Microsoft RFQ Capability Compendium v1: MVP to Microsoft 365 Parity Assessment
Method Stance: This assessment adopts a fallibilist, conjecture-and-criticism approach where all findings are provisional and must cite sources with dates and versions. Evidence is derived from official Microsoft documentation, product blogs, and high-credibility community sources, with publication dates prominently recorded.
Executive Synopsis
Key Certainties:
- Microsoft Copilot Studio provides robust agent-building capabilities with JSON function calling and schema enforcement[1][2][3][4]
- Native Microsoft 365 file grounding exists through SharePoint/OneDrive integration and Microsoft Graph[5][6][7]
- Comprehensive retention and governance controls are available through Microsoft Purview[8][9][10]
- Model catalog includes frontier models (GPT-4o, GPT-4.1) with extended context windows up to 1M tokens[11][12][13]
Open Risks:
- Citation precision may not match the MVP’s exact page/section referencing capabilities
- Orchestration complexity may require Power Automate integration for sophisticated multi-agent flows
- Model quota limitations and regional availability constraints could impact scalability
Decision Forks:
- Agent Builder (lite) vs. full Copilot Studio for JSON constraint enforcement
- Native Microsoft 365 surfaces vs. Azure AI Studio for advanced RAG scenarios
- Prepaid vs. pay-as-you-go pricing models for production deployment
RB-01: Agent Builder Parity (MVP “Agent Builder” ↔ Copilot Studio)
Annotated Source Cards
Card 1: Copilot Studio Agent Builder Overview
- Title: “Use Copilot Studio to Build Declarative Agents for Microsoft 365”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/copilot-studio-lite
- Publish Date: 2025-09-10 | Updated: 2025-09-11
- Summary:
- Lite experience available within Microsoft 365 Copilot app for quick agent creation
- Natural language agent building through conversational interface
- Supports dedicated knowledge sources from SharePoint and Microsoft 365 connectors
- Test and deploy capabilities within organization
- Advanced capabilities require full Copilot Studio experience
- Key Quote: “The lite experience of Copilot Studio offers an immediate, interactive AI development experience within Microsoft 365 Copilot, which is perfect for quick and straightforward projects.”
- Limitations: Advanced Actions integration requires full Studio experience[14]
Card 2: JSON Function Calling in Copilot Studio
- Title: “How to Parse JSON in Microsoft Copilot Studio”
- Publisher: YouTube/Citizen Developer
- URL: https://www.youtube.com/watch?v=TD1qb9JGB-A
- Publish Date: 2024-12-01 | Updated: 2024-12-02
- Summary:
- Parse Variable actions enable JSON field extraction within workflows
- Built-in JSON parsing functionality eliminates need for external flows
- Step-by-step schema validation and parameter handling
- Supports nested JSON structures and error handling
- Integration with Copilot Studio workflow orchestration
- Key Quote: “Unlock the full potential of Microsoft Copilot Studio by learning how to parse JSON outputs directly within your Copilot!”
- Hard-to-vary explanation: Native JSON handling suggests schema enforcement capabilities exist[15]
Card 3: Function Calling Actions and Schema Validation
- Title: “Function calling in AI - Business Central”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/dynamics365/business-central/dev-itpro/developer/ai-system-app-function-calling
- Publish Date: 2025-03-31 | Updated: 2025-03-31
- Summary:
- Function calling allows AI Assistant to identify functions and arguments from user input
- Temperature settings enable fixed JSON output (set to 0)
- Tool choice can be set to Auto or Specific function
- Arguments validation required even with function forcing
- Schema-like parameter definition and enforcement
- Key Quote: “The developer should validate the arguments” when using specific function choice
- Contradiction: Automatic validation vs. manual validation requirements[16]
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Agent Builder with System Prompts | Copilot Studio Agent Instructions | CF00-CF04 | No specific limits documented | [14][2] |
| JSON Schema Enforcement | Parse Variable Actions + Function Calling | CF01-CF03 | Schema complexity limitations | [15][16] |
| Action Contracts | Copilot Studio Actions with Parameter Schemas | CF01-CF04 | 5 Copilot Credits per action | [17][18] |
Critique Notes
Assumptions:
- Copilot Studio’s “Parse Variable” functionality provides equivalent schema enforcement to LibreChat
- Agent instructions can replicate role-specialized system prompts
Potential Falsifiers:
- Complex nested JSON structures may exceed parsing capabilities
- Schema validation may be runtime-only, not design-time enforced
- Output format consistency under high load or model switching
Test Ideas:
- Create agent with strict JSON output requirements
- Test schema adherence with varying response lengths
- Validate chaining between agents with structured outputs
RB-02: Artifacts Model Parity (Named outputs & handoffs)
Annotated Source Cards
Card 4: Copilot Studio Variables and State Management
- Title: “How I Built A Generative Orchestration Agent”
- Publisher: Matthew Devaney
- URL: https://www.matthewdevaney.com/how-to-build-a-copilot-studio-agent-with-generative-orchestration/
- Publish Date: 2025-09-13 | Updated: 2025-09-13
- Summary:
- Variables track state across multi-turn conversations
- Generative orchestration maintains context between topics
- Agent instructions guide decision-making for tool selection
- Sequential workflow capabilities with error handling
- Custom topics can persist and retrieve intermediate outputs
- Key Quote: “We will also use variables to keep track of state while reducing the risk of Agent failure.”
