Overview
The /aiad-auto-evolution command is the Prismatic Platform's most advanced autonomous operation -- a self-evolving meta-command that orchestrates the continuous evolution of the entire AIAD ecosystem. Currently at version 4.0.0 and Generation 18, this command has achieved full autonomy with mathematically proven safety guarantees, enabling platform-wide evolution without human approval while preserving guaranteed human override capability. It represents the culmination of 18 generations of evolutionary design, from basic performance tuning in v1.0.0 to self-referential meta-evolution with Lean4 formal verification in the current release.
What distinguishes this command from conventional automation is its self-referential nature: the /aiad-auto-evolution command can evolve its own specification. When invoked with --target self, the command applies its own evolution algorithms to its specification file, enabling recursive self-improvement within mathematically bounded safety constraints. This capability is unique in the platform and represents a fundamental shift from human-directed evolution to autonomous, safety-proven evolution. Five core Lean4 theorems -- Quality Preservation, Regression Prevention, Safety Boundedness, Termination Guarantee, and Human Override Preservation -- provide mathematical proofs that autonomous evolution cannot degrade system quality, remove existing functionality, exceed safety thresholds, run indefinitely, or disable human control.
The command operates under COSMIC authority level and is executed by the evolution-orchestrator agent, coordinating with eight supporting agents including the recursive-optimizer, capability-emergence-detector, meta-evolution-orchestrator, mycelial-propagator, and trinity-gate-validator. This multi-agent coordination enables sophisticated evolution strategies that span the entire platform, from individual agent optimization to ecosystem-wide capability emergence cultivation.
Usage
/aiad-auto-evolution [options]Standard Autonomous Evolution Cycle
/aiad-auto-evolutionConservative Evolution with Strict Safety
/aiad-auto-evolution --automation-level conservative --safety-constraints strictSelf-Meta-Evolution of This Command
/aiad-auto-evolution --target self --evolution-scope metaCASCADE Methodology with Formal Verification
/aiad-auto-evolution --cascade-methodology true --lean4-verification true --automation-level transcendentFull Autonomy with Mathematical Safety Proofs
/aiad-auto-evolution --target platform_wide --autonomy_level full_autonomous --safety_proof_requirement mathematical_certainty --consensus_threshold 0.85Options and Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
--evolution-scope | string | comprehensive | Scope: targeted, comprehensive, revolutionary, experimental, consciousness, meta |
--automation-level | string | intelligent | Autonomy: conservative, intelligent, aggressive, revolutionary, transcendent |
--safety-constraints | string | strict | Safety: strict, standard, relaxed, experimental, consciousness_bounded |
--target | string | ecosystem | Target: ecosystem, agents, commands, self, specific |
--intelligence-focus | array | all | Focus: recursive, emergent, collective, meta, consciousness, mycelial, all |
--3nl-mode | string | fusion | 3NL mode: L1_logic, L2_neural, L3_linguistic, fusion |
--trinity-validation | boolean | true | Enable 11-layer Trinity Gate validation |
--nabla-epistemic | boolean | true | Enable NABLA Infinity epistemic confidence validation |
--mycelial-propagation | boolean | true | Enable mycelial network pattern propagation |
--quality-dna-persist | boolean | true | Persist evolution state to Quality DNA |
--cascade-methodology | boolean | true | Enable CASCADE quality evolution patterns |
--lean4-verification | boolean | false | Enable Lean4 formal verification proofs |
--garden-integration | boolean | true | Enable GARDEN repository pattern extraction |
--mcp-tools-direct | boolean | true | Enable direct MCP tool invocation |
--genetic-library-access | boolean | true | Access genetic library (17 generations, 630+ assets) |
--auto-test-generation | boolean | false | Enable autonomous test generation |
--multi-llm-coordination | string | claude_primary | Multi-LLM mode: claude_primary, claude_only, multi_provider, consensus |
