System Architecture Update
We're shipping a fundamental architectural shift in how agentic content systems manage context and workflow orchestration. After analyzing the limitations of traditional "knowledge base + scheduling" approaches, we've rebuilt our core architecture around context-as-files and task-driven workflows.
The traditional approach treats knowledge as a separate bucket from scheduling configuration. This creates fragmentation where site-level research, post-level prompts, and user-level voice samples exist in different systems with different editing models. Schedules become the primary interface, answering "when does something happen?" instead of focusing on the actual work.
Context-as-Files Implementation
Our new architecture treats all contextual information as editable files within a unified system:
Site-level context: Research data, topic lists, brand guidelines
Post-level context: Prompts, Q&A sessions, editor state
User-level context: Voice samples, personality configuration, writing examples
Each context file uses the same read/write interface. A customization prompt functions "like a CSS file," while a topic list operates as another file type. The agent uses one consistent method for accessing and modifying these files. The only difference is semantic meaning, not technical implementation.
Critical constraint: Nothing goes live until user preview and approval. This provides a single editing model with a clear gate before changes propagate to production.
Task-Driven Workflow Engine
We've replaced schedule-centric interfaces with a task board architecture: backlog → research → outline → draft → review → done. Each card represents a discrete task that agents and users move through defined stages.
Schedules now function as triggers rather than primary interfaces. They determine when work appears on the board or when users receive reminders, but the actual work lives on the task board. A published post results from one or more tasks completing, similar to shipping a feature from multiple Linear tasks.
Voice-to-Task Integration (Riffs)
Our voice interface (Riffs) integrates as a research/voice task type rather than a separate system. The Riffs interface handles scheduling: a calendar of voice sessions and session initiation.
What a Riff produces (notes, drafts) appears on the task board and in context files, not isolated on a separate results page. This maintains architectural consistency where all artifacts exist within the unified task and context system.
Two-Part Content System Implementation
The system implements a dual-phase content generation approach:
Phase 1: Idea Capture
Embedded idea documents within the application interface
Slack bot integration for idea generation from existing workflows
Automated backlogs that function like product feature requests
Phase 2: Content Development
Scheduled voice sessions (Riffs) for converting ideas to content
Task board progression from captured ideas through published posts
Context-aware agent assistance throughout the development pipeline
Technical Implementation Roadmap
Phase 1: Task-first interface with Kanban board and schedule triggers
Phase 2: Context-as-files architecture with preview/approval gates
Phase 3: Full Riffs integration as tasks with calendar-based scheduling
This architecture addresses the core problem in automated content generation: fragmented context management and schedule-driven interfaces that obscure actual work progress.
System Benefits
The unified approach eliminates context switching between different editing models while maintaining strict approval gates. Schedules become background configuration rather than primary interfaces. All work artifacts live within a consistent task and context system, enabling better agent coordination and user workflow management.
Best,
Amrutha