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Agent and Tool Execution
Machine Intelligence Runtime provides the controlled execution layer for AI agents, tool calls, retries, approvals, and task evidence.
Agents are not magic workers. They are runtime loops that choose actions, call tools, inspect results, and decide what to do next. The quality of an agent depends on the runtime boundaries around that loop.
Tool execution is the security boundary
A model can suggest an action, but the runtime must decide whether the action is valid, allowed, reversible, and sufficiently understood. This makes tool execution one of the most important responsibilities in Machine Intelligence Runtime.
Schema validation
Inputs should be validated before any tool receives them. The runtime should reject vague, malformed, or unsafe calls instead of allowing best-effort execution.
Permission checks
Tool access should be scoped by user, task, data class, and action type. Read-only, draft-only, and destructive actions require different treatment.
Approval gates
High-impact actions should pause for user review. The runtime should show what will happen, why, and what evidence supports the action.
Retries and recovery
Failures should not become silent hallucinations. The runtime should capture errors, retry when safe, downgrade when necessary, or ask for correction.
Agent loop model
- Observe: receive the user request, tool output, file state, or workflow trigger.
- Plan: choose a next step under explicit task constraints.
- Validate: check policy, schemas, permissions, and required approvals.
- Act: execute a bounded tool call or generate a user-facing response.
- Record: store results, logs, evidence, and unresolved questions.
- Stop: end the loop when the objective is complete or when more authority is required.
Runtime rule: an agent without tool boundaries is not autonomy. It is unreviewed side effect generation.