ac01fffd9a
Architectural snapshot of the lakehouse codebase at the point where the
full matrix-driven agent loop with Mem0 versioning + deletion was
validated end-to-end.
WHAT THIS REPO IS
A clean single-commit snapshot of the lakehouse code. Heavy test data
(.parquet datasets, vector indexes) excluded — see REPLICATION.md for
regen path. Full lakehouse history at git.agentview.dev/profit/lakehouse.
WHAT WAS PROVEN
- Vector retrieval across multi-corpora matrix (chicago_permits + entity
briefs + sec_tickers + distilled procedural + llm_team runs)
- Observer hand-review (cloud + heuristic fallback) gating each candidate
- Local-model agent loop (qwen3.5:latest) with tool use + scratchpad
- Playbook seal on success → next-iter retrieval surfaces it as preamble
- Mem0 versioning + deletion in pathway_memory:
* UPSERT: ADD on new workflow, UPDATE bumps replay_count on identical
* REVISE: chains versions, parent.superseded_at + superseded_by stamped
* RETIRE: marks specific trace retired with reason, excluded from retrieval
* HISTORY: walks chain root→tip, cycle-safe
KEY DIRECTORIES
- crates/vectord/src/pathway_memory.rs — Mem0 ops live here
- crates/vectord/src/playbook_memory.rs — original Mem0 reference
- tests/agent_test/ — local-model agent harness + PRD + session archives
- scripts/dump_raw_corpus.sh — MinIO bucket dump (raw test corpus)
- scripts/vectorize_raw_corpus.ts — corpus → vector indexes
- scripts/analyze_chicago_contracts.ts — real inference pipeline
- scripts/seal_agent_playbook.ts — Mem0 upsert from agent traces
Replication: see REPLICATION.md for Debian 13 clean install + cloud-only
adaptation (no local Ollama).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
84 lines
4.0 KiB
Markdown
84 lines
4.0 KiB
Markdown
# 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.**
|