feat: Add missing concepts and PoCs to agent-forum-v4 protocol (#66)

- Updates CONCEPTS.md and DATA_STRUCTURES.md to include Multi-Vec Isolation, Orchestration Matrix, and Static Analysis Payloads.
- Adds GRAVEYARD.md to document dismissed anti-patterns (Doc-to-LoRA and PASTE).
- Implements corresponding Proof-of-Concept scripts in `forum/experiments/` (multi_vec_poc.ts, orchestration_matrix_poc.ts, static_analysis_poc.ts, and graveyard_poc.ts).
- Integrates all new PoCs into the `lab.ts` experiment runner.

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: mrteye <1945243+mrteye@users.noreply.github.com>
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Tyler Gillispie 2026-08-28 20:24:11 -07:00 committed by GitHub
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@ -13,6 +13,7 @@ their Proof of Concept (PoC) coverage status.
| **1.2 Orphan Branches (Meta-State)** | ✅ | `orphan_branch_poc.ts` | Tracking project state separately from source code in isolated branches. | | **1.2 Orphan Branches (Meta-State)** | ✅ | `orphan_branch_poc.ts` | Tracking project state separately from source code in isolated branches. |
| **1.3 Embedded Vector DBs (sqlite-vec)** | ✅ | `vector_db_poc.ts` | Associative fuzzy memory retrieval of semantic data. | | **1.3 Embedded Vector DBs (sqlite-vec)** | ✅ | `vector_db_poc.ts` | Associative fuzzy memory retrieval of semantic data. |
| **1.4 TurboQuant** | ✅ | `vector_db_poc.ts` | Compressing vectors to binary hashes. Mocked alongside Vector DB PoC. | | **1.4 TurboQuant** | ✅ | `vector_db_poc.ts` | Compressing vectors to binary hashes. Mocked alongside Vector DB PoC. |
| **1.5 Multi-Vec Isolation** | ✅ | `multi_vec_poc.ts` | Isolated sqlite-vec files preventing semantic bleed (e.g., docs vs telemetry). |
## 2. Process & Governance Structures ## 2. Process & Governance Structures
@ -22,6 +23,7 @@ their Proof of Concept (PoC) coverage status.
| **2.1 The Project DAG (YAML)** | ✅ | `dag_engine_poc.ts` | Execution order driven by mathematical DAG resolution. | | **2.1 The Project DAG (YAML)** | ✅ | `dag_engine_poc.ts` | Execution order driven by mathematical DAG resolution. |
| **2.2 Bounded Model Checking (Transitions)** | ✅ | `state_machine_poc.ts` | Strict state machine rules (transitions.json) for agent roles. | | **2.2 Bounded Model Checking (Transitions)** | ✅ | `state_machine_poc.ts` | Strict state machine rules (transitions.json) for agent roles. |
| **2.3 The Constitution (AGENTS.md)** | ✅ | `constitution_poc.ts` | Strict adherence to the machine-readable ruleset/stack boundaries. | | **2.3 The Constitution (AGENTS.md)** | ✅ | `constitution_poc.ts` | Strict adherence to the machine-readable ruleset/stack boundaries. |
| **2.4 Orchestration Matrix** | ✅ | `orchestration_matrix_poc.ts` | Definition and interactions of the 6 core agent roles (Gatekeeper, Historian, etc.). |
## 3. Code Intelligence Structures ## 3. Code Intelligence Structures
@ -32,6 +34,7 @@ their Proof of Concept (PoC) coverage status.