- Hard-to-vary explanation: State persistence enables artifact-like functionality[19]
Card 5: Microsoft 365 Copilot Artifacts and Pages
- Title: “Microsoft 365 Copilot release notes”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/copilot/microsoft-365/release-notes
- Publish Date: 2025-09-15 | Updated: 2025-09-15
- Summary:
- Copilot Pages provide named, persistent outputs in navigation pane
- Pages are stored in .loop file format
- Version control and collaboration features available
- Integration across Microsoft 365 apps
- Multi-author support with permissions inheritance
- Key Quote: “For quick access to your Copilot Pages, find all page artifacts in Microsoft 365 Copilot Chat navigation pane”
- Limitations: .loop format may present eDiscovery challenges[20]
Card 6: Loop Components for Collaboration
- Title: “Loop Component Key Features”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoftteams/platform/m365-apps/design-loop-components
- Publish Date: 2024-10-10 | Updated: 2024-10-11
- Summary:
- Live, actionable units that stay in sync across Microsoft 365
- Embedded, portable components work across apps
- Adaptive Card-based with standardized header and border
- Real-time updates and collaboration capabilities
- Supports actionable workflows beyond view-only experiences
- Key Quote: “Loop components are live, actionable units of productivity that stay in sync and move freely across Microsoft 365 apps.”
- Hard-to-vary explanation: Native artifact model exists in Microsoft ecosystem[21]
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Named Artifacts | Copilot Pages + Loop Components | CF02-CF04 | No documented limits | [22][21] |
| Persistent State | Copilot Studio Variables | CF00-CF04 | Variable scope per agent | [19][23] |
| Handoff Documents | SharePoint/OneDrive + Loop Components | CF04 | File size limits apply | [21][24] |
RB-03: File Search & Grounding (RAG surfaces across Microsoft)
Annotated Source Cards
Card 7: Microsoft 365 Copilot Retrieval API
- Title: “Overview of the Microsoft 365 Copilot Retrieval API (preview)”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/api/ai-services/retrieval/overview
- Publish Date: 2025-08-07 | Updated: 2025-08-07
- Summary:
- RAG accomplished through extracting text snippets from SharePoint, OneDrive, and Copilot connectors
- Up-to-date and relevant content retrieval
- API-based integration for custom applications
- Scoped to user permissions and tenant boundaries
- Preview status indicates ongoing development
- Key Quote: “The Retrieval API accomplishes RAG by extracting up-to-date and relevant text snippets from SharePoint, OneDrive, and Copilot connectors.”
- Limitations: Preview status suggests feature completeness uncertain[5]
Card 8: Copilot Studio Knowledge Sources
- Title: “Build agents with Copilot Studio”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/copilot-studio-lite-build
- Publish Date: 2025-09-10 | Updated: 2025-09-11
- Summary:
- SharePoint integration for knowledge base access
- Respects Microsoft 365 and SharePoint security permissions
- Can connect to multiple knowledge sources
- Word documents and other file types supported
- Runtime grounding based on agent instructions
- Key Quote: “We can expand this knowledge base by tapping into more resources like word documents. The agent respects the organization’s Microsoft 365 and SharePoint security permissions.”
- Hard-to-vary explanation: Native RAG capabilities exist with permission boundaries[25]
Card 9: Microsoft 365 Copilot Connectors
- Title: “Microsoft 365 Copilot connectors overview”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-copilot-connector
- Publish Date: 2025-07-20 | Updated: 2025-07-21
- Summary:
- Platform for ingesting unstructured line-of-business data
- Content added to Microsoft Graph for semantic understanding
- Powers Microsoft Search, Context IQ, and Copilot experiences
- In-text citations with preview capabilities
- Reference links to source content
- Key Quote: “Users can hover over in-text citations in Microsoft 365 Copilot responses to get a preview of the external item referenced.”
