Architectural standards, schemas, and production patterns.

import re
from typing import Set

def extract_numbers(text: str) -> Set[str]:
    cleaned = re.sub(r"[\$,]", "", text)
    return set(re.findall(r"\b\d+(?:\.\d+)?\b", cleaned))

def verify_response_faithfulness(generated_answer: str, context_chunks: list[str]) -> bool:
    source_text = " ".join(context_chunks)
    source_numbers = extract_numbers(source_text)
    answer_numbers = extract_numbers(generated_answer)
    unsupported = answer_numbers - source_numbers
    return len(unsupported) == 0

| Architecture | Core Mechanism | Use Case |
|---|---|---|
| Naive RAG | Query -> embed -> vector DB -> LLM | Single-turn Q&A, clean homogeneous docs |
| Multimodal RAG | Shared embedding space text/images | Visual charts, scanned PDFs |
| HyDE | LLM generates hypothetical doc first | Vocabulary mismatch between user & corpus |
| Corrective (CRAG) | Evaluates retrieved docs; web fallback | High precision; internal data has gaps |
| Graph RAG | Entity relationship graph | Entity connections (legal, corporate structures) |
| Hybrid RAG | Vector + keyword (BM25) | Exact terminology AND semantic meaning |
| Adaptive RAG | Query analyzer routes retrieval mode | Diverse user question patterns |
| Agentic RAG | Multi-agent ReAct cycles with memory | Multi-step cross-system investigations |

from llama_parse import LlamaParse

parser = LlamaParse(
    result_type="markdown",
    verbose=True,
    language="en",
    split_by_page=True,
)

# Returns page chunks formatted strictly as markdown with preserved tables
documents = parser.load_data("financial_report.pdf")
for doc in documents:
    assert "| --- |" in doc.text or len(doc.text) < 100 # Verify tabular preservation

Full Self-Attention: O(N^2) time & memory
Block-Sparse / Subquadratic: O(N * sqrt(N)) or O(N log N)
State-Space / Recurrent Models: O(N) linear time during inference

Pipeline guideline:
1. Use 1M+ token context windows for low-frequency synthesis tasks.
2. Use chunked vector index + reranking for high-concurrency Q&A to avoid quadratic VRAM spikes.

Production Heuristic:
1. File size < 50 pages: Direct prompt with prompt caching enabled (90% cost reduction on subsequent calls).
2. File size > 50 pages: Hybrid search (BM25 + Dense Vector) -> Top 10 chunks -> Re-ranker -> Filtered context window.
3. Tabular / Structured: SQL extraction first, never raw context dumping.

def evaluate_model_turn(prediction: str, golden_record: dict) -> dict:
    has_valid_citations = all(c in prediction for c in golden_record['expected_citations'])
    exact_match_score = 1.0 if golden_record['target_answer'] in prediction else 0.0
    
    return {
        'citation_precision': 1.0 if has_valid_citations else 0.0,
        'exact_match': exact_match_score,
        'passed': has_valid_citations and (exact_match_score > 0.8)
    }

{
  "name": "execute_command",
  "description": "Run a shell command inside the sandboxed container. Returns stdout and stderr.",
  "parameters": {
    "type": "object",
    "properties": {
      "command": {
        "type": "string",
        "description": "The exact shell command line string to run."
      },
      "timeout_seconds": {
        "type": "integer",
        "default": 30,
        "description": "Maximum execution duration before process cancellation."
      }
    },
    "required": ["command"]
  }
}

The 5 Layers of Autonomous Agent Systems:

1. Runtime: Sandboxed container, execution permissions, scoped credentials.
2. Tools: MCP servers, database connectors, issue tracker APIs.
3. Context: Codebase index, PRDs, database schemas, persistent rules.
4. Feedback Loops: Automated linting, test runners, assertion gates.
5. Human Review: Strategic intent check and production deployment sign-off.