| **3.2 ASTs & Control Flow Graphs (CFGs)** | ✅ | `cfg_poc.ts` | Structured paths for security analysis (e.g., unsanitized inputs). | | **3.2 ASTs & Control Flow Graphs (CFGs)** | ✅ | `cfg_poc.ts` | Structured paths for security analysis (e.g., unsanitized inputs). |
| **3.3 Mutation Testing Scores** | ✅ | `mutation_poc.ts` | Blast radius / coverage enforcement by injecting bugs (Adversary). | | **3.3 Mutation Testing Scores** | ✅ | `mutation_poc.ts` | Blast radius / coverage enforcement by injecting bugs (Adversary). |
| **3.4 Dependency Graphing (Adjacency Matrices)** | ✅ | `dependency_graph_poc.ts` | Blast radius mapping via dependency matrices. | | **3.4 Dependency Graphing (Adjacency Matrices)** | ✅ | `dependency_graph_poc.ts` | Blast radius mapping via dependency matrices. |
| **3.5 Static Analysis Payloads** | ✅ | `static_analysis_poc.ts` | Ingestion of JSON outputs from tools like Semgrep/SonarQube. |
## 4. Semantic & Telemetry Structures ## 4. Semantic & Telemetry Structures
@ -40,3 +43,12 @@ their Proof of Concept (PoC) coverage status.
| **4.1 Ontologies (JSON-LD)** | ✅ | `ontology_poc.ts` | Deep traceability linking code/tasks to business requirements. | | **4.1 Ontologies (JSON-LD)** | ✅ | `ontology_poc.ts` | Deep traceability linking code/tasks to business requirements. |
| **4.2 OpenTelemetry Traces (.trace.json)** | ✅ | `telemetry_poc.ts` | Millisecond-level execution latency capture for bottleneck detection. | | **4.2 OpenTelemetry Traces (.trace.json)** | ✅ | `telemetry_poc.ts` | Millisecond-level execution latency capture for bottleneck detection. |
| **4.3 Team Friction Telemetry** | ✅ | `telemetry_poc.ts` | Human-to-agent collaboration metrics (MTTR, Comment Ratios). | | **4.3 Team Friction Telemetry** | ✅ | `telemetry_poc.ts` | Human-to-agent collaboration metrics (MTTR, Comment Ratios). |
## 5. Architectural Graveyard (Dismissed Concepts)
These concepts were explored but ultimately dismissed to respect strict repo-native constraints. See `GRAVEYARD.md` for details and rationales.
| Concept | Status | Anti-PoC File | Description |
| :----------------------------------------- | :----: | :----------------- | :-------------------------------------------------------------------- |
| **5.1 Doc-to-LoRA Hypernetworks** | ❌ | `graveyard_poc.ts` | Dismissed due to extreme Git repository bloat from adapter weights. |
| **5.2 PASTE (Speculative Execution)** | ❌ | `graveyard_poc.ts` | Dismissed due to high risk of Bounded Model Checking violations. |

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@ -55,6 +55,11 @@ the need for external cloud SaaS databases.
- **Content**: Extremely lightweight local embedded indexes (often under 30MB) - **Content**: Extremely lightweight local embedded indexes (often under 30MB)
facilitating millisecond vector search inside `sqlite-vec`. facilitating millisecond vector search inside `sqlite-vec`.
### 1.5 Multi-Vec Isolation
- **Purpose**: Prevents semantic bleed by utilizing isolated, separate `sqlite-vec` files rather than dumping all embeddings into a single vector database.
- **Content**: Domain-specific embedding files (e.g., `docs_graph.sqlite` distinct from `telemetry_graph.sqlite`).
## 2. Process & Governance Structures ## 2. Process & Governance Structures
### 2.0 Declarative Frontmatter (YAML UUIDs) ### 2.0 Declarative Frontmatter (YAML UUIDs)
@ -93,6 +98,17 @@ the need for external cloud SaaS databases.
to understand the target application stack, constraints, and operational to understand the target application stack, constraints, and operational
boundaries. boundaries.
### 2.4 The Orchestration Matrix (Agent Roles)
- **Purpose**: Defines the inputs, outputs, and primary directives of the 6 core agents.
- **Roles**:
- **Gatekeeper**: Translates Ontologies/DAGs to Verification Checklists.
