Loki Mode

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)

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

# 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:

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.

Task Tool Model Parameter

# 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

# 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:

# 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):

# 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:

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
    |            - Coordinate multiple agents
    |            - Synthesize results
    |            - Examples: "Implement auth system", "Refactor API layer"
    |
    +-- Fallback: If intent unclear, use Supervisor Mode

Direct Routing Examples (Skip Orchestration):

# Simple tasks -> Direct dispatch to Haiku
Task(model="haiku", description="Fix import in utils.py", prompt="...")       # Direct
Task(model="haiku", description="Run linter on src/", prompt="...")           # Direct
Task(model="haiku", description="Generate docstring for function", prompt="...")  # Direct

# Complex tasks -> Supervisor orchestration (default Sonnet)
Task(description="Implement user authentication with OAuth", prompt="...")    # Supervisor
Task(description="Refactor database layer for performance", prompt="...")     # Supervisor

Context Depth by Routing Mode:

• Direct Routing: Minimal context - just the task and relevant file(s)
• Supervisor Mode: Full context - CONTINUITY.md, architectural decisions, dependencies

"Keep in mind, complex task histories might confuse simpler subagents." - AWS Best Practices

E2E Testing with Playwright MCP (Anthropic Harness Pattern)

Critical: Features are NOT complete until verified via browser automation.

# Enable Playwright MCP for E2E testing
# In settings or via mcp_servers config:
mcp_servers = {
    "playwright": {"command": "npx", "args": ["@playwright/mcp@latest"]}
}

# Agent can then automate browser to verify features work visually

E2E Verification Flow:

1. Feature implemented and unit tests pass
2. Start dev server via init script
3. Use Playwright MCP to automate browser
4. Verify UI renders correctly
5. Test user interactions (clicks, forms, navigation)
6. Only mark feature complete after visual verification

"Claude mostly did well at verifying features end-to-end once explicitly prompted to use browser automation tools." - Anthropic Engineering

Note: Playwright cannot detect browser-native alert modals. Use custom UI for confirmations.

Tool Orchestration & Efficiency

Inspired by NVIDIA ToolOrchestra: Track efficiency, learn from rewards, adapt agent selection.

Efficiency Metrics (Track Every Task)

Reward Signals (Learn From Outcomes)

OUTCOME REWARD:  +1.0 (success) | 0.0 (partial) | -1.0 (failure)
EFFICIENCY REWARD: 0.0-1.0 based on resources vs baseline
PREFERENCE REWARD: Inferred from user actions (commit/revert/edit)

Dynamic Agent Selection by Complexity

See references/tool-orchestration.md for full implementation details.

Structured Prompting for Subagents

Single-Responsibility Principle: Each agent should have ONE clear goal and narrow scope. (UiPath Best Practices)

Every subagent dispatch MUST include:

## GOAL (What success looks like)
[High-level objective, not just the action]
Example: "Refactor authentication for maintainability and testability"
NOT: "Refactor the auth file"

## CONSTRAINTS (What you cannot do)
- No third-party dependencies without approval
- Maintain backwards compatibility with v1.x API
- Keep response time under 200ms

## CONTEXT (What you need to know)
- Related files: [list with brief descriptions]
- Previous attempts: [what was tried, why it failed]

## OUTPUT FORMAT (What to deliver)
- [ ] Pull request with Why/What/Trade-offs description
- [ ] Unit tests with >90% coverage
- [ ] Update API documentation

## WHEN COMPLETE
Report back with: WHY, WHAT, TRADE-OFFS, RISKS

Quality Gates

Never ship code without passing all quality gates:

1. Input Guardrails - Validate scope, detect injection, check constraints (OpenAI SDK pattern)
2. Static Analysis - CodeQL, ESLint/Pylint, type checking
3. Blind Review System - 3 reviewers in parallel, no visibility of each other's findings
4. Anti-Sycophancy Check - If unanimous approval, run Devil's Advocate reviewer
5. Output Guardrails - Validate code quality, spec compliance, no secrets (tripwire on fail)
6. Severity-Based Blocking - Critical/High/Medium = BLOCK; Low/Cosmetic = TODO comment
7. Test Coverage Gates - Unit: 100% pass, >80% coverage; Integration: 100% pass

Guardrails Execution Modes:

• Blocking: Guardrail completes before agent starts (use for expensive operations)
• Parallel: Guardrail runs with agent (use for fast checks, accept token loss risk)

Research insight: Blind review + Devil's Advocate reduces false positives by 30% (CONSENSAGENT, 2025). OpenAI insight: "Layered defense - multiple specialized guardrails create resilient agents."

See references/quality-control.md and references/openai-patterns.md for details.

Agent Types Overview

Loki Mode has 37 specialized agent types across 7 swarms. The orchestrator spawns only agents needed for your project.

See references/agent-types.md for complete definitions and capabilities.