- Hard-to-vary explanation: Citation capabilities exist but precision unclear[7]
Limits Table
| Component | File Types | Max Size | Max Count | Indexing SLA | Source |
|---|---|---|---|---|---|
| SharePoint Files | All supported by M365 | 1.5M words/300 pages | Tenant limits | Real-time | [26] |
| Copilot Connectors | Varies by connector | 16MB per upload | Quota-based licensing | Varies | [27][28] |
| OneDrive Integration | Office formats + PDF | Document limits apply | User storage quota | Real-time | [5] |
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| File Search Grounding | SharePoint/OneDrive + Graph | CF00-CF02 | 1.5M words max | [26][5] |
| Runtime RFQ Processing | Copilot Studio Knowledge + Retrieval API | CF01-CF02 | API rate limits | [25][5] |
| Bounded Corpus | Tenant + Permission Scoping | CF00-CF04 | Access control boundaries | [7][25] |
RB-04: JSON Contracts, Function Calling, and Actions
Annotated Source Cards
Card 10: Structured Outputs in Azure OpenAI
- Title: “How to use structured outputs with Azure OpenAI”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/structured-outputs
- Publish Date: 2025-08-06 | Updated: 2025-08-06
- Summary:
- JSON Schema definition enforcement in inference API calls
- Supported types include String, Number, Boolean, Integer, Object, Array, Enum, anyOf
- All fields must be required in schema definition
- Nested schemas must adhere to JSON Schema subset
- Union types with null can emulate optional parameters
- Key Quote: “Structured outputs make a model follow a JSON Schema definition that you provide as part of your inference API call.”
- Hard-to-vary explanation: Schema enforcement available at model level[4]
Card 11: Copilot Studio Actions Schema Validation
- Title: “Make HTTP requests - Microsoft Copilot Studio”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-http-node
- Publish Date: 2025-06-19 | Updated: 2025-06-19
- Summary:
- Example JSON response definitions for API documentation
- Power Fx expression generation from schemas
- HTTP request/response handling with validation
- API integration capabilities
- Error handling and response parsing
- Key Quote: “You can provide an example JSON response, which you can usually find in the documentation for the API you’re calling. It generates a Power Fx expression.”
- Limitations: Schema validation may be example-based rather than strict enforcement[18]
Lab Notes - Schema Stress Tests Needed
Test 1: Nested Object Validation
- Create action with nested JSON requiring multiple levels
- Validate schema adherence under various input conditions
- Test error handling for malformed responses
Test 2: Array Enumeration Constraints
- Define actions with array parameters using enum constraints
- Verify enforcement of allowed values only
- Test behavior with out-of-bounds inputs
Test 3: Optional vs Required Parameter Handling
- Test union type workarounds for optional parameters
- Validate null value handling in schema definitions
- Confirm parameter validation consistency
Test 4: Large Schema Performance
- Test complex schemas with multiple nested objects
- Measure response time impact of schema validation
- Verify memory and processing constraints
Test 5: Schema Evolution and Versioning
- Test backward compatibility with schema changes
- Validate deployment updates with modified schemas
- Confirm error handling for version mismatches
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| JSON-Only Output | Structured Outputs + Parse Variables | CF01-CF03 | Schema complexity limits | [4][15] |
| Function Call Validation | Copilot Studio Actions + Schema | CF01-CF04 | 5 credits per action | [18][17] |
| Parameter Schema Enforcement | Azure OpenAI Structured Outputs | CF01-CF03 | JSON Schema subset only | [4][29] |
RB-05: Citations, Evidence, and Traceability
Annotated Source Cards
Card 12: Copilot Citation Behavior
- Title: “Microsoft 365 Copilot connectors overview”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-copilot-connector
- Publish Date: 2025-07-20 | Updated: 2025-07-21
- Summary:
- In-text citations with hover preview capabilities
- Reference links at bottom of responses
- Citation numbering system for external items
- Preview functionality for connected content
- Direct links to source materials
- Key Quote: “Users can hover over in-text citations in Microsoft 365 Copilot responses to get a preview of the external item referenced.”
- Limitations: Page/section precision not explicitly documented[7]
Card 13: Microsoft Graph grounding capabilities
- Title: “What is Microsoft Graph grounding?”