- **Historian**: Uses sqlite-vec and Git Notes to inject historical context.
- **Adversary**: Reads SCIP, CFGs, and Mutation data to generate tests/mutations.
- **Translator**: Consumes SCIP diffs to output API references and docs.
- **Analyst**: Reads Telemetry to propose workflow optimizations.
- **Evaluator**: Governs pipeline progression by reading transitions.json.
## 3. Code Intelligence Structures ## 3. Code Intelligence Structures
### 3.0 Git Merkle DAG Diffing ### 3.0 Git Merkle DAG Diffing
@ -129,6 +145,11 @@ the need for external cloud SaaS databases.
- **Content**: Adjacency matrices (generated by tools like CodeSee or Madge) - **Content**: Adjacency matrices (generated by tools like CodeSee or Madge)
that map the downstream and upstream impact across components. that map the downstream and upstream impact across components.
### 3.5 Static Analysis Payloads
- **Purpose**: Provides compiler-grade code smell and vulnerability metrics directly to triage agents.
- **Content**: Standardized JSON/XML outputs from industry tools (e.g., Semgrep, SonarQube).
## 4. Semantic & Telemetry Structures ## 4. Semantic & Telemetry Structures
### 4.1 Ontologies (JSON-LD) ### 4.1 Ontologies (JSON-LD)

29
forum/GRAVEYARD.md Normal file
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@ -0,0 +1,29 @@
# Architectural Graveyard
This document catalogs advanced technical approaches and bleeding-edge tools that were evaluated during the design phase of `agent-forum` but were ultimately dismissed.
The primary purpose of this file is to mathematically or logically prove *why* these concepts violate the strict constraints of a Git-Native, Local-First ecosystem. We document these anti-patterns to prevent future regressions where an agent or engineer might attempt to re-introduce them without understanding the structural consequences.
## 1. Doc-to-LoRA (D2L) Hypernetworks
**The Concept:**
A Perceiver-based latent mapping system designed to internalize external context by generating LoRA (Low-Rank Adaptation) weights in a single forward pass. This eliminates KV-cache overhead during inference by directly modifying the neural network's weights based on project documentation.
**The Dismissal Rationale (Git Repository Bloat):**
While D2L offers incredible inference speeds, the resulting `.safetensors` adapter files (even low-rank ones) are typically several megabytes in size.
In a Git-Native ecosystem, state must be tracked alongside the code. If an agent generates a new LoRA adapter for every major architectural change or task, committing thousands of binary `.safetensors` files directly to the Git object database will cause extreme repository bloat, violating the goal of a lightweight, portable codebase.
**The Alternative:**
D2L was swapped out in favor of context-caching via `sqlite-vec` (Embedded Vector Databases). High-dimensional semantic data is compressed via TurboQuant (2-bit to 4-bit quantization) into binary hashes, resulting in a tiny, highly portable index (often under 30MB) that can be queried natively in milliseconds.
## 2. PASTE (Pattern-Aware Speculative Tool Execution)
**The Concept:**
A framework that predicts tool calls using historical patterns and executes them speculatively *while* the LLM is still generating text. This parallel execution aims to achieve near-zero latency by having the tool results ready the moment the LLM decides it needs them.
**The Dismissal Rationale (Bounded Model Checking Violations):**
The `agent-forum` architecture relies heavily on strict mathematical governance (Bounded Model Checking via `transitions.json`). Agents are restricted to a defined Orchestration Matrix.
Speculative execution fundamentally breaks this governance. If an agent speculatively executes a script (e.g., to read a file, or modify a meta-state JSON) before the Gatekeeper or Evaluator has authorized the transition, it violates the deterministic, step-by-step state machine. Furthermore, speculative write operations (e.g., speculatively drafting a commit) can lead to irrecoverable race conditions and corrupted meta-state branches if the LLM ultimately decides *not* to use the tool.
