This report synthesizes the supplied Machine Intelligence Runtime research input into a publication-ready category overview. MIR is defined as an emerging execution-time layer that governs state, context, authority, tools, memory, recovery, telemetry, and evidence around model inference.
The report deliberately excludes unverified accelerator comparisons, customer anecdotes, future product claims, and statistics that could not be confirmed from primary sources.
Architecture
MIR sits between application intent and model or tool execution. Its core components are an execution state manager, context and memory manager, policy decision point, tool mediator, recovery controller, and evidence ledger. The components can be implemented as one process or distributed services, but their contracts should remain explicit.
1Application
Captures user intent and presents reviewable outcomes.
- Inputs
- Objective, identity, user interaction
- Outputs
- Run request, review, approval
- Failure mode
- Ambiguous objective or hidden consequence
- Keep outside the prompt
- Final user authorization and product policy
2Runtime orchestration
Maintains state, schedules steps, budgets work, and selects recovery paths.
- Inputs
- Run contract and observations
- Outputs
- State transitions and work dispatch
- Failure mode
- Loops, stale workers, or unbounded budgets
- Keep outside the prompt
- Lifecycle authority cannot live only in a prompt
3Policy and permissions
Evaluates identity, risk, data, tools, and required approvals.
- Inputs
- Subject, action, resource, context
- Outputs
- Allow, deny, transform, approve, escalate
- Failure mode
- Ambient authority or policy bypass
- Keep outside the prompt
- Credentials, policies, and decisions remain typed
4Memory and context
Selects scoped, attributable, current information for the run.
- Inputs
- Sources, memory scopes, retention rules
- Outputs
- Context projection and provenance
- Failure mode
- Stale, poisoned, excessive, or prohibited memory
- Keep outside the prompt
- Consent, deletion, and source authority
5Tools and connectors
Executes typed actions through bounded adapters.
- Inputs
- Validated arguments and scoped credentials
- Outputs
- Structured result, side effect, artifact
- Failure mode
- Partial writes, exfiltration, timeouts, injection
- Keep outside the prompt
- Permission and side-effect class
6Model adapters
Normalizes model capabilities, streaming, tools, budgets, and errors.
- Inputs
- Context, prompt contract, model route
- Outputs
- Candidates, plans, embeddings, scores
- Failure mode
- Invalid output, provider failure, capability mismatch
- Keep outside the prompt
- Model selection and provider policy
7Evidence and telemetry
Preserves operational traces and user-facing execution evidence.
- Inputs
- Events from every layer
- Outputs
- Timeline, artifacts, metrics, incident record
- Failure mode
- Missing correlation, sensitive overcollection, silent effects
- Keep outside the prompt
- Retention, access, and evidence semantics
Operational responsibilities
The runtime observes state, constrains authority, recovers from failure, and proves what happened. It also schedules model calls, verifiers, tools, and approvals against finite budgets. This broadens performance from token throughput to successful, compliant task completion.
Protocols such as MCP can expose tools and resources, while model servers such as ONNX Runtime or vLLM execute inference. MIR composes these layers rather than relabeling them.
Security and governance
Agentic systems add prompt-injection, overprivileged tools, credential exposure, poisoned memory, runaway loops, silent side effects, and cross-agent trust risks. Production controls include default-deny permissions, typed schemas, short-lived credentials, isolated execution, outbound network policy, approval gates, checkpoints, monitoring, and evidence. Advanced token-level or reflective controls remain research topics.
Open research questions
- Benchmarks that combine task success, policy, recovery, evidence, latency, and cost.
- Intervention policies under uncertainty.
- Formal and probabilistic assurances across tool and agent composition.
- Portable evidence and replay semantics.
- Hardware-software co-design for long-context and multi-step workloads.
Source note
The supplied report was treated as an editorial research input. Claims were decomposed and checked against primary sources. Material without adequate support was omitted rather than presented with false precision.
Source record
References
- AI Runtime Infrastructure Primary source
Christopher Cruz. arXiv. Published 2026-03; last reviewed 2026-06-20 UTC. Research paper.
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. arXiv / ICLR. Published 2024-08; last reviewed 2026-06-20 UTC. Research paper.
- Model Context Protocol Specification — 2025-11-25 Primary source
Model Context Protocol project. Model Context Protocol. Published 2025-11-25; last reviewed 2026-06-24 UTC. Protocol specification.
National Institute of Standards and Technology. NIST. Published 2023-01-26; last reviewed 2026-06-20 UTC. Government framework.
- Agentic AI Threats and Mitigations Primary source
OWASP Agentic Security Initiative. OWASP. Published Current guidance; last reviewed 2026-06-20 UTC. Security guidance.
OpenTelemetry project. Cloud Native Computing Foundation. Published Current specification repository; last reviewed 2026-06-24 UTC. Official specification.