# SOUL.md: Agent Identity and Constraints
Role: Autonomous Browser Specialist
Execution Rules:
1. Always confirm target URLs before navigating.
2. Take screenshot assertions before and after DOM modifications.
3. Return extracted tabular data strictly as JSON schemas.

class AgentDaemon:
    def __init__(self, db_path: str):
        self.db = sqlite3.connect(db_path)
        self.init_schema()

    def dispatch_event(self, event_type: str, payload: dict):
        # Log to local audit store before tool dispatch
        cursor = self.db.cursor()
        cursor.execute("INSERT INTO event_log (type, payload) VALUES (?, ?)", 
                       (event_type, json.dumps(payload)))
        self.db.commit()

from typing import TypedDict
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    task: str
    plan: list[str]
    code: str
    test_results: str
    iterations: int

workflow = StateGraph(AgentState)
workflow.add_node("planner", plan_task)
workflow.add_node("executor", write_code)
workflow.add_node("evaluator", run_tests)

workflow.add_edge("planner", "executor")
workflow.add_edge("executor", "evaluator")
workflow.add_conditional_edges("evaluator", should_continue, {
    "retry": "executor",
    "complete": END
})

# Direct terminal agent commands:
gh pr create --title "Feature: spend analytics ETL" --body "Automated by agent"
supabase db diff -f add_vendor_indices
playwright test tests/e2e/dashboard.spec.ts --reporter=json

version: '3.8'
services:
  agent-runner:
    image: python:3.11-slim
    user: "1000:1000"
    working_dir: /workspace
    volumes:
      - ./project:/workspace:rw
    read_only: false
    security_opt:
      - no-new-privileges:true
    cap_drop:
      - ALL

# Agent Handoff Summary
- Completed: Implemented Pydantic models for contract schemas
- Working File: app/schemas/contract.py
- Failing Test: tests/test_validation.py::test_date_overlap
- Next Immediate Step: Update regex parser to handle Q3 fiscal year notation
- Git Branch: feature/contract-schemas

best_score = baseline_evaluation()

for iteration in range(max_trials):
    patch = agent.propose_improvement()
    apply_patch(patch)
    score = run_test_suite()
    
    if score > best_score:
        best_score = score
        git_commit(f"Improved benchmark to {score}")
    else:
        git_revert()

Agent 1 (Planner): Generates task specification and API interface definitions.
Agent 2 (Builder): Implements code matching the specification.
Agent 3 (Critic): Executes linter, type-checker, and automated tests. Rejects PR on any failure.

Vector Retrieval (Passive):
Query -> Top 5 document chunks -> Probabilistic text answer

Direct Corpus Interaction (Active):
Query -> Inspect database schema -> Generate SQL -> Execute query -> Validate output -> Answer with source proof

# Project Overview
Goal: Production API service for real-time spend analytics and contract optimization.

# Tech Stack & Standards
- Language: Python 3.11
- Web: FastAPI + Pydantic v2
- Database: PostgreSQL with SQLAlchemy (async)
- Testing: pytest with pytest-asyncio

# Coding Conventions
- Use explicit type annotations for all function signatures.
- Prefer pure functions for business calculations.
- Store database models in `app/models/` and schemas in `app/schemas/`.

# Negative Constraints
- Never run unmigrated database queries against production.
- Do not add external dependencies without explicit user confirmation.
- Do not edit files inside `migrations/` directly.

# SYSTEM ROLE
You are a Principal AI Systems Engineer specializing in high-throughput data pipelines.

# OBJECTIVE
Audit the provided Python ETL pipeline for concurrency bottlenecks and memory leaks.

# CONSTRAINTS
- Propose changes only in standard library Python or existing project dependencies.
- Do not modify database schema definitions.
- Return findings categorized strictly by Severity: High, Medium, Low.

# OUTPUT FORMAT
Return a valid JSON object matching the AuditReport schema.

Prompt Assembly Sequence for Optimal Cache Hit:
1. Static System Rules (Cached - 90% cost savings)
2. Project Conventions & Schemas (Cached)
3. Retrieved Context Documents (Cached across multi-turn queries)
4. Dynamic User Request & Turn History (Uncached)

Feature Specification Protocol:
1. Clarification: Ask 2-3 specific questions regarding UI/UX and database constraints.
2. Plan: Draft an implementation plan covering files to create, edit, and test.
3. Tests First: Write failing pytest / vitest cases capturing expected behavior.
4. Implementation: Write minimal code required to pass the test suite.

/plan: Freeze code writing. Outline step-by-step implementation dependencies.
/grill-me: Interview the engineer with 3 tough questions before finalizing architecture.
/boost: Deploy multi-perspective verification agents across the code changes.

