title: Wire existing components for Claude-Antigravity coordination

insight: Create 900 lines of glue code across 5 new files to wire together existing sync tools, Agent Bus, Task Queue, Execution Layer, and MCP Server instead of building new infrastructure

reasoning_chain: Claude and Gemini agents operate in silos despite existing coordination components

reasoning_chain: All building blocks (sync tools, AgentBus, TaskQueue, ExecutionLayer, MCP Server) already exist in codebase but disconnected

reasoning_chain: Solution: MCP tools as integration surface - both agents access same capabilities

reasoning_chain: Dual-layer storage: Redis for real-time + files for offline fallback

reasoning_chain: Hooks provide frictionless automation (SessionStart auto-sync, PreToolUse lock checks)

reasoning_chain: Graceful degradation: if Redis unreachable, fallback to file-only mode

source_context: Claude Code and Google Antigravity agents both work on the Genesis project but operate in silos. All the building blocks for seamless coordination already exist in the codebase -- they're just disconnected


title: Phase 1: Task Board with dual-write persistence

insight: Create shared task board with 5 MCP tools that write to both Redis and file snapshots for offline fallback

reasoning_chain: Need shared task board both agents can access

reasoning_chain: Every write operation writes to Redis TaskQueue (real-time) AND publishes EventMessage to AgentBus

reasoning_chain: Also snapshots full board to file for offline fallback

reasoning_chain: 5 tools: create, list, update, claim, complete

source_context: task_board_create(task_json) - Create task in Redis TaskQueue + file snapshot + Redis event


title: Phase 1: Redis-backed file lock registry

insight: Create file locking system using Redis hash with 4-hour TTL to prevent both agents from editing same files

reasoning_chain: Need to prevent conflicts when both agents work simultaneously

reasoning_chain: Redis hash genesis:file_locks with key=filepath, value={agent, claimed_at, expires_at}

reasoning_chain: Also updates CLAIMED FILES section in UVS_PARALLEL_WORK.md for offline visibility

source_context: file_lock_claim(files_json, agent) - Lock files in Redis hash genesis:file_locks with 4-hour TTL


title: Phase 2: Structured handoff system

insight: Create structured auto-handoff when agent finishes work - writes to file, publishes Redis event, updates current_state.json

reasoning_chain: Need way for agents to transfer context when switching work

reasoning_chain: Handoff includes: summary, files_changed, tasks_completed, tasks_created, next_actions

reasoning_chain: Other agent can pull handoffs plus events since last sync

source_context: handoff_push(summary, files_changed_json, next_actions_json) - Write structured handoff


title: Phase 2: Session auto-sync hook

insight: Create SessionStart hook that auto-syncs Antigravity activity when Claude session starts

reasoning_chain: Claude should know what Antigravity did since last session

reasoning_chain: On session start: read last sync timestamp, filter events.jsonl for Antigravity events, read LATEST handoff, read taskboard, check file locks

reasoning_chain: Output concise summary as additionalContext

source_context: On every session start: read data/antigravity-sync/claude_last_sync.json for timestamp, filter events.jsonl for events after that timestamp from antigravity source


title: Phase 3: Execution Bridge for Claude-to-Gemini dispatch

insight: Create MCP tools that let Claude decompose tasks into RWL stories and dispatch to Gemini swarm for parallel execution

reasoning_chain: Claude should be able to leverage Gemini swarm without leaving Claude context

reasoning_chain: exec_decompose breaks task into RWL stories via GenesisExecutionLayer

reasoning_chain: exec_swarm executes stories in parallel via layer.execute_rwl_swarm()

reasoning_chain: GeminiRateMaximizer selects optimal models for each story

reasoning_chain: Results aggregate back as JSON for Claude verification

source_context: Claude -> exec_decompose(task, context) -> 5 RWL stories as JSON, Claude reviews, Claude -> exec_swarm(stories, max_parallel=3) -> GenesisExecutionLayer fires 3 parallel Gemini agents


title: Phase 4: MCP server registration for 14 new tools

insight: Register all 14 new tools in stdio_server.py following exact same pattern as existing 18 tools

reasoning_chain: stdio_server.py has TOOLS dict (schema definitions) and TOOL_HANDLERS dict (lambda dispatchers)

reasoning_chain: Need to add 14 imports, 14 TOOLS entries, 14 TOOL_HANDLERS entries

reasoning_chain: Final count verified: 32 tools = 18 original + 14 new

source_context: Add 14 new tool definitions to the TOOLS dict. Now add the 14 handler entries.


