The Agential Sidekick

The Agential Sidekick is built on a robust, three-tiered architecture designed for modularity, security, and scalability within a corporate environment. This separation of concerns allows each component to be developed and managed independently.

• Core LLM Engine: Serves as the primary reasoning and language generation brain. It must be hosted securely (on-premise, private cloud, or via a compliant enterprise service) to prevent data leakage and "Shadow AI."
• Sidekick Hub: The central orchestration layer. It manages the user interface, state, personalization, and secure communication between all components. The Hub is a critical security chokepoint, requiring robust protection for user tokens and data flows.
• Integrated Applications: Enterprise systems like Microsoft Outlook and Teams act as the data sources and action platforms. The Sidekick interacts with these via secure APIs, primarily the Microsoft Graph API, using delegated permissions to ensure it only accesses what the user is authorized to.

This architecture is enveloped by a Governance & Security Layer, which includes the enterprise's existing Identity and Access Management (IAM), Data Loss Prevention (DLP), and compliance frameworks.

A core innovation of the Sidekick is its ability to adopt different personas, fundamentally changing the cognitive process it applies to a problem. This moves beyond simple tone changes to offer a suite of specialized cognitive tools, managed through meticulously crafted meta-prompts (system prompts).

This multi-persona system transforms the assistant into a metacognitive augmentation platform. A user can intentionally switch from an analytical mode to a divergent, brainstorming mode, or deploy a skeptical reviewer to challenge their own confirmation bias, actively diversifying their own thinking process.

The true power of the Sidekick is unlocked through deep, proactive integration with enterprise workflows, primarily via the Microsoft Graph API. The goal is to evolve from a reactive tool to a proactive partner.

Outlook Integration Examples:

• Semantic Email Summarization: Efficiently summarizes entire email threads by filtering via `conversationId` for performance.
• Persona-Based Drafting: Creates safe drafts in the user's "Drafts" folder for review, rather than sending emails directly.
• Automated Meeting Preparation: Proactively gathers context before meetings by retrieving attendee lists and recent documents they've worked on, providing the user with a "just-in-time" briefing.

Teams Integration Examples:

• Relevance-Based Summarization: Uses delta queries to efficiently summarize what's new in active channels without repeatedly fetching the entire history.
• Action Item Tracking: After analyzing a meeting transcript, it can automatically create tasks in Microsoft To Do or Planner and initiate follow-up chats with assignees, bridging the gap between discussion and execution.

The blueprint's most innovative proposal is Symbiotic Onboarding. This process creates a deep, personalized "Attentional Map" by analyzing the unconscious variations in a user's vocal prosody (emphasis, pacing, tone) as they read a document aloud.

This is scientifically grounded: prosody is a primary channel for conveying intent and importance. By capturing these vocal cues and using a technique called Forced Alignment to map them to specific words in the text, the system can infer the user's cognitive priorities.

The result is "Attentional RAG":

• A sophisticated Retrieval-Augmented Generation (RAG) system where retrieved information is re-ranked not just by semantic relevance, but by the user's personal "Attentional Score."
• This ensures the most subjectively important information is placed in the most salient part of the LLM's context window, mitigating the "lost in the middle" problem and leading to hyper-personalized outputs.

The concepts in this blueprint are grounded in extensive research and existing technologies. Explore some of the key sources below.

• Microsoft Graph Overview
• Persona Vectors: Controlling LLM Traits
• Disentanglement of prosodic meaning (PNAS)
• Forced Alignment - Speech & Technology
• Streamline RAG with Intelligent Metadata Filtering
• Seizing the agentic AI advantage (McKinsey)