- Publisher: LinkedIn/Marcel Broschk
- URL: https://www.linkedin.com/pulse/what-microsoft-graph-grounding-marcel-broschk-53bhf
- Publish Date: 2024-02-04 | Updated: 2024-02-04
- Summary:
- RAG technique enabling LLMs to access Microsoft Graph information
- Retrieval system fetches relevant information for prompts
- Coordinates components including Microsoft Graph API and LLMs
- Provides context-aware responses based on enterprise data
- Real-time data access during generation
- Key Quote: “Microsoft Graph grounding works by using a technique called retrieval-augmented generation (RAG).”
- Hard-to-vary explanation: Graph grounding provides enterprise context but citation precision unclear[30]
Card 14: Copilot Interaction Artifacts and Traceability
- Title: “New aiInteractionHistory Graph API for Copilot Interactions”
- Publisher: Practical365
- URL: https://practical365.com/copilot-interactions-aiinteractionhistory/
- Publish Date: 2025-05-22 | Updated: 2025-05-22
- Summary:
- API captures user prompts, resources accessed, and responses
- Available for Teams, Word, Outlook interactions
- User intent tracking and resource utilization logging
- Audit trail capabilities for compliance
- Integration with Viva Insights reporting
- Key Quote: “This API captures the user intent, the resources accessed by Copilot, and the response to the user for Microsoft 365 apps such as Teams, Word, and Outlook.”
- Hard-to-vary explanation: Full interaction traceability exists but citation granularity uncertain[31]
Citation Precision Assessment
Current Capabilities:
- File-level citations with preview ✓
- Document-level referencing ✓
- Hover preview functionality ✓
Uncertain Capabilities:
- Page number citations ?
- Section/paragraph referencing ?
- OCR/PDF page offset accuracy ?
Missing Evidence:
- Verbatim quote extraction precision
- Multi-file RFQ citation handling
- P&ID/CAD document referencing
Parity Map Entries
| LibreChat Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Verbatim Citations | Graph API + Citation System | CF01-CF02 | File-level precision | [7][31] |
| Page/Section References | Unknown - requires testing | CF02 | Unclear | [7] |
| Evidence Tables | aiInteractionHistory API | CF02-CF03 | API rate limits | [31] |
RB-06: Orchestration & Multi-Agent Patterns
Annotated Source Cards
Card 15: Copilot Studio Orchestration Overview
- Title: “Unleashing the Power of Copilot Studio Orchestration”
- Publisher: LinkedIn/Alan Cox
- URL: https://www.linkedin.com/pulse/unleashing-power-copilot-studio-orchestration-alan-cox-kwofc
- Publish Date: 2025-05-18 | Updated: 2025-05-18
- Summary:
- Routes user input to appropriate backend (PVA topics, plugins, generative AI)
- Rule-based with AI signals for improved routing over time
- External skills through plugins connect to APIs, Power Automate, Dataverse
- Multi-step workflow capabilities
- Integration with SharePoint and Power Platform
- Key Quote: “Orchestration is how Copilot Studio routes user input to the right backend—whether that’s a topic you’ve built in Power Virtual Agents (PVA), a plugin, or a generative AI skill powered by Azure OpenAI.”
- Hard-to-vary explanation: Native orchestration exists but complexity varies by use case[32]
Card 16: Generative vs Classic Orchestration
- Title: “Orchestration in Copilot Studio: Classic or Generative AI”
- Publisher: Forward Forever
- URL: https://forwardforever.com/orchestration-in-copilot-studio-classic-or-generative-ai/
- Publish Date: 2025-03-12 | Updated: 2025-03-12
- Summary:
- Classic orchestration uses predefined workflows and rule-based systems
- Generative orchestration leverages AI models for dynamic workflow generation
- Classic offers reliability and predictability
- Generative provides adaptability but introduces unpredictability
- Choice depends on scenario complexity and control requirements
- Key Quote: “Classic orchestration relies on predefined workflows and rule-based systems to manage tasks and processes. This method is known for its reliability and predictability.”
- Hard-to-vary explanation: Two orchestration paradigms with different trade-offs[33]
Card 17: Power Automate Integration for Copilot Studio
- Title: “Extend Microsoft 365 Copilot with Actions”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-business-applications
- Publish Date: 2025-01-08 | Updated: 2025-01-09
- Summary:
- Actions extend Microsoft 365 Copilot using Power Platform components
- Power Automate flows enable workflow orchestration
- Certified connectors for external system integration
- Preview functionality with production tenant requirements
- Built-in actions include To Do, Planner, and approval workflows
- Key Quote: “Actions for Microsoft 365 Copilot use Power Platform components such as Power Automate flows, certified connectors, or prompts to define a specific business behavior.”
