LLM Prompt Interfaces as Execution Contracts: Reducing Unsafe Tool Invocation Across the Incident Lifecycle in AIOps

Abstract

Natural language interfaces enable flexible interaction with LLM‑based AIOps platforms but introduce reliability risks when user input lacks a verified mapping to a supported intent, parameter schema, or policy envelope. Unconstrained prompts can trigger hallucinated procedures, incorrect tool invocation, parameter‑level violations such as missing service identifiers or malformed time windows, and silent partial execution that evades post‑mortem detection. Guided discovery exposes system capabilities via BM25‑plus‑embedding hybrid retrieval over a versioned intent catalog, matching exact operational tokens alongside paraphrastic variation. Soft enforcement applies a two‑stage slot filling pipeline — deterministic parsers for structured identifiers combined with LLM‑based entity extraction — validated against JSON‑Schema definitions, converting validation failures into actionable repair prompts rather than hard rejections. Dynamic recommendation maintains lightweight dialog state across incident lifecycle stages and employs a rules‑plus‑learned ranker to re‑rank intents, pre‑populate parameters, and suppress sequencing or policy violations contextually. The reference architecture integrates these mechanisms with margin‑based confidence gating that enforces stricter thresholds for destructive operations, server‑side RBAC and environment policy gates evaluated before tool invocation, and an allowlisted execution registry that rejects hallucinated tool calls. Structured telemetry emitted at each pipeline stage — retrieval scores, extraction accuracy, validation error distributions, policy denial rates, and correction loop metrics — enables governance, drift monitoring, and continuous catalog improvement. This model demonstrates that a meaningful proportion of perceived LLM shortcomings in production AIOps can be mitigated through UX‑level constraints that align operator requests with the system's executable capability set. In production systems such as incident monitoring copilots, conversational agents invoke runbooks, diagnostic queries, or remediation workflows through downstream tool interfaces. Prompt‑level ambiguity may therefore propagate into incorrect execution plans, such as mis‑scoped tenant queries, environment‑mismatched remediation, or sequencing violations in mitigation workflows. The reliability of an operational agent is thus determined not solely by model reasoning but by the safety of the interface layer mediating prompt‑to‑execution translation.