--autonomy_level | string | transcendent | Full autonomy level: transcendent, full_autonomous, mathematical_safe, proof_bounded |
--safety_proof_requirement | string | critical_only | Proof level: disabled, critical_only, all_changes, mathematical_certainty |
--consensus_threshold | float | 0.75 | Multi-LLM consensus threshold for autonomous decisions |
--autonomous_rollback | boolean | true | Enable autonomous rollback with proof validation |
--real_time_safety_monitoring | boolean | true | Enable real-time safety proof monitoring |
Authority and Access
| Property | Value |
|---|---|
| Authority Level | COSMIC (highest clearance) |
| Executing Agent | evolution-orchestrator (v4) |
| Supporting Agents | recursive-optimizer, capability-emergence-detector, meta-evolution-orchestrator, meta-intelligence-engine, mycelial-propagator, consciousness-emergence-coordinator, trinity-gate-validator, quality-floor-guardian |
| Status | Experimental (revolutionary capability) |
| Usage Frequency | Low (scheduled and triggered) |
| Category | Meta-Evolution |
| Generation | 18 (Full Autonomy Design) |
| Apex Fitness | 0.999 |
| Safety Level | Mathematically Proven (Lean4) |
| Classification | Full Autonomy + Mathematical Safety Proofs |
Technical Implementation
The command's core architecture is built around the Autonomous Decision-Making Engine, which processes evolution opportunities through a multi-stage pipeline: opportunity analysis, risk-benefit evaluation, strategy generation, safety validation, prioritization, and execution with monitoring. Every autonomous decision must pass through the mathematical safety proof verification system before execution.
defmodule AIAD.AutoEvolution.DecisionEngine do
@moduledoc """
Autonomous decision-making engine for AIAD ecosystem evolution.
Every decision requires mathematical safety proof verification
before autonomous execution is authorized.
"""
@proof_confidence_threshold 0.99
@consensus_threshold 0.75
@spec make_evolution_decisions(map(), map()) :: {:ok, [map()]} | {:error, term()}
def make_evolution_decisions(context, constraints) do
context
|> analyze_evolution_opportunities()
|> evaluate_risk_benefit_ratios()
|> generate_evolution_strategies()
|> validate_safety_constraints(constraints)
|> verify_mathematical_proofs()
|> obtain_multi_llm_consensus()
|> prioritize_evolution_actions()
|> execute_autonomous_evolution()
end
defp verify_mathematical_proofs(strategies) do
Enum.map(strategies, fn strategy ->
with {:ok, proof} <- generate_safety_proof(strategy),
{:ok, verification} <- verify_with_lean4(proof),
true <- verification.confidence >= @proof_confidence_threshold do
%{strategy | safety_verified: true, proof: verification}
else
_ -> %{strategy | safety_verified: false, blocked: true}
end
end)
|> Enum.filter(& &1.safety_verified)
end
defp obtain_multi_llm_consensus(strategies) do
Enum.map(strategies, fn strategy ->
consensus = ByzantineConsensus.safety_consensus(strategy)
case consensus do
{:safe, result} when result.agreement >= @consensus_threshold ->
%{strategy | consensus_approved: true}
_ ->
%{strategy | consensus_approved: false, blocked: true}
end
end)
|> Enum.filter(& &1.consensus_approved)
end
end
The safety proof system integrates five core Lean4 theorems that provide mathematical guarantees for autonomous evolution. The Quality Preservation theorem proves that valid evolutions cannot decrease the system quality score. The Regression Prevention theorem proves that existing functionality is preserved across all evolutions. The Safety Boundedness theorem proves that autonomous actions cannot exceed acceptable risk thresholds. The Termination Guarantee theorem proves that evolution processes always terminate within bounded resources. The Human Override Preservation theorem -- axiomatically true -- proves that human override capability can never be disabled by any autonomous action.
The CASCADE Methodology provides five proven quality evolution patterns extracted from the platform's achievement of a 100/100 quality score: Type Mismatch Elimination, Dead Code Removal, Empty Check Optimization, Timer Replacement, and Nuclear Cache Invalidation. Each pattern has a 100% success rate and is automatically applied during evolution cycles.