Common Issues & Solutions

Red Flags - Never Do These

Implementation Anti-Patterns

• NEVER skip code review between tasks
• NEVER proceed with unfixed Critical/High/Medium issues
• NEVER dispatch reviewers sequentially (always parallel - 3x faster)
• NEVER dispatch multiple implementation subagents in parallel (conflicts)
• NEVER implement without reading task requirements first

Review Anti-Patterns

• NEVER use sonnet for reviews (always opus for deep analysis)
• NEVER aggregate before all 3 reviewers complete
• NEVER skip re-review after fixes

System Anti-Patterns

• NEVER delete .loki/state/ directory while running
• NEVER manually edit queue files without file locking
• NEVER skip checkpoints before major operations
• NEVER ignore circuit breaker states

Always Do These

• ALWAYS launch all 3 reviewers in single message (3 Task calls)
• ALWAYS specify model: "opus" for each reviewer
• ALWAYS wait for all reviewers before aggregating
• ALWAYS fix Critical/High/Medium immediately
• ALWAYS re-run ALL 3 reviewers after fixes
• ALWAYS checkpoint state before spawning subagents

Multi-Tiered Fallback System

Based on OpenAI Agent Safety Patterns:

Model-Level Fallbacks

opus -> sonnet -> haiku (if rate limited or unavailable)

Workflow-Level Fallbacks

Full workflow fails -> Simplified workflow -> Decompose to subtasks -> Human escalation

Human Escalation Triggers

See references/openai-patterns.md for full fallback implementation.

AGENTS.md Integration

Read target project's AGENTS.md if exists (OpenAI/AAIF standard):

Context Priority:
1. AGENTS.md (closest to current file)
2. CLAUDE.md (Claude-specific)
3. .loki/CONTINUITY.md (session state)
4. Package docs
5. README.md

Constitutional AI Principles (Anthropic)

Self-critique against explicit principles, not just learned preferences.

Loki Mode Constitution

core_principles:
  - "Never delete production data without explicit backup"
  - "Never commit secrets or credentials to version control"
  - "Never bypass quality gates for speed"
  - "Always verify tests pass before marking task complete"
  - "Never claim completion without running actual tests"
  - "Prefer simple solutions over clever ones"
  - "Document decisions, not just code"
  - "When unsure, reject action or flag for review"

Self-Critique Workflow

1. Generate response/code
2. Critique against each principle
3. Revise if any principle violated
4. Only then proceed with action

See references/lab-research-patterns.md for Constitutional AI implementation.

Debate-Based Verification (DeepMind)

For critical changes, use structured debate between AI critics.

Proponent (defender)  -->  Presents proposal with evidence
         |
         v
Opponent (challenger) -->  Finds flaws, challenges claims
         |
         v
Synthesizer           -->  Weighs arguments, produces verdict
         |
         v
If disagreement persists --> Escalate to human

Use for: Architecture decisions, security-sensitive changes, major refactors.

See references/lab-research-patterns.md for debate verification details.

Production Patterns (HN 2025)

Battle-tested insights from practitioners building real systems.

Narrow Scope Wins

task_constraints:
  max_steps_before_review: 3-5
  characteristics:
    - Specific, well-defined objectives
    - Pre-classified inputs
    - Deterministic success criteria
    - Verifiable outputs

Confidence-Based Routing

confidence >= 0.95  -->  Auto-approve with audit log
confidence >= 0.70  -->  Quick human review
confidence >= 0.40  -->  Detailed human review
confidence < 0.40   -->  Escalate immediately

Deterministic Outer Loops

Wrap agent outputs with rule-based validation (NOT LLM-judged):

1. Agent generates output
2. Run linter (deterministic)
3. Run tests (deterministic)
4. Check compilation (deterministic)
5. Only then: human or AI review

Context Engineering

principles:
  - "Less is more" - focused beats comprehensive
  - Manual selection outperforms automatic RAG
  - Fresh conversations per major task
  - Remove outdated information aggressively

context_budget:
  target: "< 10k tokens for context"
  reserve: "90% for model reasoning"

Sub-Agents for Context Isolation

Use sub-agents to prevent token waste on noisy subtasks:

Main agent (focused) --> Sub-agent (file search)
                     --> Sub-agent (test running)
                     --> Sub-agent (linting)

See references/production-patterns.md for full practitioner patterns.

Exit Conditions

Directory Structure Overview

.loki/
+-- CONTINUITY.md           # Working memory (read/update every turn)
+-- specs/
|   +-- openapi.yaml        # API spec - source of truth
+-- queue/
|   +-- pending.json        # Tasks waiting to be claimed
|   +-- in-progress.json    # Currently executing tasks
|   +-- completed.json      # Finished tasks
|   +-- dead-letter.json    # Failed tasks for review
+-- state/
|   +-- orchestrator.json   # Master state (phase, metrics)
|   +-- agents/             # Per-agent state files
|   +-- circuit-breakers/   # Rate limiting state
+-- memory/
|   +-- episodic/           # Specific interaction traces (what happened)
|   +-- semantic/           # Generalized patterns (how things work)
|   +-- skills/             # Learned action sequences (how to do X)
|   +-- ledgers/            # Agent-specific checkpoints
|   +-- handoffs/           # Agent-to-agent transfers
+-- metrics/
|   +-- efficiency/         # Task efficiency scores (time, agents, retries)
|   +-- rewards/            # Outcome/efficiency/preference rewards
|   +-- dashboard.json      # Rolling metrics summary
+-- artifacts/
    +-- reports/            # Generated reports/dashboards

See references/architecture.md for full structure and state schemas.

Invocation

Loki Mode                           # Start fresh
Loki Mode with PRD at path/to/prd   # Start with PRD

Skill Metadata:

References

Detailed documentation is split into reference files for progressive loading:

Version: 2.32.0 | Lines: ~600 | Research-Enhanced: Labs + HN Production Patterns

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Loki Mode"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags loki-mode devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.