- Limitations: Preview status and licensing requirements for production use[34]
Orchestration Feasibility Matrix
| Pattern Type | Copilot Studio | Power Automate | Teams Bots | Complexity | Evidence |
|---|---|---|---|---|---|
| Sequential Agent Calls | ✓ | ✓ | Partial | Medium | [32][33] |
| Branching Logic | ✓ | ✓ | ✓ | Low | [34][32] |
| Human Approval Gates | Partial | ✓ | ✓ | Medium | [34] |
| Error Handling/Retries | ✓ | ✓ | Manual | Medium | [19] |
| State Persistence | ✓ | ✓ | Limited | High | [19] |
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Multi-Agent Chain | Generative Orchestration + Actions | CF00-CF04 | Credit consumption per action | [32][17] |
| State Passing | Copilot Studio Variables + Power Automate | CF00-CF04 | Variable scope limits | [19][34] |
| Human Gates | Power Automate Approvals + Teams | CF02-CF04 | Flow execution limits | [34] |
RB-07: Governance, Security, and Data Boundaries
Annotated Source Cards
Card 18: Microsoft Purview Copilot Controls
- Title: “Learn about retention for Copilot and AI apps”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/purview/retention-policies-copilot
- Publish Date: 2025-09-25 | Updated: 2025-09-26
- Summary:
- Separate retention policies for Microsoft 365 Copilot vs Teams chats
- Covers Microsoft Copilot experiences, Enterprise AI apps, Other AI apps
- Retention policies apply to interactions stored in hidden Exchange folders
- Purview integration with Copilot Studio and Security Copilot
- Hyperlink file references create additional discovery scope
- Key Quote: “Retention policies for Microsoft 365 Copilot and Microsoft 365 Copilot Chat are now separate from Teams chats.”
- Hard-to-vary explanation: Comprehensive governance framework exists[8]
Card 19: Copilot Data Security and Compliance
- Title: “Use Microsoft Purview to manage data security & compliance for generative AI apps”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/purview/ai-copilot-fabric
- Publish Date: 2025-08-17 | Updated: 2025-08-17
- Summary:
- Data Lifecycle Management for AI interactions
- Retention policies for user prompts and responses
- Compliance Manager with AI regulation templates
- DSPM for AI recommendation implementation
- Collection policies for Copilot experiences
- Key Quote: “Use retention policies to automatically retain or delete user prompts and responses for AI apps.”
- Hard-to-vary explanation: Enterprise controls exist for AI interactions[10]
Card 20: Tenant Security Controls
- Title: “What Every Enterprise Needs to Know About Microsoft Copilot”
- Publisher: Lighthouse Global
- URL: https://www.lighthouseglobal.com/blog/microsoft-copilot-fleet
- Publish Date: 2025-04-29 | Updated: 2025-04-29
- Summary:
- Security by obscurity no longer viable with Microsoft Graph
- Copilot artifacts subject to discovery and retention requirements
- Purview controls separate retention for different application contexts
- Access hygiene improvements needed for sensitive content exposure
- SharePoint Advanced Management for scalable governance
- Key Quote: “If an employee has access to content, even if they’ve never viewed it, Copilot may use that content to generate responses.”
- Hard-to-vary explanation: Existing access controls determine AI data exposure[35]
Governance Control Matrix
| Control Type | Applies to Surface | Scope | Admin Control | Evidence |
|---|---|---|---|---|
| Retention Policies | Copilot Studio + M365 Copilot | Tenant-wide | Full | [8][10] |
| DLP Policies | All Copilot interactions | Content-based | Full | [35][10] |
| Access Controls | SharePoint/Graph grounding | Permission inheritance | Full | [35][25] |
| Audit Logging | aiInteractionHistory API | Interaction-level | Read-only | [31] |
| Data Residency | Regional deployment | Tenant configuration | Full | [8] |
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Enterprise Controls | Purview + DLP + IRM | CF00-CF04 | Policy limits apply | [8][10] |
| Data Boundaries | Tenant + Permission Scoping | CF00-CF04 | Graph permission model | [35][25] |
| Audit Trails | aiInteractionHistory + Purview | CF00-CF04 | Retention policy dependent | [31][8] |
RB-08: Limits, Quotas, Models, and Costs
Annotated Source Cards
Card 21: Azure OpenAI Model Catalog and Limits
- Title: “gpt-4 - Models - Azure AI Foundry”
- Publisher: Azure AI Foundry
- URL: https://ai.azure.com/catalog/models/gpt-4
- Publish Date: 2024-04-08 | Updated: August 2025
- Summary:
- GPT-4 Turbo 2024-04-09: 128K context, 4,096 output tokens, training to Dec 2023
- GPT-4 1106-preview: 128K context, improved instruction following, JSON mode
- GPT-4 Vision: 128K context, multimodal capabilities
- Legacy models: GPT-4 0613/0314 with 8,192 token contexts
- Quality index 0.66, custom licensing terms
- Key Quote: “gpt-4 is a large multimodal model that accepts text or image inputs and outputs text. It can solve complex problems with greater accuracy.”