Workflow Integration
The /aiad-auto-evolution command operates in three execution modes. Scheduled Evolution runs every 6 hours for 15-45 minutes, performing comprehensive ecosystem analysis and targeted improvements. Opportunity-Driven Evolution triggers within 5 minutes when significant improvement opportunities are detected, such as new capability emergence potential or cross-domain optimization patterns. Emergency Evolution activates within 30 seconds for critical performance degradation, security vulnerabilities, or capability regressions.
In practice, most developers interact with this command indirectly through scheduled cycles that continuously improve the platform. Direct invocation is reserved for targeted evolution campaigns, self-meta-evolution of the command itself, and emergency situations requiring maximum-authority evolution authority. The command integrates with the platform's session lifecycle, persisting evolution state to Quality DNA for cross-session continuity.
Integration Points
| Component | Relationship |
|---|---|
| Prismatic Agents | Coordinates 8 supporting agents for evolution operations |
| Trinity Gate | 11-layer validation of all evolution outputs |
| NABLA Infinity | Epistemic confidence validation (0.95 threshold) |
| Quality Gates | 21 quality gates including 7 full-autonomy gates |
| Quality DNA | Cross-session evolution state persistence |
| Mycelial Network | Pattern propagation across 400+ agent ecosystem |
| Telemetry | Evolution metrics, safety proof verification, consensus tracking |
| GARDEN | Legacy pattern extraction from 116 repositories |
| Lean4 Theorem Library | Mathematical safety proofs in priv/lean4/theorems/ |
| MCP Tools | Direct access to 27 production tools via prismatic-mcp server |
| Genetic Library | 17 generations, 630+ assets with auto-trait inheritance |
Doctrine Compliance
All evolution operations are governed by the NO MERCY, NO DOUBTS doctrine with additional autonomous safety enforcement:
- NO MERCY: Zero tolerance for quality regressions. The quality score must be preserved at 100/100 across all evolution cycles. Every evolution must produce measurable improvement or be rolled back. No incomplete evolutions are committed. The CASCADE methodology enforces 100% pattern compliance with zero regression tolerance.
- NO DOUBTS: Every autonomous decision is backed by mathematical proof. Multi-LLM Byzantine Fault Tolerant consensus validates safety-critical decisions. Evolution strategies are generated from evidence-based analysis, not speculation. Lean4 formal verification provides proof-level confidence for critical changes. Human override capability is axiomatically preserved and continuously monitored.
The command also enforces the Addiction Preservation doctrine: contradictory evolution signals are preserved and analyzed rather than prematurely resolved, ensuring the full epistemic picture informs evolution decisions.
Best Practices
-
Start with conservative automation: When first using the command, begin with
--automation-level conservativeto understand the evolution pipeline before escalating to more autonomous levels. -
Enable Lean4 verification for critical changes: For evolutions targeting core infrastructure or safety-critical components, always enable
--lean4-verification trueto obtain mathematical safety proofs. -
Use CASCADE methodology for quality improvements: The five CASCADE patterns have 100% proven success rates. Enable
--cascade-methodology truefor any quality-focused evolution campaign. -
Monitor the safety dashboard: When running full-autonomy evolutions, keep the real-time safety monitoring dashboard active (
--real_time_safety_monitoring true) to observe proof verification and consensus status in real time. -
Leverage GARDEN for pattern extraction: Enable
--garden-integration trueto extract proven patterns from 20+ years of legacy repository knowledge during evolution cycles. -
Self-evolve cautiously: Meta-evolution (
--target self) modifies the command's own specification. Always enable maximum safety constraints and mathematical certainty proofs when self-evolving.
Related Commands
- /evolve - Living AIAD ecosystem evolution with 5-phase cycle and GitLab observability
- /mycelialize - Biological-inspired pattern propagation at 500K patterns/sec with emergence detection
- /mycelialize-formal - Lean4 + Prolog formal verification for mathematically proven pattern propagation
- /quality-gates - Enforce quality gate checkpoints with zero-warning compilation validation
- /quality-enforce - Mandatory progressive quality debt elimination with AIAD enforcement
- /regression-check - Execute 25 custom Credo regression checks preventing 700+ violations
- /3nl - Three-layer neural linguistic processing and coordination
Connect & Contribute
Created by Tomáš Korcak (korczis) | Open Source under GHL