| 2131788687eb2453… | The time-to-value for building directly on Fabric is the longest because a platform build must run underneath the data project before business value is delivered. | active | contextual | yes | |
| 09a6527f3cfb5718… | When building directly on Fabric, the customer team owns integration, security hardening, testing, and ongoing maintenance as Azure services evolve, which creates a continuing engineering burden. | active | contextual | yes | |
| 346e6655b0f11f66… | A direct Fabric build requires engineering the governance features demanded by the RFP, including fact-versus-recommendation typing, source-to-AI citations, validation, and human-in-the-loop controls, because these capabilities are not turnkey. | active | contextual | yes | |
| 89bc6c4ddf1fa79b… | Building directly on Fabric requires assembling Foundry Agent Service, an agent framework, retrieval, vector storage, a citation and provenance model, injection defenses, agent telemetry, and a custom frontend so that a complete agent platform can function. | active | contextual | yes | |
| 4fefb7e65ea6f608… | The capability ceiling of a Fabric plus Foundry plus custom build approach is high, but the key decision factor is the cost of achieving and sustaining that capability on a project that must deliver value quickly. | active | contextual | yes | |
| a59dd8cd22a09c40… | Pyrana provides a configuration-driven handoff package that includes agent configurations, knowledge sets, metric definitions, and audit trails so that Summer Fridays IT can read, review, and own the implementation as documentation-as-code. | active | contextual | yes | |
| 7abbe5f69289abfc… | Pyrana includes built-in governance features such as citations, typing, validation, HITL, telemetry, and audit capabilities so that it matches the RFP requirements line-for-line. | active | contextual | yes | |
| d73a3abd276ab0ae… | The recommended Pyrana on Fabric path is to adopt a pre-integrated, governed agentic platform that already runs on Azure and reads OneLake so that foundational platform integration is already in place. | active | contextual | yes | |
| 71a3dbb99c267683… | After assembling the direct Fabric stack, the next required step is to engineer the governance demanded by the RFP so that the custom solution meets required controls. | active | contextual | yes | |
| 6d9d9faeb6d6971f… | Building directly on Fabric requires assembling Foundry Agent Service, Agent Framework, retrieval, vector store, a citation and provenance model, injection defenses, agent telemetry, and a custom frontend as separate components. | active | contextual | yes | |
| ad46ac622e4ddb09… | The capability ceiling of a Fabric plus Foundry plus custom build approach is high, but the central evaluation question is the cost of achieving and maintaining that capability when the project must deliver value quickly. | active | contextual | yes | |
| 46033322e722367f… | On Pyrana, agent definitions, citations, controls, and tests are generated as governed artifacts rather than authored by hand so that governance documentation is system-produced and controlled. | active | contextual | yes | |
| 13169aaafd00fe44… | In the proposed hybrid model, the Fabric solution architecture, data model, and dashboards are delivered in Fabric, while the AI-agent inventory and governance approach are delivered on Pyrana. | active | contextual | yes | |
| 77203673f0a5824e… | A solution is production-ready when IT has reviewed security and governance, UAT is complete, metrics and sources are documented, dashboards reconcile to Fabric, agents have documented controls and tests, and handoff is delivered. | active | contextual | yes | |
| 96e69c02129dcea0… | Required vendor deliverables include a project roadmap, Fabric solution architecture, data-source inventory and integration plan, data model and metrics and dashboard approach, AI-agent inventory and governance, security and access-control model, data-validation and metric-definition approach, and testing through handoff materials. | active | contextual | yes | |
| d123b39876583f5a… | Pyrana makes agents governed and testable by defining each agent in versioned configuration with documented purpose, sources, access, and limits, supported by telemetry and an evaluation harness for testing and monitoring. | active | contextual | yes | |
| f50f259e70137a85… | Pyrana separates facts from recommendations by typing knowledge as fact, derived, or assumption with confidence scores so that a recommendation is never presented as a validated fact. | active | contextual | yes | |
| 5ecb8da21e7bd889… | Pyrana flags incomplete data through ambient gap detection that surfaces missing or stale knowledge before it reaches a recommendation so that downstream advice is aware of knowledge gaps. | active | contextual | yes | |
| 7c330a71a13ef6f7… | Pyrana prevents invented claims by generating claims with the model and then validating them with deterministic code against verbatim source text so that no claim is accepted without verification. | active | contextual | yes | |
| 7376e8a4335c1f6b… | Pyrana implements source traceability by making every Context Unit content-addressed with SHA-256 and attaching source, version, and approver metadata, while logging which units each agent answer used. | active | contextual | yes | |
| 6fc39c5216dc3903… | AI agents must protect against prompt injection, and strategic recommendations must be clearly distinguishable from validated business facts so that generated advice is safely separated from verified information. | active | contextual | yes | |
| 457e5eac6dfc953e… | AI agents must use only approved sources, respect permissions, and provide source traceability so that outputs remain governed and auditable. | active | contextual | yes | |
| 1a15864dd4c85cf9… | AI agents must be secure, governed, and limited to approved use cases, with each agent documented for purpose, sources, audience, access, limitations, testing, and monitoring so that governance is explicit. | active | contextual | yes | |
| 37ffec41384b8993… | Systems must never expose raw sensitive data, using Purview DLP and an output gateway that normalizes and validates every agent response while withholding raw confidential or customer-level data unless explicitly approved. | active | contextual | yes | |
