buildpulse/docs/ROADMAP.md

BuildPulse Roadmap

Philosophy

Each version must be useful on its own. Do not build future architecture before the current manual workflow is proven.

v0.1 — Single-Project Planning Cockpit

Goal: Create the smallest useful cockpit for one project.

Includes:

• Single project summary
• Feature Plan
• Parking Lot
• Manual Pulse Log
• Markdown/JSON/JSONL export
• Local persistence

Does not include:

• AI
• Phases
• Releases
• Multiple projects
• Real agent integration

v0.2 — AI Idea Placement

Goal: Use AI to help classify new ideas.

Potential features:

• AI classify idea as Now / Next / Later / Parking Lot / Reject
• AI suggest reason for placement
• AI suggest smallest safe version
• AI detect scope creep
• AI generate acceptance criteria draft

Build only one or two AI actions first.

Highest-value first AI action:

Classify new idea and suggest placement.

v0.3 — Phases and Releases

Goal: Add structured implementation planning.

Potential features:

• Phases
• Releases
• Definition of done
• Forbidden features per release
• Required vs optional features
• Release readiness view

v0.4 — Handoff Workflow Hardening

Goal: Turn a chosen feature into a sharp, target-specific AI coding brief with as little friction as possible.

Current shipped slices:

• v0.3.1 — focused handoff shortcuts
• v0.3.2 — target-specific handoff presets
• v0.4.0 — one-tap feature handoffs
• v0.4.1 — preview/edit before copy + explicit INTENT controls

Planned slices:

• v0.4.2 — paste agent result into RESULT/BLOCKER/TEST_RESULT pulses
• v0.4.3 — session modes (30-minute, feature-based, bugfix, QA review)

Core rules:

• Keep handoff generation close to the feature decision surface.
• Include release/phase context, blockers, parking-lot warnings, and return format.
• Do not add live execution, telemetry, router logic, or agent streaming in this phase.

v0.5 — Local/Cloud AI Assistant

Goal: Start learning where local LLMs are good enough.

Potential features:

• Configure local endpoint manually
• Use local LLM for summaries
• Use local LLM for feature classification
• Track model usefulness
• Manual cloud fallback

v1.0 — Agent Pulse Ingestion MVP

Goal: Allow real agents/tools to submit Pulse events.

Potential features:

• Simple HTTP API for Pulse events
• Append-only pulse event store
• Real-time or refresh-based timeline
• Feature state updated from events
• Basic progress computation
• Safety/event validation

v1.5 — Agent Observability

Goal: Show real live-ish agent activity.

Potential features:

• Agent activity view
• Blocker/risk view
• Handoff events
• Evidence refs
• Test result parsing
• Git commit refs

v2.0 — Agent Pulse Framework

Goal: Become a real event-driven nervous system for autonomous agents.

Potential features:

• WebSocket stream
• Event subscriptions
• Agent coordination protocol
• Ownership claims
• Handoff handling
• Progress engine
• Constraints and approval gates
• Plugin system

Long-Term Vision

BuildPulse becomes the human cockpit for:

• Feature planning
• Idea maturation
• Scope control
• AI agent work visibility
• Local/cloud AI routing
• Autonomous agent observability