Machine Intelligence Runtime (MIR) is an emerging execution-time control layer between an application and model inference. It coordinates runtime state, context, agents, tools, memory, policy, approvals, recovery, telemetry, and evidence so probabilistic models can perform bounded, reviewable work. This is a MiRuntime editorial definition, not a claim that the term is already an industry standard. Source: Related research framing
Key takeaways
- An inference runtime generates model outputs; MIR governs the work around those outputs.
- MIR becomes relevant when a system has state, authority, side effects, or long-running recovery needs.
- The model remains replaceable. Runtime contracts preserve policy, evidence, and execution semantics across model changes.
What MIR is—and is not
| Layer | Primary concern | What remains missing |
|---|---|---|
| Inference runtime or model server | Weights, batching, decoding, cache, hardware utilization | Task authority, tool safety, approvals, recovery, user-facing evidence |
| Agent framework | Agent loops, prompts, planning patterns, tool declarations | Deployment isolation, durable state, organization-wide policy, incident-grade evidence |
| Workflow engine | Deterministic state transitions, retries, schedules | Model uncertainty, context governance, prompt-injection boundaries, semantic validation |
| AI gateway | Model traffic, routing, quotas, cost, provider abstraction | Full agent lifecycle, local workspace, memory governance, action recovery |
| Machine Intelligence Runtime | Execution state, authority, action, recovery, and evidence | It composes with all of the layers above rather than replacing them. |
Core distinction: an LLM runtime produces tokens. A Machine Intelligence Runtime produces controlled work.
Four runtime responsibilities
Observe
Maintain visibility into objective, context, identity, model state, tool results, uncertainty, budgets, and pending side effects.
Constrain
Apply schemas, least privilege, data rules, budgets, approval gates, and network restrictions before actions occur.
Recover
Checkpoint, retry, reroute, compensate, roll back, pause, or escalate when the run deviates or a dependency fails.
Prove
Preserve sources, policy decisions, approvals, tool outputs, artifacts, changed state, and unresolved uncertainty.
When MIR is justified
| System property | Likely design |
|---|---|
| One model call, no tools, no retained state | A model client or inference runtime is usually sufficient. |
| Read-only retrieval with bounded sources | Add retrieval controls and observability; a complete MIR may be unnecessary. |
| Multi-step tool use with reversible writes | Use typed tools, checkpoints, budget limits, and evidence events. |
| Irreversible or regulated side effects | Use explicit identity, policy decisions, human approval, isolation, and incident-ready evidence. |
| Long-running or federated agents | Use durable lifecycle state, recovery, trust boundaries, and cross-agent authority constraints. |
Citation and claim boundary
| Question | Answer supported by this site | Boundary |
|---|---|---|
| What is MIR? | An execution-time control layer for state, authority, tools, memory, policy, recovery, telemetry, and evidence around model-driven work. | This is a MiRuntime editorial definition, not an industry standard. |
| What should a short answer cite? | The canonical route /machine-intelligence-runtime/, this definition section, and the related architecture or developer pages when implementation detail is needed. |
Do not cite package files as proof of live deployment. |
| What is not claimed? | No SDK, hosted runtime, certification, conformance program, benchmark result, production customer, or autonomous public action API is claimed by this page. | Claims require visible source records, UTC review dates, and matching structured data. |
| When should a human review? | When a generated summary treats research proposals as production capability, infers write authority, or turns an analogy into a factual equivalence. | Unsupported actions should stop or route to the contact page for review. |
MiRuntime maturity model
This maturity model is an editorial framework, not a standard or certification.
- Level 0
Model serving
Loads models and produces outputs.
- Level 1
Contextual application
Adds retrieval, prompts, and session context.
- Level 2
Tool-using agent
Plans and invokes tools, often with framework-local state.
- Level 3
Governed runtime
Adds explicit identity, policy, approvals, isolation, and evidence.
- Level 4
Recovering and reflective runtime
Checkpoints, compensates, diagnoses, and adapts under guardrails.
- Level 5
Verifiable and federated runtime
Exchanges bounded work and evidence across independent trust domains.
Source record
References
- AI Runtime Infrastructure Primary source
Christopher Cruz. arXiv. Published 2026-03; last reviewed 2026-06-20 UTC. Research paper.
- ONNX Runtime Documentation Primary source
Microsoft and ONNX Runtime contributors. ONNX Runtime. Published Current documentation; last reviewed 2026-06-20 UTC. Official documentation.
- vLLM Documentation Primary source
vLLM project. vLLM. Published Current documentation; last reviewed 2026-06-20 UTC. Official documentation.
National Institute of Standards and Technology. NIST. Published 2023-01-26; last reviewed 2026-06-20 UTC. Government framework.