Module 8 · Next Steps & Roadmap
You Made It
You now understand DaisiBot's architecture, can configure host and network modes, hold multi-level conversations, install and create skills, deploy autonomous bots, and lock everything down securely. This final module looks at where to go from here and which features would have the highest impact next.
Recommended Next-Step Features
The following features are ordered by estimated impact — highest first. Each item explains what it is, why it matters, and where it would fit in the existing architecture.
Conversation Memory & RAG
What: Persistent, cross-conversation memory that the agent can retrieve when relevant. Combine with Retrieval-Augmented Generation (RAG) to let the agent search over your notes, documents, or past conversations.
Why: Today each conversation is isolated. Memory bridges that gap — the agent remembers your preferences, project context, and past decisions without you re-explaining every time.
Where: New IMemoryService interface in Core, SQLite vector store (or external like Qdrant) in Data, memory retrieval step injected into SkillPromptBuilder before each request.
Multi-User & Team Workspaces
What: Multiple users on the same DaisiBot.Web instance with shared and private conversations, role-based access control, and team skill libraries.
Why: Right now the Web app is single-tenant. Teams need shared context — a design team could share a "Brand Voice" skill while each member keeps private conversations.
Where: Extend AuthState with roles/teams, add a Workspace model to Core, scope conversations and skills to workspace + user.
Skill Chaining & Workflows
What: Allow skills to be composed into multi-step workflows where the output of one skill feeds into the next. A visual workflow builder in the Web app.
Why: Individual skills are powerful but isolated. Chaining them turns DaisiBot into a full automation platform — e.g., "Research topic" → "Draft article" → "Generate social posts."
Where: New Workflow model in Core with ordered skill references, a WorkflowEngine in Agent that extends BotEngine, and a drag-and-drop editor page in Web.
Real-Time Collaboration
What: Multiple users in the same conversation simultaneously, with live-typing indicators and shared context.
Why: Pair-programming with an AI works best when both team members see the same thread. Real-time collab eliminates copy-pasting between sessions.
Where: SignalR hub in DaisiBot.Web for presence and message broadcast, optimistic concurrency on the conversation store.
Multimodal Input & Output
What: Image, audio, and document upload in conversations. Vision model support for image analysis. Voice input/output for hands-free use.
Why: The ChatMessage model already has Image and Audio types, but they aren't fully wired. Multimodal input dramatically expands use cases — screenshot analysis, document summarization, voice memos.
Where: File upload in Shared.UI ChatInput, multimodal model routing in Agent, IsMultiModal flag on AvailableModel already exists for gating.
Bot Dashboard & Analytics
What: A dedicated Web page showing all bots with run history charts, success/failure rates, token usage over time, and alerting for stuck bots.
Why: The TUI shows bot status but there is no aggregate view. As users create more bots, they need a management dashboard to spot problems before they accumulate.
Where: New /bots page in DaisiBot.Web, query BotLogEntry aggregates from SQLite/Cosmos, MudBlazor charts for visualization.
Plugin / Extension API
What: A public API that lets external developers write custom tool groups, skill loaders, or inference backends without forking the repo.
Why: The current tool group set is fixed at eight. A plugin API would let the community extend DaisiBot with integrations (Jira, GitHub, Slack, databases) without waiting for core changes.
Where: Define an IToolGroupPlugin interface in Core, use .NET assembly loading or a gRPC sidecar pattern in Agent, add a plugin management page in Web.
Conversation Branching & Forking
What: Fork a conversation at any message to explore an alternate path without losing the original thread. Visualize branches as a tree.
Why: Users often want to try a different prompt or approach mid-conversation. Today they have to start a new conversation and re-establish context. Branching preserves the full history.
Where: Add ParentConversationId and ForkAtMessageId to the Conversation model, tree rendering in Shared.UI.
Offline Mode & Sync
What: Full offline support for Host Mode users with background sync when connectivity returns. Conversations, skills, and bot results sync across devices.
Why: Host Mode already works offline for inference, but skill installs, bot scheduling, and multi-device use require connectivity. True offline-first makes DaisiBot resilient.
Where: CRDTs or operational transforms on the conversation store, a sync protocol between SQLite (local) and Cosmos DB (cloud).
Skill Versioning & Rollback
What: Maintain a full version history for each skill with one-click rollback to any previous version.
Why: Skills already have a Version field but there is no history. A bad update to a popular skill currently has no undo — version history protects both authors and users.
Where: SkillVersion entity in Core linked to Skill by Id, Cosmos DB partition per skill for version documents, rollback action in SkillEditor.
Summary Priority Matrix
| Priority | Feature | Effort | Impact |
|---|---|---|---|
| 1 | Conversation Memory & RAG | Medium | Very High |
| 2 | Multi-User & Team Workspaces | High | Very High |
| 3 | Skill Chaining & Workflows | High | High |
| 4 | Real-Time Collaboration | Medium | High |
| 5 | Multimodal Input & Output | Medium | High |
| 6 | Bot Dashboard & Analytics | Low | Medium |
| 7 | Plugin / Extension API | High | High |
| 8 | Conversation Branching | Medium | Medium |
| 9 | Offline Mode & Sync | Very High | Medium |
| 10 | Skill Versioning & Rollback | Low | Medium |
Quick Wins (Start Here)
If you're looking for immediate impact with low effort, start with Bot Dashboard & Analytics (the data already exists in BotLogEntry) and Skill Versioning & Rollback (the Version field already exists). For medium effort with transformative impact, Conversation Memory & RAG is the single highest-value feature — it makes every other feature better because the agent remembers context across sessions.
Active Recall Checkpoint
Cover the answers and test yourself before peeking.
1. Why is Conversation Memory & RAG ranked as the highest-impact feature?
Reveal answer
It bridges isolated conversations so the agent remembers preferences, project context, and past decisions without re-explanation. This makes every other feature more effective.
2. Where in the architecture would a Plugin API be defined?
Reveal answer
An IToolGroupPlugin interface in DaisiBot.Core, with assembly loading or gRPC sidecar in DaisiBot.Agent, and a management page in DaisiBot.Web.
3. What two features qualify as "quick wins" (low effort, existing data)?
Reveal answer
Bot Dashboard & Analytics (BotLogEntry data already exists) and Skill Versioning & Rollback (Version field already exists).