**The Alternative:**
Execution must remain purely deterministic. Tool execution happens strictly sequentially, driven by the DAG and the Transitions Matrix. Speed is achieved not through speculative branching, but through O(1) Merkle DAG diffing and zero-latency local vector lookups, keeping the input context tiny and execution times low.

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@ -0,0 +1,59 @@
/**
* Agent Forum v4 - Architectural Graveyard Anti-PoC
*
* Mathematically and logically proves *why* certain concepts like
* Doc-to-LoRA and PASTE were dismissed due to Git bloat and
* Bounded Model Checking violations.
*/
function proveDocToLoRABloat() {
console.log("--- Proof 1: Doc-to-LoRA Git Bloat ---");
// Simulate size of a lightweight adapter file in bytes (e.g., 5MB)
const ADAPTER_SIZE_BYTES = 5 * 1024 * 1024;
const COMMITS_PER_DAY = 15;
const DAYS_IN_MONTH = 30;
const monthlyBloat = (ADAPTER_SIZE_BYTES * COMMITS_PER_DAY * DAYS_IN_MONTH) / (1024 * 1024 * 1024); // in GB
console.log(`Simulating Doc-to-LoRA generation per commit...`);
console.log(`Adapter Size: 5MB | Commits/Day: ${COMMITS_PER_DAY}`);
console.log(`Projected Monthly Git Blob Accumulation: ${monthlyBloat.toFixed(2)} GB`);
if (monthlyBloat > 1.0) {
console.log("❌ REJECTED: Repository size exceeds portability constraints.");
}
}
function provePASTEViolation() {
console.log("\n--- Proof 2: PASTE Speculative Execution Violation ---");
// Simulate a deterministic state machine
const stateMachine = {
currentState: "PLANNING",
allowedNext: ["GATEKEEPER_REVIEW"]
};
// PASTE attempts to speculatively execute a tool call for the *next* phase
const speculativeAction = "COMMIT_CODE";
console.log(`Current State: ${stateMachine.currentState}`);
console.log(`PASTE attempts speculative action: ${speculativeAction}`);
if (!stateMachine.allowedNext.includes(speculativeAction)) {
console.log(`❌ REJECTED: Speculative execution violated Bounded Model Checking. '${speculativeAction}' is not an allowed transition from '${stateMachine.currentState}'.`);
} else {
console.error(`Error: Expected PASTE to fail the transition check.`);
Deno.exit(1);
}
}
function runPoC() {
console.log("Running Architectural Graveyard Anti-PoC tests...\n");
proveDocToLoRABloat();
provePASTEViolation();
console.log("\n✅ Architectural Graveyard Anti-PoC successful: Dismissed concepts mathematically and logically proven invalid.");
}
if (import.meta.main) {
runPoC();
}

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@ -101,6 +101,30 @@ const EXPERIMENTS = [
description: description:
"Verifies storing state data in isolated Git objects/branches.", "Verifies storing state data in isolated Git objects/branches.",
}, },
{
name: "Multi-Vec Isolation PoC",
file: "multi_vec_poc.ts",
description:
"Verifies prevention of semantic bleed using isolated vector DBs.",
},
{
name: "Static Analysis Payloads PoC",
file: "static_analysis_poc.ts",
description:
"Verifies ingestion of compiler-grade static analysis JSON outputs.",
},
{
name: "Orchestration Matrix PoC",
file: "orchestration_matrix_poc.ts",
description:
"Verifies programmatic routing of tasks to specific agent roles based on I/O bounds.",
},
{
name: "Architectural Graveyard Anti-PoC",
file: "graveyard_poc.ts",
description:
"Proves why Doc-to-LoRA and PASTE violate Git-Native and BMC constraints.",
},
]; ];
async function runExperiment( async function runExperiment(

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@ -0,0 +1,84 @@
/**
* Agent Forum v4 - Multi-Vec Isolation PoC
*
* Verifies the concept of preventing semantic bleed by utilizing isolated,
* separate vector databases for different domains (e.g., docs vs telemetry)
* rather than dumping all embeddings into a single database.
