ComputeResource {
resource\_id
provider\_type
hardware\_profile
model\_capability
region
latency\_profile
concurrency\_limit
cost\_profile
uptime\_score
trust\_level
}
| | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | This structure enables more granular scheduling decisions. For example, low-latency trading signal tasks should prioritize resources with consistently stable response times; large-scale offline training tasks can utilize lower-cost resources with less stringent real-time requirements; while tasks involving sensitive data should execute within higher-trust execution environments. #### 3.2 AI Workload Abstraction Traditional compute scheduling primarily focuses on hardware metrics such as CPU, GPU, memory, and bandwidth utilization. AI-native scheduling must additionally understand the semantics and execution structure of the task itself. manadia decomposes an AI workload into multiple observable stages: * Model Inference * Token Processing * Context Assembly * Data Retrieval * Tool Calling * Memory Access * Agent Planning * Strategy Execution * Trace Logging * Evaluation This abstraction allows the system to move beyond simply asking, "Which node is available?" to determining, "What capabilities does this task require?" For example, a market prediction task may require real-time market data, low-latency inference, risk models, trading tools, and audit records, whereas a content generation task places greater emphasis on context window size, output quality, and cost efficiency. manadia abstracts AI Workloads into standardized task units that are orchestratable, traceable, and billable. The system supports task priority scheduling, cost constraints, timeout control, retry mechanisms, and multi-level degradation strategies. It can fully interpret the lifecycle of an AI task—from request intake, context assembly, model routing, tool invocation, strategy execution, and response generation to trace logging and billing settlement—making the entire execution process observable, reproducible, and auditable. #### 3.3 Dynamic Model Routing Dynamic Model Routing is one of the core capabilities of the manadia Compute Coordination Network. Its objective is to determine the optimal execution path across different models, APIs, and compute nodes. Model routing considers multiple dimensions: * Task type * Model capability * Context length * Response latency * Invocation cost * Output stability * Current workload * Failure rate * Available region * Security and trust level A simplified routing workflow can be represented as: |route(task):
classify task\_type
estimate quality\_requirement
estimate latency\_limit
estimate cost\_limit
filter available models
score candidates by capability, latency, cost, reliability, trust
select primary route
prepare fallback route
execute request
log trace and metrics
| | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | For manadia, Dynamic Model Routing serves not only external developers but also the internal Trustworthy AI Prediction Model. Different prediction tasks may require different model capabilities: event understanding relies on language models, market state recognition depends on time-series models, anomaly detection utilizes statistical models, while strategy selection leverages Agent reasoning. Multi-model routing enables these capabilities to be flexibly combined. #### 3.4 Agent Runtime AI Agents are one of the primary execution units within the manadia network. Unlike a single model invocation, an Agent continuously reads context, invokes tools, processes data, makes decisions, and records results across multiple execution steps. Therefore, manadia provides an Agent Runtime to manage the entire lifecycle of Agent execution. An Agent Runtime may consist of the following modules: * Session Manager — Manages session state and task context * Prompt Context — Constructs model input context * Tool Registry — Manages available tools * MCP Connector — Connects external tools and services * Memory Layer — Stores short-term and long-term memory * Action Executor — Executes tool invocations and strategy actions * Evaluator — Evaluates intermediate and final outputs * Trace Logger — Records the complete execution workflow |Agent Runtime
│
├── Session Manager
│ ├── Session State
│ ├── Task Context
│ └── Execution History
│
├── Prompt Context Builder
│ ├── System Prompts
│ ├── Conversation History
│ ├── Real-Time Data Fragments
│ └── Tool Descriptions
│
├── Tool Registry
│ ├── Available Tools
│ ├── Tool Permissions
│ ├── Tool Invocation Records
│ └── Tool Result Cache
│
├── Action Executor
│ ├── Tool Invocation
│ ├── Parameter Validation
│ ├── Error Handling
│ └── Retry Logic
│
├── Memory Layer
│ ├── Short-Term Context (Session)
│ ├── Long-Term Memory (Database)
│ ├── User Preferences
│ └── Learning Feedback
│
├── Evaluator
│ ├── Intermediate Step Evaluation
│ ├── Final Output Evaluation
│ └── Quality Scoring
│
└── Trace Logger
├── Step-by-Step Execution Records
├── Model Invocation Logs
├── Tool Invocation Logs
├── Data Source Access Logs
└── Full Replay Capability
| | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | Within the Trustworthy AI Prediction Model, the Agent Runtime plays a particularly important role. Prediction Agents continuously read market data, assess market conditions, select strategies, adjust parameters, invoke execution modules, record position changes, and feed execution results back into the model evaluation system. The Agent Runtime employs sandboxed execution to ensure multiple Agents operate independently without interference. It features layered short-term and long-term memory storage, enabling persistent storage and retrieval of conversation context, decision history, and tool outputs. The system also implements a principle of least privilege, strictly limiting Agent access to highly sensitive tools such as wallets, signing, and trading. Every operation is fully recorded and auditable. #### 3.5 Tool and MCP Integration The capabilities of future AI systems will largely depend on their ability to invoke external tools. Models provide reasoning capabilities, while tools provide execution capabilities. Agents within the manadia network can invoke various types of tools, including: * Market data tools * On-chain data tools * Wallet and signing tools * Trading execution tools * Database tools * Search tools * Risk management tools * Audit tools * Enterprise APIs * MCP services All tool invocations are incorporated into a unified tracing system. An Agent execution failure may not originate from the model itself, but from data source timeouts, tool execution errors, insufficient permissions, invalid parameters, or unavailable external APIs. Agent Trace enables developers and users to accurately identify the root cause of such issues. #### 3.6 Compute Monitoring and Consumption Analytics To support real-world business applications, AI systems must understand both their operational costs and performance characteristics. manadia's AI Consumption Analytics records and analyzes the following metrics: * Request count * Token consumption * Model invocation cost * Average response time * P95/P99 latency * Success rate * Failure rate * Retry count * Fallback count * Tool invocation count * Cost per task * Cost per user * Performance by model These metrics continuously optimize model routing strategies. For example, if a particular model incurs significantly higher costs with only marginal quality improvements for a specific task, its routing priority can be reduced. Likewise, if a compute node experiences increased latency during peak traffic periods, the scheduling layer can automatically reroute requests. #### 3.7 High Concurrency and Fault Tolerance AI workloads naturally exhibit bursty traffic patterns. Market volatility, trending events, prediction market settlements, enterprise batch jobs, and concentrated user activity may all generate sudden spikes in request volume. manadia improves high-concurrency stability through the following mechanisms: * Request queues * Task prioritization * Caching strategies * Batch processing * Load balancing * Node health checks * Timeout retries * Multi-model fallback * Circuit breaker mechanisms * Regional routing For the Trustworthy AI Prediction Model, high-concurrency capability is more than a user experience concern—it directly impacts strategy execution. During periods of extreme market volatility, data update frequency, model inference frequency, and strategy execution frequency all increase significantly, requiring the underlying infrastructure to remain consistently stable. manadia adopts a multi-layer traffic control architecture consisting of ingress rate limiting, scheduling-layer queuing, execution-layer circuit breakers, and resource-layer overload protection. The network supports multi-region disaster recovery and active-active deployment across geographically distributed regions. During peak traffic periods, compute nodes are automatically scaled, while abnormal conditions trigger strategy degradation and multi-model backup routing, ensuring that mission-critical services—including the Prediction Model, Oracle, and payment infrastructure—maintain over 99.9% availability even under extreme traffic conditions. --- # Agent Instructions This documentation is published with GitBook. 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