Deterministic Agent Runtime
A deterministic agent runtime separates AI planning from production execution. The LLM can interpret the request, gather context, propose a plan, generate a workflow, or choose a tool. The actual production action runs through a typed, versioned, permission-scoped, observable execution path.
Core Pattern
- Agent gathers context and proposes an action.
- Policy and approval layers decide whether the action is allowed.
- A durable workflow engine executes the action through typed steps.
- Every external side effect is logged and idempotent where possible.
- The system writes a human-readable audit trace back to the ticket, request, or evidence record.
Why It Matters for AI ITSM
IT workflows touch identity, devices, applications, finance, compliance, and security. A chatbot-style agent that improvises against broad credentials is not acceptable for production IT. The product needs agentic UX with deterministic runtime guarantees.
Reference Implementations
- Serval documents deterministic TypeScript workflows with no LLM in the default runtime path.
- Temporal, Hatchet, Inngest, and Trigger.dev represent durable execution patterns.
- OpenAI Agents SDK and LangGraph show guardrails/tracing and agent orchestration, but still need a product-owned execution boundary.
Init Intelligence Implication
Init Intelligence should make “the agent can propose; the runtime proves” a core engineering principle. The buyer-facing product can feel conversational, but the trust layer should be a workflow engine plus policy engine plus audit trail. ^[inferred]