Multiple coordinated operations.
A future KryomAI workflow could accept a high-level objective, break it into subtasks, select tools, execute actions, evaluate results, and return a useful outcome.
01
Receive objective02
Interpret and plan03
Select tools and resources04
Execute coordinated tasks05
Evaluate results06
Return outcomeThis is a proposed and evolving architecture model. It communicates KryomAI's technical direction rather than claiming that every layer is already implemented in production.