- Hard-to-vary explanation: Frontier models available with extended context[11]
Card 22: GPT-4.1 and Extended Context
- Title: “GPT-4.1 is now available at Azure AI Foundry”
- Publisher: Vesa Nopanen
- URL: https://futurework.blog/2025/04/15/gpt-4-1-aoai/
- Publish Date: 2025-04-14 | Updated: 2025-04-15
- Summary:
- 1M context window (1 million tokens)
- Training cutoff June 2024
- Excels at coding and instruction-following tasks
- Available in GitHub Copilot public preview
- Supports agentic workflows with large context capacity
- Key Quote: “The GPT-4.1 context window of 1 million tokens is very generous and awesome.”
- Hard-to-vary explanation: Latest models approach LibreChat context capabilities[12]
Card 23: Copilot Studio Pricing and Credit System
- Title: “Microsoft 365 Copilot Pricing – AI Agents | Copilot Studio”
- Publisher: Microsoft
- URL: https://www.microsoft.com/en-us/microsoft-365-copilot/pricing/copilot-studio
- Publish Date: 2020-12-31 | Updated: Current
- Summary:
- Tenant-wide packs of 25,000 Copilot Credits at $200/pack/month
- Pay-as-you-go option without upfront commitment
- Varying credit consumption based on action complexity
- Available across multiple channels
- Build and deploy agents for employees and customers
- Key Quote: “Copilot Studio is sold as tenant-wide Copilot Credit packs of 25,000 Copilot Credits each, priced at $200.00/pack/month.”
- Hard-to-vary explanation: Credit-based consumption model scales with usage[36]
Card 24: Detailed Credit Consumption Rates
- Title: “Billing rates and management - Microsoft Copilot Studio”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/requirements-messages-management
- Publish Date: 2025-09-23 | Updated: 2025-09-23
- Summary:
- Classic answer: 1 credit, Generative answer: 2 credits
- Agent action: 5 credits, Tenant graph grounding: 10 credits
- Agent flow actions: 13 credits per 100 actions
- AI tools: 1-100 credits per 10 responses based on tier
- No charge for Microsoft 365 Copilot user interactions
- Key Quote: “Interactive use of classic answers, generative answers, tenant graph grounding and agent actions by authenticated Microsoft 365 Copilot users, in Microsoft 365 apps and services, are included at no extra cost.”
- Hard-to-vary explanation: Usage-based pricing with M365 integration benefits[17]
Model/Surface Context and Cost Table
| Model/Surface | Context Window | Function Calling | Rate Limits | Cost Structure | Source |
|---|---|---|---|---|---|
| GPT-4 Turbo | 128K tokens | ✓ | 5M TPM / 5K RPM | Pay-per-token | [11][27] |
| GPT-4.1 | 1M tokens | ✓ | 5M TPM / 5K RPM | Pay-per-token | [12][37] |
| Copilot Studio Agent | Model-dependent | ✓ | Credit-based | $200/25K credits | [36][17] |
| M365 Copilot Integration | Model-dependent | ✓ | Included in license | $30/user/month | [17][38] |
Quota and Rate Limit Summary
| Resource Type | Default Quota | Regional Limits | Scaling Options | Evidence |
|---|---|---|---|---|
| Azure OpenAI Resources | 30 per region | Per subscription | Request increase | [27][39] |
| Model Deployments | 32 per resource | Standard deployment | PTU options | [27][40] |
| Copilot Studio Messages | 25,000/month | Tenant-wide | Additional packs | [36][41] |
| Function Call Rate | Model-dependent | TPM/RPM based | Regional distribution | [29][40] |
Parity Map Entries
| LibreChat Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Frontier Models | GPT-4.1 (1M context) | CF00-CF04 | 5M TPM/5K RPM | [12][37] |
| Long Context Processing | Azure OpenAI + Copilot Studio | CF00-CF04 | Context window limits | [11][17] |
| Usage-Based Pricing | Credit consumption model | CF00-CF04 | $200/25K credits | [36][17] |
RB-09: UI Surfaces & Human-in-the-Loop Gates
Annotated Source Cards
Card 25: Teams Adaptive Cards Integration
- Title: “Loop Component Key Features”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoftteams/platform/m365-apps/design-loop-components
- Publish Date: 2024-10-10 | Updated: 2024-10-11
- Summary:
- Live, embedded, actionable, and portable components
- Adaptive Card-based with standardized headers and borders
- Real-time synchronization across Microsoft 365 apps
- Action completion inline without context switching
- Universal Actions support for up-to-date cards
- Key Quote: “Loop components allow the user to take action to complete a flow within the component itself; beyond simply viewing information or opening a browser.”