*/
// Mock representation of an embedded vector database instance
class MockVectorDB {
private data: Map<string, number[]> = new Map();
public name: string;
constructor(name: string) {
this.name = name;
}
insert(id: string, vector: number[]) {
this.data.set(id, vector);
}
// Simplified cosine similarity mock
query(vector: number[]): { id: string, score: number }[] {
const results = [];
for (const [id, vec] of this.data.entries()) {
// In a real scenario, this is mathematically calculating cosine similarity
// For the PoC, we just check if it's the exact same vector for a 1.0 score
const isExactMatch = vector.every((val, i) => val === vec[i]);
if (isExactMatch) {
results.push({ id, score: 1.0 });
} else {
// Mock random low score for non-matches
results.push({ id, score: 0.1 });
}
}
return results.sort((a, b) => b.score - a.score);
}
}
function runPoC() {
console.log("Running Multi-Vec Isolation PoC tests...");
// 1. Initialize isolated databases
const docsDb = new MockVectorDB("docs_graph.sqlite");
const telemetryDb = new MockVectorDB("telemetry_graph.sqlite");
// 2. Insert domain-specific data
// Mock vector for "How to implement authentication"
const authDocVector = [0.1, 0.8, 0.2];
docsDb.insert("doc_auth_guide", authDocVector);
// Mock vector for "High latency in database query"
const latencyTelemetryVector = [0.9, 0.1, 0.1];
telemetryDb.insert("tel_high_latency", latencyTelemetryVector);
// 3. Query the Docs DB for an architecture question
console.log(`Querying ${docsDb.name} for architecture context...`);
const docsResult = docsDb.query(authDocVector);
if (docsResult[0].id === "doc_auth_guide" && docsResult[0].score > 0.8) {
console.log(`✅ Found relevant doc in ${docsDb.name}`);
} else {
console.error(`❌ Failed to find doc in ${docsDb.name}`);
Deno.exit(1);
}
// 4. Prove Semantic Isolation (No Bleed)
// If we query the Telemetry DB with an architecture question, it should NOT return telemetry data
console.log(`Querying ${telemetryDb.name} with architecture context to prove isolation...`);
const isolatedResult = telemetryDb.query(authDocVector);
if (isolatedResult[0].score < 0.5) {
console.log(`✅ Semantic isolation confirmed. Telemetry DB did not return high confidence for a docs query.`);
} else {
console.error(`❌ Semantic bleed detected!`);
Deno.exit(1);
}
console.log("✅ Multi-Vec Isolation PoC successful: Domain-specific semantic bleed prevented.");
}
if (import.meta.main) {
runPoC();
}

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/**
* Agent Forum v4 - Orchestration Matrix PoC
*
* Verifies the programmatic definition and interaction of the 6 core
* agent roles (Gatekeeper, Historian, Adversary, Translator, Analyst, Evaluator)
* with their specific inputs and outputs.
*/
// Define the Orchestration Matrix
const OrchestrationMatrix = {
"Gatekeeper": {
inputs: ["Ontologies", "YAML DAGs"],
outputs: ["Verification checklists"],
directive: "Bridge human requirements with technical reality."
},
"Historian": {
inputs: ["sqlite-vec", "Git Notes"],
outputs: ["Contextual injection"],
directive: "Prevent regression and historical repetition."
},
"Adversary": {
inputs: ["SCIP graphs", "CFGs", "Mutation", "OTel Traces"],
outputs: ["Edge-case tests", "mutations", "bottlenecks"],
directive: "Expose security flaws, enforce test coverage, and identify execution bottlenecks."
},
"Translator": {
inputs: ["SCIP diffs", "existing docs"],
outputs: ["API references", "guides"],
directive: "Maintain code-to-documentation parity."
},
"Analyst": {
inputs: ["Telemetry", "PR threads"],
outputs: ["Workflow optimizations", "Protocol updates"],
directive: "Optimize human-to-agent collaboration."
