Detecting when a parsed intent is underspecified or has more than one plausible resolution, and either asking a targeted clarifying question or offering the…
Flagging usage patterns that look like misuse or excessive access before they're discovered in a periodic review — one user's prompts pulling an unusual volume…
Producing regulator-ready documentation directly from provenance logs — which model, which team, which decision, at what cost and latency — as a byproduct of…
Continuing to produce a usable, clearly-labelled partial result when one or more connected systems are unreachable, rather than failing the whole request — and…
Determining that a record in one system and a record in another refer to the same real-world entity, in the absence of a shared universal identifier — the…
Translating each connected system's own permission model — SFDC sharing rules, SAP authorisation objects, ServiceNow ACLs — into one filterable model, then…
Predefining which entities, systems, and fields a recurring task type needs — a QBR always pulls account tier, ticket trends, and revenue; a headcount…
Tracking how current each piece of grounded data is, and deciding — per field, not just per source — whether a cached value is fresh enough to use or must be…
Recording, for every grounded prompt, exactly which fields were fetched from which system, when, matched to which resolved entity, and under whose request —…
Ensuring that data subject to a residency or sovereignty constraint — PHI that cannot leave premises, ITAR-controlled technical data — is never routed to an…
Maintaining the translation between each connected system's native schema and a canonical, cross-system representation — so "revenue" resolves consistently…
Masking or redacting specific sensitive fields — an SSN, a salary figure, a diagnosis code — within an otherwise-permitted record before it reaches the…
Holding a routed-for-review prompt or a proposed tool call for explicit human sign-off before it executes, with the reviewer given enough context — what data,…
Routing and executing calls to the specific backend systems a resolved intent requires — often several in parallel — and assembling their raw responses for…
Interpreting a free-text prompt to infer which enterprise entities, attributes, and time windows it actually requires — turning "show me everything about…
Routing a newly authored or modified prompt template through a review and sign-off step before it enters the registry — typically via an existing enterprise…
Formally retiring a prompt template that's outdated, superseded, or has drifted from the data model it was built against — with a visible deprecation notice…
Detecting and blocking the specific failure where retrieved, untrusted data is used to hijack the LLM's behaviour against the requesting user's actual intent —…
A central, searchable catalogue of approved prompt templates that prompt consumers can find and use directly, without going through the authoring/approval…
Creating and iterating on a reusable prompt template — with its own version history, not a string overwritten in place — so a change to a widely-used template…
Letting a user save a specific assembled context block for reuse — the same Globex account summary reused across three different prompts in a week — rather…
Filtering which records and fields a grounded response may include based on the requesting user's role and entitlements, evaluated per system rather than…
Classifying a grounded prompt's sensitivity based on what data it contains and what it's being asked to do with it — distinguishing a routine QBR summary from…
Formatting resolved, multi-system data into a coherent block — structured text, YAML/JSON, or a short narrative summary — suitable for injection into an LLM…
The developer-facing counterpart to natural-language parsing: accepting an exact, schema-level request — named entity, named fields, explicit filters — that…
Sending a classified prompt down the execution path its tags earn — a registry-matched, low-sensitivity prompt executes immediately; a minor modification gets…
Recognising that a request matches a known recurring task shape — a QBR, a headcount justification, an escalation response — and invoking the pre-built data…
Surfacing which templates, task types, and data sources actually get used, by whom, and how often — feeding the Prompt Registry's discovery ranking and giving…
Showing the assembled context block to the requesting user before it's sent to the LLM, and letting them edit, remove, or add to it — the Level-1 pattern from…