lakehouse/docs/SYSTEM_EVOLUTION_LAYERS.md

Future Expansion — Advanced System Evolution Layers

Adopted 2026-04-24 from J. The system stops optimizing for task completion. It optimizes for provable execution, repeatable outcomes, resilience under drift, failure, and adversarial conditions.

Layer roster + iteration mapping

#	Layer	Short form	Target iter
1	Counterfactual Execution	Generate synthetic failure variants from each success	iter 5
2	Model Trust Profiling	Per-(model, task_type) success rate → routing weight	iter 3
3	Execution DNA	Compress successful runs into reusable patterns	iter 4
4	Drift Sentinel	Re-validate historical tasks on a schedule	iter 5
5	Adversarial Injection	Inject poisoned context / malformed outputs / conflicts	iter 6
6	Permission Gradient	Confidence → execution tier (≥0.9 full, ≥0.7 dry-run, ≥0.5 sim, <0.5 block)	iter 3
7	Multi-Agent Disagreement	Planner/Critic/Validator — disagreement = signal	iter 4
8	Temporal Context	Time-aware memory with decay_score + last_validated_at	iter 4
9	Execution Cost Intelligence	Tokens, iterations, cloud_calls, latency per task	iter 3
10	Human Override as Data	Capture manual fixes as jsonl rows	iter 3

Detail (J's original framing preserved)

1. Counterfactual Execution Layer

Simulate alternate failure paths for every successful task. Real Execution → Success → Generate Variations (env, version, inputs) → Simulate Failure Cases → Store Synthetic Failure Signatures. Purpose: pre-train against unseen failures before real exposure.

2. Model Trust Profiling ← iter 3

Per-(model, task_type) performance tracking.

{ "model": "...", "task_type": "...", "success_rate": 0.0, "failure_modes": [], "trust_score": 0.0 }

Usage: route by trust score, adjust validation strictness dynamically, per-model risk budgets.

3. Execution DNA (Trace Compression)

Successful executions → reusable fragments.

{ "dna_id": "hash", "task_signature": "...", "critical_steps": [], "failure_avoidance": [] }

Replaces doc retrieval with pattern retrieval; faster convergence on similar tasks.

4. Drift Sentinel

Select Historical Task → Re-run Current Env → Compare → If Failure → Mark Drifted → Trigger Re-learning. Detect silent decay; maintain long-term reliability.

5. Adversarial Injection Engine

Inject malformed outputs / outdated docs / conflicting instructions / poisoned memory. Verify validation catches, execution blocks unsafe actions, memory rejects corrupted data. Build system immunity.

6. Permission Gradient Execution ← iter 3

Confidence-based control replacing binary:

• confidence ≥ 0.9 → full execution
• confidence ≥ 0.7 → dry-run + diff
• confidence ≥ 0.5 → simulation only
• confidence < 0.5 → block Inputs: validation score, model trust score, memory match confidence. Risk-aware control; reduced catastrophic-failure surface.

7. Multi-Agent Disagreement Engine

Planner / Critic / Validator; disagreement triggers more context, bigger model, stricter validation. Disagreement is signal, not noise.

8. Temporal Context Layer

{ "created_at": "ts", "last_validated_at": "ts", "decay_score": 0.0 }

Retrieval priority: recent + validated + high success rate. Avoid stale knowledge.

9. Execution Cost Intelligence ← iter 3

{ "task": "...", "tokens_used": 0, "iterations": 0, "cloud_calls": 0, "latency_ms": 0 }

Optimize local vs cloud; reduce unnecessary iterations.

10. Human Override as Data ← iter 3

{ "human_fix": "...", "reason": "...", "task_signature": "...", "validated": true }

Manual fixes become reusable knowledge.

Final Principle

Memory is not passive recall. It is operational substrate:

• failures become structured knowledge
• successes become reusable execution patterns
• all outputs are validated before reuse

System Directive

Not speed. Not convenience. Correctness. Verifiability. Resilience under change.