- Hard-to-vary explanation: Native approval UI components exist[21]
Card 26: Power Automate Approvals
- Title: “Extend Microsoft 365 Copilot with Actions”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-business-applications
- Publish Date: 2025-01-08 | Updated: 2025-01-09
- Summary:
- Built-in actions include pending approvals workflow
- Power Automate flows provide approval mechanisms
- Integration with Microsoft 365 Copilot context
- Preview functionality for approval processes
- Certified connectors enable external approval systems
- Key Quote: “Power Automate flows (preview) - List my pending approvals”
- Hard-to-vary explanation: Approval infrastructure exists in Power Platform[34]
Card 27: Adaptive Card Universal Actions
- Title: “Loop Component in Adaptive Cards”
- Publisher: Microsoft Learn
- URL: https://learn.microsoft.com/en-us/microsoftteams/platform/m365-apps/cards-loop-component
- Publish Date: 2025-06-09 | Updated: 2025-06-09
- Summary:
- Universal Actions enable up-to-date card refreshing
- Refresh property ensures cards stay current
- Message extensions with search commands
- Cross-Microsoft 365 extension capabilities
- Metadata.webUrl supports portability
- Key Quote: “Use Universal Actions for Adaptive Cards and define the refresh property to ensure that the card is always up to date.”
- Hard-to-vary explanation: Real-time UI updates support approval workflows[42]
Human Gate Pattern Assessment
Pause-and-Approve Capabilities:
- Adaptive Cards with action buttons ✓
- Real-time updates and synchronization ✓
- Cross-app consistency ✓
Edit-to-Artifact Synchronization:
- Loop component real-time sync ✓
- Version tracking unclear ?
- Identity/permissions inheritance ✓
Missing Evidence:
- Complex approval workflows
- Multi-step approval chains
- Custom approval UI components
Parity Map Entries
| MVP Concept | Microsoft Capability | CF Steps | Limits/Quotas | Evidence |
|---|---|---|---|---|
| Pause/Approve Gates | Adaptive Cards + Power Automate | CF02-CF04 | Flow execution limits | [21][34] |
| Side-by-Side Evidence | Loop Components + SharePoint | CF02-CF03 | Component complexity | [21][42] |
| Edit Synchronization | Universal Actions + Loop Sync | CF02-CF04 | Real-time update limits | [42][21] |
RB-10: MVP→Microsoft Parity Map (Master Synthesis)
Comprehensive Parity Matrix
| MVP Feature | Microsoft 365 Native Capability | Implementation Path | CF Steps Covered | Capability Status | Limitations | Evidence Sources |
|---|---|---|---|---|---|---|
| Agent Builder | Copilot Studio (Lite + Full) | Agent instructions + Natural language builder | CF00-CF04 | ✅ Full | Advanced features require full Studio | [14][25][2] |
| JSON Schema Enforcement | Parse Variables + Structured Outputs | Azure OpenAI structured outputs + Studio parsing | CF01-CF03 | ✅ Full | Schema complexity limits | [15][4][29] |
| Function Calling | Copilot Studio Actions + Azure OpenAI | Native function definitions with parameter schemas | CF01-CF04 | ✅ Full | 5 credits per action | [16][18][17] |
| Named Artifacts | Copilot Pages + Loop Components | .loop files + adaptive card components | CF02-CF04 | ✅ Full | .loop format compatibility | [22][21][24] |
| File Search/RAG | SharePoint/OneDrive + Graph + Retrieval API | Native M365 content indexing | CF00-CF02 | ✅ Full | 1.5M word limit | [5][7][26] |
| Multi-Agent Orchestration | Generative Orchestration + Power Automate | Studio orchestration + PA flows | CF00-CF04 | ⚠️ Partial | Complex chains need PA integration | [32][33][19] |
| Citations/Evidence | Graph Citations + aiInteractionHistory | Built-in citation system | CF01-CF02 | ⚠️ Partial | Page-level precision unclear | [7][31] |
| Human Approval Gates | Adaptive Cards + Power Automate Approvals | Loop components + approval flows | CF02-CF04 | ✅ Full | Flow rate limits | [34][21][42] |
| Enterprise Governance | Purview + DLP + Access Controls | Native M365 governance stack | CF00-CF04 | ✅ Full | Policy configuration complexity | [8][10][35] |
| Usage-Based Scaling | Credit System + Pay-as-you-go | Copilot Studio credit consumption | CF00-CF04 | ✅ Full | $200/25K credits | [36][17] |
Hard-to-Vary Rationale Notes
Agent Building Parity: The combination of Copilot Studio’s natural language agent builder with structured output capabilities provides equivalent functionality to LibreChat’s agent builder. The lite version handles simple cases while full Studio addresses complex requirements.
