Product Requirements Document: AI-Agent-First Knowledge Platform

Version: 1.0
Date: March 10, 2026
Author: Product Team
Status: Draft
1. Executive Summary

This platform is being built from the ground up as an AI-agent-first knowledge platform — a system where AI agents are first-class citizens alongside human users. Every note, workspace, workflow, and API is designed to be created, consumed, and orchestrated by both humans and autonomous agents.

The current application is a capable note-taking app with workspaces, hashtags, posts, and bolted-on AI features. The rebuild inverts the paradigm: instead of a human app with AI helpers, it becomes a knowledge operating system where agents think, collaborate, and act — and humans supervise, guide, and participate.
Vision Statement

    "The knowledge layer for the agentic era — where humans and AI agents create, organize, reason over, and act on knowledge together."

Key Differentiators

    Agent-native data model — Every entity (note, task, artifact) is structured for machine readability while remaining human-friendly
    MCP-native architecture — Full Model Context Protocol support as the primary integration surface
    Knowledge graph, not folders — Semantic relationships replace hierarchical organization
    Event-driven agent orchestration — Agents react to knowledge changes in real-time
    Multi-tenant agent identity — Agents have identities, permissions, audit trails, and reputation just like human users

2. Problem Statement
Current Limitations
Problem	Impact
Notes are unstructured blobs of text	Agents can't reliably parse, query, or reason over content
AI is an afterthought (chat sidebar)	No deep integration; agents can't autonomously create or manage knowledge
No agent identity system	Can't track what an agent did, why, or with what authority
API is human-UI-coupled	No clean programmatic interface for agent tooling
Flat organization (workspaces + hashtags)	No semantic relationships, no knowledge graph
Single-user mental model	No agent-to-agent collaboration or delegation
Hardcoded AI endpoints	Can't swap models, providers, or agent frameworks
Backend split across multiple services	Fragile, hard to extend, inconsistent APIs
No event/webhook system	Agents can't react to changes or trigger workflows
Block-based billing is vague	No clear unit economics for agent compute
Market Opportunity

The AI agent ecosystem is exploding — every major LLM provider ships agent frameworks (OpenAI Agents SDK, Anthropic MCP, Google A2A, LangChain, CrewAI, AutoGen). But agents lack a persistent, shared knowledge layer that isn't just a vector database. This platform fills that gap: a place where agents store their work, share context, collaborate with humans, and build institutional knowledge.
3. Target Users
3.1 Human Users
Persona	Description	Key Needs
Knowledge Worker	Individual using notes, tasks, research	Fast capture, AI-assisted organization, search
Team Lead	Manages a team with shared workspaces	Oversight of agent work, approval workflows, analytics
Developer	Builds agent workflows, integrations	Clean APIs, MCP tools, webhooks, SDK
Creator	Publishes posts, builds content	AI-assisted writing, scheduling, distribution
3.2 AI Agent Users
Persona	Description	Key Needs
Research Agent	Gathers, synthesizes, and stores information	Read/write notes, semantic search, source linking
Workflow Agent	Orchestrates multi-step processes	Task management, status updates, human-in-the-loop
Analysis Agent	Processes data and produces insights	Structured output storage, chart/artifact creation
Assistant Agent	Responds to user queries using knowledge base	Full-text + semantic search, context retrieval
Integration Agent	Syncs data between external systems and the platform	Webhooks, CRUD APIs, bulk operations
Monitoring Agent	Watches for changes and triggers actions	Event subscriptions, real-time notifications
4. Architecture Overview
4.1 High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐│                        CLIENT LAYER                             ││                                                                 ││   ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────┐  ││   │  Web UI  │  │Mobile PWA│  │   CLI    │  │  Agent SDKs  │  ││   │ (React)  │  │  (React) │  │(Terminal)│  │ (TS/Py/Rust) │  ││   └────┬─────┘  └────┬─────┘  └────┬─────┘  └──────┬───────┘  ││        └──────────────┴─────────────┴───────────────┘          │└────────────────────────────┬────────────────────────────────────┘                             │┌────────────────────────────┼────────────────────────────────────┐│                     API GATEWAY LAYER                           ││                                                                 ││   ┌────────────────────────┴────────────────────────────────┐  ││   │              Unified REST + GraphQL API                  │  ││   │         (Auth, Rate Limiting, Agent Identity)            │  ││   └────────────────────────┬────────────────────────────────┘  ││                            │                                    ││   ┌────────────┐  ┌───────┴───────┐  ┌──────────────────┐     ││   │  MCP Server│  │  WebSocket    │  │  Webhook Engine  │     ││   │  (Tools)   │  │  (Real-time)  │  │  (Outbound)      │     ││   └────────────┘  └───────────────┘  └──────────────────┘     │└────────────────────────────┬────────────────────────────────────┘                             │┌────────────────────────────┼────────────────────────────────────┐│                      SERVICE LAYER                              ││                                                                 ││  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────┐  ││  │Knowledge │ │ Agent    │ │Workflow  │ │   AI Inference   │  ││  │ Service  │ │ Service  │ │ Engine   │ │   Gateway        │  ││  └──────────┘ └──────────┘ └──────────┘ └──────────────────┘  ││  ┌──────────┐ ┌───────