| 2bf2376d76354ab2… | Data validation and reconciliation should be anchored in Fabric so that dashboards reconcile to Fabric and agent claims are validated against source text before use. | active | contextual | yes | |
| 34da270bea243d65… | Metric definitions and business logic must be maintained in Fabric or Power BI under governed Context Units so that agents and humans share one definition library and documentation remains controlled. | active | contextual | yes | |
| f1aca2ca07a0785c… | In the Pyrana-on-Fabric approach, governance is built in through citations, typing, validation, HITL, telemetry, and audit so that the platform matches the RFP line-for-line. | active | contextual | yes | |
| 9d61150863bc9011… | The recommended approach is Pyrana on Fabric, which adopts a pre-integrated, governed agentic platform that already runs on Azure and reads OneLake so that deployment starts from an existing governed foundation. | active | contextual | yes | |
| 03d8a5e84b43f17e… | A direct Fabric build must then engineer the governance demanded by the RFP after assembling the technical stack so that compliance and control requirements are met. | active | contextual | yes | |
| 8a781fa784645822… | The capability ceiling of a Fabric plus Foundry plus custom build approach is high, but the document frames the real issue as the cost of achieving and maintaining that capability under a requirement to deliver value quickly. | active | contextual | yes | |
| 1e4a4f764f94f3dd… | Because Pyrana is configuration-driven, the handoff package consists of agent configs, knowledge sets, metric definitions, and audit trails as documentation-as-code that Summer Fridays IT can read, review, and own. | active | contextual | yes | |
| 8b55bb7a5bac319a… | Every required deliverable maps cleanly to the hybrid approach, with the Fabric solution architecture, data model, and dashboards delivered in Fabric while the AI-agent inventory and governance approach are delivered on Pyrana. | active | contextual | yes | |
| a803cdb3bd1689d9… | A production-ready state is reached when IT has reviewed security and governance, UAT is complete, metrics and sources are documented, dashboards reconcile to Fabric, agents have documented controls and tests, and handoff is delivered. | active | contextual | yes | |
| 0b4925ed060066c4… | The RFP requires vendor deliverables including a project roadmap, Fabric solution architecture, data-source inventory and integration plan, data model and metrics and dashboard approach, AI-agent inventory and governance, security and access-control model, data-validation and metric-definition approach, and testing, UAT, deployment, support, and handoff. | active | contextual | yes | |
| 1ec6373721435054… | Each agent is defined in versioned configuration with documented purpose, sources, access, and limits, and telemetry plus an evaluation harness make testing and monitoring first-class capabilities. | active | contextual | yes | |
| a90cbdced418b895… | Knowledge is typed as fact, derived, or assumption and includes confidence scores so that recommendations are never presented as validated facts. | active | contextual | yes | |
| 56839354b43eb6b9… | Ambient gap-detection surfaces missing or stale knowledge before it reaches a recommendation so that incomplete data can be flagged prior to decision support. | active | contextual | yes | |
| 16c1faa727c182e1… | Claims are generated by the model but validated by deterministic code against verbatim source text so that no claim is accepted without verification. | active | contextual | yes | |
| 7c42961472a9aa11… | Every Context Unit is content-addressed using SHA-256 and carries source, version, and approver metadata, and every agent answer logs which units it used so that source traceability is preserved. | active | contextual | yes | |
| dcc7e3651289b21b… | The document states that Pyrana's architecture is most directly aligned with the AI agent requirements in this section because several listed requirements are already built into the platform rather than needing to be added. | active | contextual | yes | |
| ea56768958e42557… | AI agents must be protected against prompt injection so that malicious prompts cannot subvert approved behavior or governance controls. | active | contextual | yes | |
| fa192b336fa673b9… | AI agents must avoid inventing metrics or conclusions, and strategic recommendations must be clearly distinguishable from validated business facts so that generated outputs do not misrepresent certainty. | active | contextual | yes | |
| 5250187c41607d12… | AI agents must provide source traceability and identify when data is incomplete or unavailable so that users can assess evidence quality and data gaps. | active | contextual | yes | |
| e12d349e51ad21d6… | AI agents must use only approved sources and respect permissions so that access controls and source governance are preserved. | active | contextual | yes | |
| c251ff5825a0879f… | Each AI agent must have documented purpose, sources, audience, access, limitations, testing, and monitoring so that the agent is fully governed and reviewable. | active | contextual | yes | |
| 7b2b44f113984a16… | The RFP requires AI agents to be secure, governed, and limited to approved use cases so that their operation remains controlled and compliant. | active | contextual | yes | |
| a9d81d76a85a36f8… | Raw sensitive data must never be exposed, and an output gateway must normalize and validate every agent response while withholding raw confidential or customer-level data unless explicitly approved. | active | contextual | yes | |
| dd7e9d082fb1ae9f… | Data validation and reconciliation are strong in Fabric, and agent claims must be validated against source text before use so that outputs remain consistent with documented evidence. | active | contextual | yes | |
| 1cdb6615a30e1288… | Metric definitions and business logic are maintained in Fabric and Power BI so that dashboards can reconcile to Fabric and agents and humans can share one governed definition library. | active | contextual | yes | |
| e2584aa142f30435… | The document states that Pyrana owns the platform and its hardening, indicating vendor responsibility for maintaining and securing the platform components. | active | contextual | yes | |