Workload surfaces
AI Agents, AI Executions, and developer integrations originate demand.
Tenzarch separates workload demand, execution state, infrastructure, and observability.
The operating model explains how the product behaves as a full execution network: user surfaces create demand, backend records preserve state, workflows orchestrate movement, routing selects eligible capacity, nodes execute workloads, and telemetry makes outcomes inspectable.
The network is larger than any one product surface.
Tenzarch should be understood as the execution architecture connecting product experiences, developer integrations, execution records, workflow orchestration, routing, infrastructure capacity, usage accounting, and operational visibility. AI Agents and AI Executions are important workload surfaces, but they are not the whole system.
Execution records
Execution Jobs and Workflow Runs preserve identity, status, timestamps, progress, and outcome references.
Infrastructure
Execution Nodes provide capacity that can be considered for assignment when eligible.
Observability
Logs, telemetry, history, assignment, failures, and results explain what happened after submission.
Every accepted workload should become a trackable execution object.
A production execution network should not hide requests behind a single spinner. Accepted demand should become a Job reference that survives page refreshes, network interruptions, retries, and downstream processing delays. Workflow Runs then let orchestration and routing progress independently while the user or developer keeps a stable reference.
01AI workload demand
02Validation and credits
03Execution Job
04Workflow Run
05Routing
06Node assignment
07Execution
08Result and telemetry
The major accounting and infrastructure systems stay separate.
Execution Credits
Usage accounting for accepted workload demand.
Node Points
Participation accounting inside Node Rewards.
Wallet sessions
User ownership and account-specific access.
Developer API Keys
Server-side programmatic access for external integrations.
Keeping these systems separate makes the platform easier to operate and explain. Credit usage should not be described as rewards. Node Points should not be described as money, yield, token balance, or execution credits. Wallet sessions and API Keys should not be treated as interchangeable credentials.
The public product should communicate scale without inventing unsupported claims.
Tenzarch can present itself as a serious execution-network platform by explaining its architecture clearly: workload intake, execution records, workflow orchestration, routing, node assignment, execution, usage accounting, logs, telemetry, and result delivery. The docs should not depend on fake metrics, fake decentralization proofs, or unsupported token promises to feel substantial.