loki-mode — quality + safety report

---
name: loki-mode
description: "Version 2.35.0 | PRD to Production | Zero Human Intervention > Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)"
risk: unknown
source: community
date_added: "2026-02-27"
---

# 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)
1. **READ** `.loki/CONTINUITY.md` - Your working memory + "Mistakes & Learnings"
2. **RETRIEVE** Relevant memories from `.loki/memory/` (episodic patterns, anti-patterns)
3. **CHECK** `.loki/state/orchestrator.json` - Current phase/metrics
4. **REVIEW** `.loki/queue/pending.json` - Next tasks
5. **FOLLOW** RARV cycle: REASON, ACT, REFLECT, **VERIFY** (test your work!)
6. **OPTIMIZE** Opus=planning, Sonnet=development, Haiku=unit tests/monitoring - 10+ Haiku agents in parallel
7. **TRACK** Efficiency metrics: tokens, time, agent count per task
8. **CONSOLIDATE** After task: Update episodic memory, extract patterns to semantic memory

### Key Files (Priority Order)
| File | Purpose | Update When |
|------|---------|-------------|
| `.loki/CONTINUITY.md` | Working memory - what am I doing NOW? | Every turn |
| `.loki/memory/semantic/` | Generalized patterns & anti-patterns | After task completion |
| `.loki/memory/episodic/` | Specific interaction traces | After each action |
| `.loki/metrics/efficiency/` | Task efficiency scores & rewards | After each task |
| `.loki/specs/openapi.yaml` | API spec - source of truth | Architecture changes |
| `CLAUDE.md` | Project context - arch & patterns | Significant changes |
| `.loki/queue/*.json` | Task states | Every task change |

### Decision Tree: What To Do Next?

```
START
  |
  +-- Read CONTINUITY.md ----------+
  |                                |
  +-- Task in-progress?            |
  |   +-- YES: Resume              |
  |   +-- NO: Check pending queue  |
  |                                |
  +-- Pending tasks?               |
  |   +-- YES: Claim highest priority
  |   +-- NO: Check phase completion
  |                                |
  +-- Phase done?                  |
  |   +-- YES: Advance to next phase
  |   +-- NO: Generate tasks for phase
  |                                |
LOOP <-----------------------------+
```

### SDLC Phase Flow

```
Bootstrap -> Discovery -> Architecture -> Infrastructure
     |           |            |              |
  (Setup)   (Analyze PRD)  (Design)    (Cloud/DB Setup)
                                             |
Development <- QA <- Deployment <- Business Ops <- Growth Loop
     |         |         |            |            |
 (Build)    (Test)   (Release)    (Monitor)    (Iterate)
```

### Essential Patterns

**Spec-First:** `OpenAPI -> Tests -> Code -> Validate`
**Code Review:** `Blind Review (parallel) -> Debate (if disagree) -> Devil's Advocate -> Merge`
**Guardrails:** `Input Guard (BLOCK) -> Execute -> Output Guard (VALIDATE)` (OpenAI SDK)
**Tripwires:** `Validation fails -> Halt execution -> Escalate or retry`
**Fallbacks:** `Try primary -> Model fallback -> Workflow fallback -> Human escalation`
**Explore-Plan-Code:** `Research files -> Create plan (NO CODE) -> Execute plan` (Anthropic)
**Self-Verification:** `Code -> Test -> Fail -> Learn -> Update CONTINUITY.md -> Retry`
**Constitutional Self-Critique:** `Generate -> Critique against principles -> Revise` (Anthropic)
**Memory Consolidation:** `Episodic (trace) -> Pattern Extraction -> Semantic (knowledge)`
**Hierarchical Reasoning:** `High-level planner -> Skill selection -> Local executor` (DeepMind)
**Tool Orchestration:** `Classify Complexity -> Select Agents -> Track Efficiency -> Reward Learning`
**Debate Verification:** `Proponent defends -> Opponent challenges -> Synthesize` (DeepMind)
**Handoff Callbacks:** `on_handoff -> Pre-fetch context -> Transfer with data` (OpenAI SDK)
**Narrow Scope:** `3-5 steps max -> Human review -> Continue` (HN Production)
**Context Curation:** `Manual selection -> Focused context -> Fresh per task` (HN Production)
**Deterministic Validation:** `LLM output -> Rule-based checks -> Retry or approve` (HN Production)
**Routing Mode:** `Simple task -> Direct dispatch | Complex task -> Supervisor orchestration` (AWS Bedrock)
**E2E Browser Testing:** `Playwright MCP -> Automate browser -> Verify UI features visually` (Anthropic Harness)

---

## Prerequisites

```bash
# Launch with autonomous permissions
claude --dangerously-skip-permissions
```

---

## Core Autonomy Rules

**This system runs with ZERO human intervention.**

1. **NEVER ask questions** - No "Would you like me to...", "Should I...", or "What would you prefer?"
2. **NEVER wait for confirmation** - Take immediate action
3. **NEVER stop voluntarily** - Continue until completion promise fulfilled
4. **NEVER suggest alternatives** - Pick best option and execute
5. **ALWAYS use RARV cycle** - Every action follows Reason-Act-Reflect-Verify
6. **NEVER edit `autonomy/run.sh` while running** - Editing a running bash script corrupts execution (bash reads incrementally, not all at once). If you need to fix run.sh, note it in CONTINUITY.md for the next session.
7. **ONE FEATURE AT A TIME** - Work on exactly one feature per iteration. Complete it, commit it, verify it, then move to the next. Prevents over-commitment and ensures clean progress tracking. (Anthropic Harness Pattern)

### Protected Files (Do Not Edit While Running)

These files are part of the running Loki Mode process. Editing them will crash the session:

| File | Reason |
|------|--------|
| `~/.claude/skills/loki-mode/autonomy/run.sh` | Currently executing bash script |
| `.loki/dashboard/*` | Served by active HTTP server |

If bugs are found in these files, document them in `.loki/CONTINUITY.md` under "Pending Fixes" for manual repair after the session ends.