title: Phase 4: PreToolUse file lock enforcement

insight: Modify pre_tool_check.py to block Edit/Write tools when file is locked by other agent

reasoning_chain: File locking is useless if agents don't check before editing

reasoning_chain: When Edit or Write invoked, check Redis genesis:file_locks

reasoning_chain: If file locked by 'antigravity', block with message 'File locked by Antigravity agent'

reasoning_chain: If unlocked or locked by 'claude', allow

source_context: When Edit or Write tools are invoked, check Redis genesis:file_locks. If file is locked by antigravity, block with message


title: Why not use Agent Teams for this implementation

insight: Agent Teams not suitable for tightly-coupled sequential modifications requiring shared context across all files

reasoning_chain: Implementation is sequential, tightly-coupled across files that share interfaces

reasoning_chain: Each new tool module must match handler signatures in stdio_server.py

reasoning_chain: Hook must reference lock tool's helper function

reasoning_chain: Would require each teammate full context of all 7 existing files upfront

reasoning_chain: Coordination overhead exceeds benefit compared to single-agent implementation

source_context: This implementation is a sequential, tightly-coupled modification across files that share interfaces and must be cross-referenced


title: Dual-write verification confirms architecture works

insight: Verify Redis + file dual-write works by testing task board and file locks

reasoning_chain: List tasks confirms Redis + file both updated

reasoning_chain: Claim file lock - Redis hash updated with TTL

reasoning_chain: Attempt conflicting claim - correctly rejected

source_context: Task board: Redis + file dual-write verified. File locks: claim, conflict detection, status, release - all working. Redis + file dual-write confirmed.


title: Voice gap identified as critical bottleneck

insight: Without voice interface, Kinan is hindered and slow - voice is required to build Receptionist AI at speed

reasoning_chain: Kinan wants to talk to Genesis like a colleague to build ReceptionistAI

reasoning_chain: Without voice, every interaction requires typing

reasoning_chain: This creates friction and slows iterative development

reasoning_chain: Voice + browser via Conductor Protocol would enable natural conversation

reasoning_chain: Native Voice Client already exists with Gemini 2.5 Flash and vision capabilities

source_context: If I could talk to you now we could build out receptionist ai together but without voice I am like hindered and slow


type: DIRECTIVE

title: Kinan prioritizes L4 agent development

insight: Build L4 (Enterprise-tier) agent as personal digital executive - the highest autonomy level for Kinan's own use

reasoning_chain: Kinan requests L1-L4 agents but needs L4 NOW for himself

reasoning_chain: L4 = Enterprise tier - full autonomy, can execute complex multi-step plans

reasoning_chain: L4 should have voice, vision, browser use with MCP, API, memory system

reasoning_chain: This becomes priority over other agent development

source_context: I need the L4 now for myself. If I could talk to you now we could build out receptionist ai together


title: Existing voice infrastructure connects to L4 agent

insight: Conductor Protocol (voice narrates + browser acts) and Native Voice Client can power L4 agent capabilities

reasoning_chain: L4 needs voice, vision, browser use with MCP, API, memory

reasoning_chain: Conductor Protocol exists at core/conductor/engine.py - voice narrates + browser acts

reasoning_chain: Native Voice Client exists at core/voice/native_client.py - Gemini 2.5 Flash + vision

reasoning_chain: Connect these to Redis, PostgreSQL, Qdrant infrastructure already in place

reasoning_chain: Creates autonomous digital executive that executes on Kinan's behalf

source_context: Conductor Protocol (voice narrates + browser acts) — code exists at core/conductor/engine.py. Native Voice Client — code exists at core/voice/native_client.py


title: Supermemory Pro requirement blocks memory search

insight: Supermemory plugin requires Pro plan - cannot search memories through that channel

reasoning_chain: Genesis attempts to search Supermemory for past sessions

reasoning_chain: Script execution shows 'Pro plan required' error

reasoning_chain: Blocks ability to retrieve context about previous decisions

source_context: Supermemory plugin requires Pro plan — can't search through that channel


title: Shift from Agent Coordination to Agent-as-Executive

insight: Move from coordinating multiple agents to building autonomous personal executive (L4) that acts on Kinan's behalf

reasoning_chain: Previous work: wire Claude and Antigravity to work together

reasoning_chain: New direction: Kinan wants personal L4 agent he can talk to

reasoning_chain: Not just coordination - this is about voice-enabled autonomous execution

reasoning_chain: L4 should handle complex multi-step tasks with full autonomy

reasoning_chain: Receptionist AI concept: voice-first interface to all Genesis capabilities