58 lines
7.7 KiB
Markdown
58 lines
7.7 KiB
Markdown
Product Requirements Document: AI-Agent-First Knowledge Platform
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Version: 1.0
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Date: March 10, 2026
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Author: Product Team
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Status: Draft
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1. Executive Summary
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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.
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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.
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Vision Statement
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"The knowledge layer for the agentic era — where humans and AI agents create, organize, reason over, and act on knowledge together."
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Key Differentiators
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Agent-native data model — Every entity (note, task, artifact) is structured for machine readability while remaining human-friendly
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MCP-native architecture — Full Model Context Protocol support as the primary integration surface
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Knowledge graph, not folders — Semantic relationships replace hierarchical organization
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Event-driven agent orchestration — Agents react to knowledge changes in real-time
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Multi-tenant agent identity — Agents have identities, permissions, audit trails, and reputation just like human users
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2. Problem Statement
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Current Limitations
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Problem Impact
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Notes are unstructured blobs of text Agents can't reliably parse, query, or reason over content
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AI is an afterthought (chat sidebar) No deep integration; agents can't autonomously create or manage knowledge
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No agent identity system Can't track what an agent did, why, or with what authority
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API is human-UI-coupled No clean programmatic interface for agent tooling
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Flat organization (workspaces + hashtags) No semantic relationships, no knowledge graph
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Single-user mental model No agent-to-agent collaboration or delegation
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Hardcoded AI endpoints Can't swap models, providers, or agent frameworks
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Backend split across multiple services Fragile, hard to extend, inconsistent APIs
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No event/webhook system Agents can't react to changes or trigger workflows
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Block-based billing is vague No clear unit economics for agent compute
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Market Opportunity
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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.
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3. Target Users
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3.1 Human Users
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Persona Description Key Needs
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Knowledge Worker Individual using notes, tasks, research Fast capture, AI-assisted organization, search
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Team Lead Manages a team with shared workspaces Oversight of agent work, approval workflows, analytics
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Developer Builds agent workflows, integrations Clean APIs, MCP tools, webhooks, SDK
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Creator Publishes posts, builds content AI-assisted writing, scheduling, distribution
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3.2 AI Agent Users
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Persona Description Key Needs
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Research Agent Gathers, synthesizes, and stores information Read/write notes, semantic search, source linking
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Workflow Agent Orchestrates multi-step processes Task management, status updates, human-in-the-loop
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Analysis Agent Processes data and produces insights Structured output storage, chart/artifact creation
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Assistant Agent Responds to user queries using knowledge base Full-text + semantic search, context retrieval
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Integration Agent Syncs data between external systems and the platform Webhooks, CRUD APIs, bulk operations
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Monitoring Agent Watches for changes and triggers actions Event subscriptions, real-time notifications
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4. Architecture Overview
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4.1 High-Level Architecture
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┌─────────────────────────────────────────────────────────────────┐│ 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 │ ││ └──────────┘ └──────────┘ └──────────┘ └──────────────────┘ ││ ┌──────────┐ ┌─────── |