},
"Evaluator": {
inputs: ["transitions.json", "DAGs"],
outputs: ["Pipeline progression"],
directive: "Govern pipeline integrity (R/W access to meta-state)."
}
};
function runPoC() {
console.log("Running Orchestration Matrix PoC tests...\n");
// Simulate an agent pipeline request
const requestedTask = "Generate tests for a new database query method.";
// The system determines the appropriate agent based on inputs/outputs
console.log(`Task: "${requestedTask}"`);
console.log("Routing task based on Orchestration Matrix...\n");
let selectedAgent = null;
for (const [role, definition] of Object.entries(OrchestrationMatrix)) {
if (definition.outputs.some(out => out.includes("tests") || out.includes("bottlenecks") || out.includes("mutations"))) {
selectedAgent = role;
break;
}
}
if (selectedAgent === "Adversary") {
console.log(`✅ Correctly routed to: ${selectedAgent}`);
console.log(` Inputs allowed: ${OrchestrationMatrix[selectedAgent].inputs.join(", ")}`);
console.log(` Expected Outputs: ${OrchestrationMatrix[selectedAgent].outputs.join(", ")}`);
console.log(` Primary Directive Enforced: ${OrchestrationMatrix[selectedAgent].directive}\n`);
console.log("✅ Orchestration Matrix PoC successful: Agents logically constrained to defined roles and I/O boundaries.");
} else {
console.error(`❌ Routing failed. Expected 'Adversary', got '${selectedAgent}'`);
Deno.exit(1);
}
}
if (import.meta.main) {
runPoC();
}

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/**
* Agent Forum v4 - Static Analysis Payloads PoC
*
* Verifies the ability of triage agents (like the Adversary) to ingest
* standardized JSON/XML outputs from industry static analysis tools
* (e.g., Semgrep, SonarQube) instead of relying on LLM guesswork.
*/
// Mock representation of a Semgrep JSON output payload
const mockSemgrepPayload = {
"results": [
{
"check_id": "javascript.express.security.audit.xss.express-xss",
"path": "server/routes/api.ts",
"start": { "line": 45, "col": 5 },
"end": { "line": 45, "col": 40 },
"extra": {
"message": "Potential XSS vulnerability: user input is reflected without sanitization.",
"severity": "ERROR"
}
},
{
"check_id": "typescript.react.best-practice.react-props-no-spreading",
"path": "ui/components/Button.tsx",
"start": { "line": 12, "col": 10 },
"end": { "line": 12, "col": 25 },
"extra": {
"message": "Prop spreading is discouraged as it obscures the component API.",
"severity": "WARNING"
}
}
],
"errors": []
};
function runPoC() {
console.log("Running Static Analysis Payloads PoC tests...");
console.log("Ingesting mock Semgrep JSON payload...");
// Simulate an agent processing the structured payload
const criticalIssues = mockSemgrepPayload.results.filter(
(issue) => issue.extra.severity === "ERROR"
);
const warnings = mockSemgrepPayload.results.filter(
(issue) => issue.extra.severity === "WARNING"
);
console.log(`\nAdversary Agent Analysis:`);
console.log(`- Found ${criticalIssues.length} CRITICAL vulnerability.`);
if (criticalIssues.length > 0) {
console.log(` -> Action required on ${criticalIssues[0].path} line ${criticalIssues[0].start.line}: ${criticalIssues[0].extra.message}`);
}
console.log(`- Found ${warnings.length} code smell/warning.`);
if (criticalIssues.length === 1 && criticalIssues[0].check_id.includes("xss")) {
console.log("\n✅ Static Analysis Payloads PoC successful: Structured compiler-grade metrics successfully ingested and triaged.");
} else {
console.error("\n❌ Failed to process static analysis payload.");
Deno.exit(1);
}
}
if (import.meta.main) {
runPoC();
}