Producing a targeted, specific clarifying question when parsed intent is ambiguous, rather than a generic 'please clarify' prompt.
Detecting sensitive content (an SSN, a diagnosis code) that lands inside a free-text field never explicitly tagged as sensitive by schema.
Matching the same real-world entity across systems using rule-based keys first, fuzzy matching second, and LLM judgement only for genuinely ambiguous remaining…
Fanning out calls to multiple connected systems in parallel and joining their results against a resolved intent.
Keeping retrieved data structurally separate from instructions in the assembled context, and flagging anomalous embedded instructions before they reach the LLM.
Suggesting improved phrasing or structure to a prompt creator drafting a new template, without authoring the template autonomously.
Proposing a new task-type data bundle by analysing clusters of similar ad-hoc requests that never matched an existing template.
Converting free-text requests into a structured entity-action-attribute representation the orchestration layer can act on.
Producing a coherent, well-formatted prose or structured summary from multiple systems' raw field-level data.
Combining a request's data-sensitivity tags with its parsed intent to classify overall risk, feeding Tag-Based Routing.
Letting a prompt consumer find a relevant template by meaning rather than exact keyword match.
Flagging usage that deviates from a user's or template's own historical baseline.
Producing a regulator-ready export (e.g. SR 11-7-style documentation) directly from structured provenance logs.
Matching a request to a registered task-type template (QBR, headcount justification, escalation response) from a bounded, known label set.
Deciding whether a cached field is fresh enough to serve or must be re-fetched, per a configured time-to-live per field-volatility class.
Aggregating structured request logs into usage trends and template rankings for the registry's discovery surface.