feat: initialize autonomous AI dev system

.github/workflows/ai_pipeline.yml

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+name: AI Pipeline
+
+on:
+  pull_request:
+
+jobs:
+  ai-validation:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Install Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies
+        run: pip install pytest
+
+      - name: Run Tests
+        run: pytest || exit 1
+
+      - name: AI Gate Check
+        run: echo "AI gates passed"

.github/workflows/ci.yml

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+name: CI
+on: [pull_request]
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - run: echo "CI Running"

ai/agents/api_agent.py

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+# API Agent
+
+class APIAgent:
+    def generate_api(self, task, context=""):
+        return f"# API code for: {task}"

ai/agents/db_agent.py

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+# DB Agent
+
+class DBAgent:
+    def generate_schema(self, task, context=""):
+        return f"-- DB schema for: {task}"

ai/agents/developer.py

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+# Developer Agent (Claude Integration)
+
+import os
+
+class DeveloperAgent:
+    def __init__(self, client=None):
+        self.client = client  # Claude client placeholder
+
+    def generate_code(self, task: str, context: str = "") -> str:
+        """
+        Generate code using Claude (pseudo implementation)
+        """
+        prompt = f"""
+You are a senior software engineer.
+
+Context:
+{context}
+
+Task:
+{task}
+
+Generate clean, production-ready code.
+"""
+
+        # Placeholder for Claude API call
+        return f"# Generated code for: {task}\n"

ai/agents/planner.py

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+# Planner Agent
+
+def plan(task: str):
+    return {"tasks": [task]}

ai/agents/qa.py

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+# QA Agent (Self-Healing Loop)
+
+class QAAgent:
+    def analyze_failure(self, logs: str) -> str:
+        """Analyze CI/CD logs and return a fix suggestion"""
+        return f"Fix issues based on logs: {logs}"
+
+    def apply_fix(self, developer, task: str, logs: str) -> str:
+        """Re-run developer agent with fix context"""
+        fix_task = self.analyze_failure(logs)
+        return developer.generate_code(f"{task}\nFix: {fix_task}")

ai/agents/ui_agent.py

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+# UI Agent
+
+class UIAgent:
+    def generate_ui(self, task, context=""):
+        return f"// UI code for: {task}"

ai/agile/gate_orchestrator.py

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+# Gate Orchestrator (Quality Gates)
+
+class GateOrchestrator:
+    def __init__(self, threshold_score: int = 8):
+        self.threshold_score = threshold_score
+
+    def run_contract_gate(self, validation):
+        api_errors = validation.get("api_errors", [])
+        ui_errors = validation.get("ui_errors", [])
+        return {
+            "passed": len(api_errors) == 0 and len(ui_errors) == 0,
+            "errors": api_errors + ui_errors
+        }
+
+    def run_test_gate(self, validation):
+        # Placeholder: assume tests generated; CI should execute them
+        api_tests = validation.get("api_tests")
+        ui_tests = validation.get("ui_tests")
+        return {
+            "passed": bool(api_tests) and bool(ui_tests),
+            "errors": [] if (api_tests and ui_tests) else ["Missing tests"]
+        }
+
+    def run_llm_judge(self, artifacts):
+        # Placeholder scoring; replace with real LLM-as-judge
+        score = 8
+        return {
+            "passed": score >= self.threshold_score,
+            "score": score
+        }
+
+    def decide(self, validation, artifacts):
+        contract_gate = self.run_contract_gate(validation)
+        if not contract_gate["passed"]:
+            return {"action": "fix", "reason": contract_gate}
+
+        test_gate = self.run_test_gate(validation)
+        if not test_gate["passed"]:
+            return {"action": "fix", "reason": test_gate}
+
+        judge = self.run_llm_judge(artifacts)
+        if not judge["passed"]:
+            return {"action": "fix", "reason": judge}
+
+        return {"action": "merge", "reason": {"score": judge["score"]}}

ai/agile/pipeline_orchestrator.py

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+# Full Pipeline Orchestrator (CI + Auto Merge + QA Feedback)
+
+from ai.agile.sprint_manager import SprintManager
+from ai.agile.gate_orchestrator import GateOrchestrator
+from ai.agents.qa import QAAgent
+
+class PipelineOrchestrator:
+    def __init__(self):
+        self.sprint = SprintManager()
+        self.gates = GateOrchestrator()
+        self.qa = QAAgent()
+
+    def run(self, feature: str, max_retries: int = 2):
+        attempt = 0
+
+        while attempt <= max_retries:
+            result = self.sprint.run_sprint(feature)
+
+            decision = self.gates.decide(
+                result["validation"],
+                result["artifacts"]
+            )
+
+            if decision["action"] == "merge":
+                return {
+                    "status": "merged",
+                    "attempt": attempt,
+                    "details": decision
+                }
+
+            # QA feedback loop
+            logs = str(decision["reason"])
+            self.qa.apply_fix(None, feature, logs)
+
+            attempt += 1
+
+        return {
+            "status": "failed",
+            "attempts": attempt
+        }

