MiRuntime.com
Runtime Architecture
A practical architecture for Machine Intelligence Runtime: model adapters, tool mediation, memory, approvals, telemetry, evidence, and local endpoint control.
A Machine Intelligence Runtime should be designed as a layered system. The model layer is important, but it is not enough. The runtime must mediate everything around the model.
Product screens, developer APIs, workflow triggers, and user-facing controls.
Session state, agent loops, routing, retries, cancellation, approvals, and task checkpoints.
Tool boundaries, data-access rules, safety checks, identity context, and human gates.
Short-term task state, long-term memory packages, retrieval context, and scoped project knowledge.
Files, databases, browsers, calendars, email, local services, private APIs, and external systems.
Local models, cloud models, multimodal models, embedding models, and specialized inference backends.
Run logs, tool traces, artifacts, changed files, user approvals, evaluation results, and exportable evidence.
Design requirements
Replaceable models
The application should not collapse when a model changes. Runtime adapters isolate model-specific prompt formats, token limits, tool formats, and streaming behavior.
Explicit tool contracts
Every tool needs an input schema, output contract, permission boundary, failure mode, and logging path. The runtime owns that discipline.
Scoped context
Context should be intentionally selected and explainable. The runtime should make it clear which memory and documents affected a task.
Reviewable side effects
File writes, messages, calendar changes, purchases, deployments, and account actions should be staged, reviewed, or blocked according to policy.
Reference flow
- Intent capture: the application receives a request and normalizes user intent.
- Context assembly: the runtime selects allowed memory, documents, state, and task constraints.
- Model planning: the model proposes a response, tool call, or workflow step.
- Runtime validation: the runtime checks schemas, policies, permissions, and required approvals.
- Tool execution: approved tools run through adapters with bounded inputs and observable outputs.
- Evidence capture: the runtime records the task path, artifacts, changes, and unresolved decisions.
- User handoff: the system returns an answer, artifact, or review queue with enough context to trust or reject the result.
Architecture principle: never let the model become the only place where state, authority, policy, or evidence exists.