<tool_call>
  <thought>Need to verify if table 'vendors' exists before running ETL.</thought>
  <function_call name="execute_sql">
    <parameter name="query">SELECT table_name FROM information_schema.tables WHERE table_schema='public';</parameter>
  </function_call>
</tool_call>

# Pipe git diff and active ticket directly into agent session:
(cat context/project_overview.md; git diff main) | claude "Review these changes for security vulnerabilities" 

Do not answer immediately. 
Ask me 3 penetrating questions about concurrency limits, failure recovery, and data isolation in this design. 
Once I answer, point out the weakest assumption in my architecture.

-- Source records: Permanent relational store
CREATE TABLE documents (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    source_url TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Embeddings store: Disposable and regeneratable
CREATE TABLE document_embeddings (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    document_id UUID REFERENCES documents(id) ON DELETE CASCADE,
    chunk_index INT NOT NULL,
    chunk_content TEXT NOT NULL,
    embedding vector(1536),
    embedding_model TEXT NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX ON document_embeddings USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

# Agent traverses backlinks directly:
grep -rn "\[\[Spend Analytics\]\]" vault/
# Agent follows concept threads through explicit relationships rather than fuzzy cosine distances

1. Ephemeral Memory: Context window turn history (lost on restart)
2. File-Based State: run-log.md / CLAUDE.md (zero cost, high persistence)
3. Graph Memory: Graphify / Neo4j (tracks entity relationships across sessions)
4. Vector Memory: pgvector / Mem0 (fuzzy semantic retrieval of past conversations)

Entity: Vendor_A
-> Contract: Software_License_2026 ($1.2M)
-> Owner: Dept_DOE
-> Status: Under Review

Querying the entity subgraph retrieves exact facts in 150 tokens vs 10,000 tokens of raw PDF chunks.

Step 1: Ingest contract & RFP documents into NotebookLM.
Step 2: Generate Grounded Blueprint (functional specs + exact numbers + source citations).
Step 3: Feed Grounded Blueprint to Antigravity / Claude Code agent for implementation.

Markdown Layer:
- Journaling, architectural field notes, reference specs
- Consumed by LLMs and terminal agents without layout noise

HTML/JS Layer:
- Interactive financial models, covenant calculators, executive KPI views
- Consumed by human stakeholders for decision-making

import sqlite3

def query_agent_memory(query: str, params: tuple = ()) -> list[dict]:
    conn = sqlite3.connect("project_memory.db")
    conn.row_factory = sqlite3.Row
    cursor = conn.cursor()
    cursor.execute(query, params)
    rows = cursor.fetchall()
    conn.close()
    return [dict(row) for row in rows]

from pydantic import BaseModel, Field
from jinja2 import Template

class SectionContent(BaseModel):
    title: str = Field(description="Section heading")
    summary: str = Field(description="Executive summary in 2 sentences")
    key_metrics: list[str] = Field(description="Bullet points with quantitative findings")

DOCUMENT_TEMPLATE = """
# {{ data.title }}

## Executive Summary
{{ data.summary }}

## Key Findings
{% for metric in data.key_metrics %}
- {{ metric }}
{% endfor %}
"""

def render_document(extracted_json: dict) -> str:
    validated = SectionContent.model_validate(extracted_json)
    template = Template(DOCUMENT_TEMPLATE)
    return template.render(data=validated)

Core Recommended Skills:
1. frontend-design: Enforces clean typographic hierarchy and removes AI-style gradients.
2. simplify: Spawns 3 review subagents to audit code complexity and redundant abstractions.
3. webapp-testing: Headless browser automation that clicks through and tests generated UI flows.
4. skill-creator: Converts validated multi-step terminal workflows into reusable agent skills.

Layer 1: Ingestion & Storage
- Databricks Unity Catalog + Mosaic AI Vector Search
- Markdown extraction + immutable chunk hashing

Layer 2: Execution & Orchestration
- LangGraph / Antigravity Agentic IDE
- Sandboxed terminal environments + scoped MCP tools

Layer 3: Verification & Governance
- Pydantic schema validation + regex numeric faithfulness scoring
- Deterministic template engines (Jinja2) for document output