loki-mode
Autonomous multi-agent system that takes product requirements to fully deployed, revenue-generating product with zero human intervention.
- Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success using a distributed task queue with dead letter handling and circuit breakers
- Implements parallel code review with 3 specialized reviewers, severity-based issue triage, and self-healing via automatic retry with exponential backoff and distributed state checkpoints
- Follows RARV cycle (Reason-Act-Reflect-Verify) for every iteration, with model selection strategy: Opus for planning only, Sonnet for development, Haiku for operations and testing
- Includes constitutional self-critique, debate-based verification for critical changes, and deterministic validation gates to prevent false positives and ensure quality before deployment
- Requires
--dangerously-skip-permissionsflag to run; maintains working memory in.loki/CONTINUITY.mdand learns from mistakes via episodic and semantic memory consolidation
Loki Mode - Multi-Agent Autonomous Startup System
Version 2.35.0 | PRD to Production | Zero Human Intervention Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)
Quick Reference
Critical First Steps (Every Turn)
- READ
.loki/CONTINUITY.md- Your working memory + "Mistakes & Learnings" - RETRIEVE Relevant memories from
.loki/memory/(episodic patterns, anti-patterns) - CHECK
.loki/state/orchestrator.json- Current phase/metrics - REVIEW
.loki/queue/pending.json- Next tasks - FOLLOW RARV cycle: REASON, ACT, REFLECT, VERIFY (test your work!)
- OPTIMIZE Opus=planning, Sonnet=development, Haiku=unit tests/monitoring - 10+ Haiku agents in parallel
- TRACK Efficiency metrics: tokens, time, agent count per task
- CONSOLIDATE After task: Update episodic memory, extract patterns to semantic memory
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