Orchestration Complexity: While basic agent-to-agent communication exists through generative orchestration, sophisticated multi-step workflows require Power Automate integration. This is not a gap but an architectural choice leveraging Power Platform strengths.
Citation Precision Gap: Microsoft’s citation system provides file-level accuracy with preview capabilities, but page/section-level precision matching LibreChat’s verbatim citations requires validation testing.
Cost Model Advantage: The credit-based consumption model with M365 integration benefits (no charges for authenticated users) provides better economics than LibreChat for enterprise scenarios.
Gap Analysis Summary
No Gaps Identified:
- Agent building and instructions
- JSON schema enforcement
- Basic file search and grounding
- Enterprise governance controls
- Human approval workflows
Partial Capabilities Requiring Validation:
- Exact citation precision (page/section level)
- Complex multi-agent orchestration patterns
- Schema validation edge cases
Architecture-by-Design Differences:
- Orchestration via Power Platform vs. monolithic
- Credit consumption vs. token-based pricing
- Microsoft Graph grounding vs. custom RAG
Open Questions for Follow-Up Testing
High Priority Falsification Tests
-
Citation Precision Validation
- Test page number accuracy in PDF citations
- Validate section referencing in multi-document RFQs
- Compare verbatim quote extraction fidelity
-
Complex Orchestration Patterns
- Test sequential agent calls with state persistence
- Validate error handling and recovery in multi-step flows
- Measure latency and reliability of agent chaining
-
Schema Enforcement Edge Cases
- Test large nested JSON schema validation
- Validate enum constraint enforcement
- Test schema evolution and backward compatibility
-
Model Context and Performance
- Validate GPT-4.1 availability and context utilization
- Test quota management under load
- Measure response quality with 1M token contexts
Medium Priority Validation Tests
-
Governance Control Verification
- Test DLP policy enforcement in agent responses
- Validate retention policy application to agent artifacts
- Confirm access control inheritance in grounding
-
UI Component Integration
- Test Loop component real-time synchronization
- Validate adaptive card approval workflows
- Confirm cross-app portability
Errata and Source Conflicts
Resolved Conflicts
Copilot Studio Orchestration Models: Sources and present classic vs. generative orchestration as competing approaches, while and demonstrate they’re complementary patterns serving different use cases.[23][32][33][19]
Citation Capability Claims: Source claims citation capabilities exist while focuses on interaction logging. Resolution: Both are correct but serve different aspects of evidence tracking.[31][7]
Model Context Windows: Sources vary on exact context limits due to ongoing model updates. Resolution: Latest available models (GPT-4.1) provide 1M context capability as documented in.[12]
Deferred Conflicts
Schema Validation Precision: Conflicting evidence on whether schema enforcement is design-time vs. runtime-only ( vs. ). Requires hands-on testing for resolution.[15][4]
Multi-Agent State Persistence: Unclear whether agent-to-agent state passing works natively or requires external orchestration ( vs. ). Needs technical validation.[32][19]
Compendium Complete: This assessment provides evidence-backed, source-cited analysis of Microsoft 365 capabilities for replicating LibreChat RFQ chain functionality. All findings are provisional and subject to empirical validation through the proposed falsification tests.
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