ai/agile/sprint_manager.py

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+# Sprint Manager (AI Agile)
+
+from ai.workflows.contract_orchestrator import run_feature as run_contract_flow
+from ai.workflows.validation_orchestrator import validate_and_generate_tests
+
+class SprintManager:
+    def __init__(self):
+        self.backlog = []
+
+    def create_backlog(self, feature: str):
+        # naive decomposition; replace with Planner agent later
+        self.backlog = [
+            f"Design DB for {feature}",
+            f"Build API for {feature}",
+            f"Build UI for {feature}"
+        ]
+        return self.backlog
+
+    def run_sprint(self, feature: str):
+        # End-to-end execution using contract-aware flow
+        result = run_contract_flow(feature)
+
+        validation = validate_and_generate_tests(
+            result.get("schema", {}),
+            result.get("api_contracts", []),
+            result.get("ui_contracts", [])
+        )
+
+        return {
+            "feature": feature,
+            "artifacts": result,
+            "validation": validation
+        }

ai/context/context_engine.py

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+# Context Engine (RAG for Repo Awareness)
+
+from typing import List
+
+class ContextEngine:
+    def __init__(self):
+        # Placeholder for vector DB / embeddings
+        self.index = {}
+
+    def index_repo(self, files: dict):
+        """Index repository files (simple in-memory for now)"""
+        for path, content in files.items():
+            self.index[path] = content
+
+    def search(self, query: str, top_k: int = 3) -> List[str]:
+        """Naive search (replace with embeddings later)"""
+        results = []
+        for path, content in self.index.items():
+            if query.lower() in content.lower():
+                results.append((path, content))
+
+        return [r[1] for r in results[:top_k]]
+
+    def get_context(self, task: str) -> str:
+        """Fetch relevant context for a task"""
+        snippets = self.search(task)
+        return "\n\n".join(snippets)

ai/context/context_engine_v2.py

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+# Context Engine V2 (Embeddings + Vector DB)
+
+from ai.context.vector_store import VectorStore
+from ai.context.embedding_model import EmbeddingModel
+
+class ContextEngineV2:
+    def __init__(self):
+        self.vector_store = VectorStore()
+        self.embedder = EmbeddingModel()
+
+    def index_repo(self, files: dict):
+        texts = list(files.values())
+        embeddings = self.embedder.encode(texts)
+        self.vector_store.add(embeddings, texts)
+
+    def get_context(self, task: str, top_k=3):
+        query_embedding = self.embedder.encode(task)[0]
+        results = self.vector_store.search(query_embedding, top_k)
+        return "\n\n".join(results)

ai/context/embedding_model.py

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+# Embedding Model (placeholder)
+
+from sentence_transformers import SentenceTransformer
+
+class EmbeddingModel:
+    def __init__(self, model_name='all-MiniLM-L6-v2'):
+        self.model = SentenceTransformer(model_name)
+
+    def encode(self, texts):
+        if isinstance(texts, str):
+            texts = [texts]
+        return self.model.encode(texts).tolist()

ai/context/vector_store.py

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+# Vector Store using FAISS (local)
+
+import faiss
+import numpy as np
+
+class VectorStore:
+    def __init__(self, dim=384):
+        self.index = faiss.IndexFlatL2(dim)
+        self.texts = []
+
+    def add(self, embeddings, texts):
+        self.index.add(np.array(embeddings).astype('float32'))
+        self.texts.extend(texts)
+
+    def search(self, query_embedding, top_k=3):
+        D, I = self.index.search(np.array([query_embedding]).astype('float32'), top_k)
+        return [self.texts[i] for i in I[0] if i < len(self.texts)]

ai/contracts/api_contract.py

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+# API Contract Generator
+
+class APIContract:
+    def generate(self, task: str, schema: dict):
+        """Generate structured API contract from schema"""
+        endpoints = []
+
+        for table, fields in schema.items():
+            endpoints.append({
+                "endpoint": f"/{table}",
+                "method": "POST",
+                "request": fields,
+                "response": ["id"] + fields
+            })
+
+        return endpoints

ai/contracts/schema_extractor.py

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+# Schema Extractor (Structured DB Parsing)
+
+import re
+
+class SchemaExtractor:
+    def extract(self, db_schema: str):
+        """Extract tables and fields from SQL-like schema"""
+        tables = {}
+        current_table = None
+
+        for line in db_schema.splitlines():
+            line = line.strip()
+
+            if line.lower().startswith("create table"):
+                current_table = line.split()[2]
+                tables[current_table] = []
+
+            elif current_table and line and not line.startswith("--"):
+                field = re.split(r"\s+", line)[0]
+                if field.lower() not in ["primary", "foreign", ")"]:
+                    tables[current_table].append(field)
+
+        return tables

ai/contracts/ui_contract.py

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+# UI Contract Generator
+
+class UIContract:
+    def generate(self, api_contracts):
+        """Generate UI bindings from API contracts"""
+        ui_bindings = []
+
+        for api in api_contracts:
+            ui_bindings.append({
+                "form": api["endpoint"],
+                "fields": api["request"],
+                "submit_to": api["endpoint"]
+            })
+
+        return ui_bindings

ai/observability/monitor.py

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+# Observability Monitor
+
+import datetime
+
+class Monitor:
+    def track(self, event: str, data: dict = None):
+        timestamp = datetime.datetime.utcnow().isoformat()
+        print({
+            "timestamp": timestamp,
+            "event": event,
+            "data": data or {}
+        })

ai/prompts/developer.prompt.txt

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+You are a senior software engineer.
+
+Rules:
+- Follow existing codebase structure
+- Reuse components if available
+- Write clean, modular, production-ready code
+- Include types and basic validation
+
+Output only code.