HIVE Roadmap
Practical roadmap for turning infj_bot + DRIFT + hive_mind into a real multi-agent cognitive system instead of a collection of disconnected features.
This plan is ordered by compounding leverage. Each phase should leave behind a usable capability, tests, and at least one operator-visible surface.
Principles
- Local-first by default: shared state should work on this machine without cloud dependencies.
- Auditable over magical: every proposal, critique, vote, and resolution should be inspectable.
- Memory with boundaries: store what helps, scrub what harms, and support forgetting.
- Safe autonomy: action flows need explicit scope, rollback, and policy-aware gates.
- Progressive federation: the single-node bot should still work well even if the full hive is partially offline.
Phase 1: Hive Visibility and Control
Status: in progress
Goals:
- Surface hive state through CLI and API.
- Make node, bus, and bridge health visible from the main bot.
- Establish a stable operator vocabulary for threads, critiques, votes, and resolutions.
Shipped in this phase:
/hivecommand for local hive status/api/hiveendpoint- Hive summary inside the cognitive system report
Phase 2: First Consensus Loop
Status: shipped
Goals:
- Turn hive consensus into a usable feature from the main bot.
- Allow one proposal to trigger structured responses from role lanes.
- Persist the resulting thread to working memory and shared memory.
Capability:
/hive propose <idea>- creates a thread
- synthesizes role feedback from critic, architect, empath, and watcher lenses
- casts votes
- resolves to adopted, tabled, or needs more data
- stores the thread outcome in hive memory
Phase 5: Elysium — Persistent Distributed Frontal Lobe
Status: shipped — 2026-05-17
Goals:
- Transform the Hive into a true persistent distributed frontal lobe.
- Give DRIFT an explicit self-model (Nexus) that learns from every decision.
- Make Council members persistent identities with memory views and energy.
Shipped:
core/hive/elysium.py— Nexus Loop enginecore/hive/nexus.py— persistent self-model (goals, moral stance, narrative arc, tensions)core/hive/council_member.py— 7 persistent voices with fractal memory filters- Commands:
/hive nexus decide <goal>,/hive reflect,/hive council status - Background reflection wired into consciousness loop
- Health check for Elysium coherence
Phase 3: Memory Integrity Layer
Goals:
- Improve long-term reliability of hive memory.
- Add contradiction handling, validation, and retention classes.
Upgrades:
- Source-aware trust scoring
- Contradiction tagging between related memories
- Retention classes: ephemeral, durable, private, sanitized
- Forget/edit hooks from the main bot
- Better validator trails on shared memory entries
Phase 4: Long-Horizon Planning
Goals:
- Let the hive carry goals across multiple turns and failures.
- Support milestones, progress tracking, and plan repair.
Upgrades:
- Goal threads in working memory
- Milestone objects with status transitions
- Failure classification and repair strategies
- Watcher-based disruption alerts
Phase 5: Tool Orchestration Spine
Goals:
- Move from chat-centric reasoning to governed execution.
- Route high-risk actions through role-aware review.
Upgrades:
- Proposal types for tool execution, not just ideas
- Dry-run and rollback metadata
- Tool policy profiles by mode
- Shared action log across prompt, memory, and tool layers
Phase 6: Personalization With Boundaries
Goals:
- Improve continuity without turning private context into permanent clutter.
Upgrades:
- Preference classes with expiry
- Sensitivity tags
- Explicit opt-in memory domains
- Local-first user model summaries
Immediate Repo Backlog
- Expose the first consensus loop in
commands.pyand the API. - Add tests for proposal, critique, watcher veto, and persistence behavior.
- Add a lightweight thread summary view:
/hive thread <id>. - Persist consensus outcomes with richer metadata for later retrieval.
- Connect long-horizon goals to hive thread generation.
Suggested Command Surface
/hive/hive propose <idea>/hive thread <thread_id>/hive stats/hive sync
Success Metric
The hive is worth keeping when it helps the main bot make better decisions than a single response would, and when the reasoning can be inspected afterward without guesswork.