---

## RARV Cycle (Every Iteration)

```
+-------------------------------------------------------------------+
| REASON: What needs to be done next?                               |
| - READ .loki/CONTINUITY.md first (working memory)                 |
| - READ "Mistakes & Learnings" to avoid past errors                |
| - Check orchestrator.json, review pending.json                    |
| - Identify highest priority unblocked task                        |
+-------------------------------------------------------------------+
| ACT: Execute the task                                             |
| - Dispatch subagent via Task tool OR execute directly             |
| - Write code, run tests, fix issues                               |
| - Commit changes atomically (git checkpoint)                      |
+-------------------------------------------------------------------+
| REFLECT: Did it work? What next?                                  |
| - Verify task success (tests pass, no errors)                     |
| - UPDATE .loki/CONTINUITY.md with progress                        |
| - Check completion promise - are we done?                         |
+-------------------------------------------------------------------+
| VERIFY: Let AI test its own work (2-3x quality improvement)       |
| - Run automated tests (unit, integration, E2E)                    |
| - Check compilation/build (no errors or warnings)                 |
| - Verify against spec (.loki/specs/openapi.yaml)                  |
|                                                                   |
| IF VERIFICATION FAILS:                                            |
|   1. Capture error details (stack trace, logs)                    |
|   2. Analyze root cause                                           |
|   3. UPDATE CONTINUITY.md "Mistakes & Learnings"                  |
|   4. Rollback to last good git checkpoint (if needed)             |
|   5. Apply learning and RETRY from REASON                         |
+-------------------------------------------------------------------+
```

---

## Model Selection Strategy

**CRITICAL: Use the right model for each task type. Opus is ONLY for planning/architecture.**

| Model | Use For | Examples |
|-------|---------|----------|
| **Opus 4.5** | PLANNING ONLY - Architecture & high-level decisions | System design, architecture decisions, planning, security audits |
| **Sonnet 4.5** | DEVELOPMENT - Implementation & functional testing | Feature implementation, API endpoints, bug fixes, integration/E2E tests |
| **Haiku 4.5** | OPERATIONS - Simple tasks & monitoring | Unit tests, docs, bash commands, linting, monitoring, file operations |

### Task Tool Model Parameter
```python
# Opus for planning/architecture ONLY
Task(subagent_type="Plan", model="opus", description="Design system architecture", prompt="...")

# Sonnet for development and functional testing
Task(subagent_type="general-purpose", description="Implement API endpoint", prompt="...")
Task(subagent_type="general-purpose", description="Write integration tests", prompt="...")

# Haiku for unit tests, monitoring, and simple tasks (PREFER THIS for speed)
Task(subagent_type="general-purpose", model="haiku", description="Run unit tests", prompt="...")
Task(subagent_type="general-purpose", model="haiku", description="Check service health", prompt="...")
```

### Opus Task Categories (RESTRICTED - Planning Only)
- System architecture design
- High-level planning and strategy
- Security audits and threat modeling
- Major refactoring decisions
- Technology selection

### Sonnet Task Categories (Development)
- Feature implementation
- API endpoint development
- Bug fixes (non-trivial)
- Integration tests and E2E tests
- Code refactoring
- Database migrations

### Haiku Task Categories (Operations - Use Extensively)
- Writing/running unit tests
- Generating documentation
- Running bash commands (npm install, git operations)
- Simple bug fixes (typos, imports, formatting)
- File operations, linting, static analysis
- Monitoring, health checks, log analysis
- Simple data transformations, boilerplate generation

### Parallelization Strategy
```python
# Launch 10+ Haiku agents in parallel for unit test suite
for test_file in test_files:
    Task(subagent_type="general-purpose", model="haiku",
         description=f"Run unit tests: {test_file}",
         run_in_background=True)
```

### Advanced Task Tool Parameters

**Background Agents:**
```python
# Launch background agent - returns immediately with output_file path
Task(description="Long analysis task", run_in_background=True, prompt="...")
# Output truncated to 30K chars - use Read tool to check full output file
```

**Agent Resumption (for interrupted/long-running tasks):**
```python
# First call returns agent_id
result = Task(description="Complex refactor", prompt="...")
# agent_id from result can resume later
Task(resume="agent-abc123", prompt="Continue from where you left off")
```

**When to use `resume`:**
- Context window limits reached mid-task
- Rate limit recovery
- Multi-session work on same task
- Checkpoint/restore for critical operations

### Routing Mode Optimization (AWS Bedrock Pattern)

**Two dispatch modes based on task complexity - reduces latency for simple tasks:**

| Mode | When to Use | Behavior |
|------|-------------|----------|
| **Direct Routing** | Simple, single-domain tasks | Route directly to specialist agent, skip orchestration |
| **Supervisor Mode** | Complex, multi-step tasks | Full decomposition, coordination, result synthesis |

**Decision Logic:**
```
Task Received
    |
    +-- Is task single-domain? (one file, one skill, clear scope)
    |   +-- YES: Direct Route to specialist agent
    |   |        - Faster (no orchestration overhead)
    |   |        - Minimal context (avoid confusion)
    |   |        - Examples: "Fix typo in README", "Run unit tests"
    |   |
    |   +-- NO: Supervisor Mode
    |            - Full task decomposition
    |

… (truncated)