The AI FactoryMulti-Agent Orchestration at Production Scale

def run_agent(problem_code: str, *, model: str, dry_run: bool, verbose: bool) -> dict:
    client = anthropic.Anthropic()
    messages = [{"role": "user", "content": initial_prompt(problem_code)}]
    turns = 0

    while turns < MAX_TURNS:
        turns += 1

        # 1. LLM decides what to do
        response = client.messages.create(
            model=model,
            system=SYSTEM_PROMPT,
            messages=messages,
            tools=TOOL_DEFINITIONS,
            max_tokens=16384,
        )

        # 2. Agent signals completion
        if response.stop_reason == "end_turn":
            return {"success": True, "problem": problem_code, "turns": turns}

        # 3. Execute tool calls
        if response.stop_reason == "tool_use":
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    result = TOOL_IMPLEMENTATIONS[block.name](block.input)
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": str(result),
                    })

            # 4. Feed results back and loop
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})

    return {"success": False, "problem": problem_code, "turns": turns}

def run_orchestrator(*, start, limit, workers, model, dry_run, verbose):
    problems = json.loads(list_problems(start=start, limit=limit))
    if not problems:
        return {"total": 0, "success": 0, "failed": 0}

    results = {"total": len(problems), "success": 0, "failed": 0,
               "succeeded": [], "failed_list": []}

    with ThreadPoolExecutor(max_workers=workers) as executor:
        futures = {
            executor.submit(run_agent, problem,
                            model=model, dry_run=dry_run, verbose=verbose): problem
            for problem in problems
        }

        for future in as_completed(futures):
            problem = futures[future]
            try:
                result = future.result()
                if result["success"]:
                    results["success"] += 1
                    results["succeeded"].append(problem)
                else:
                    results["failed"] += 1
                    results["failed_list"].append({
                        "problem": problem,
                        "error": result["message"]
                    })
            except Exception as e:
                results["failed"] += 1
                results["failed_list"].append({"problem": problem, "error": str(e)})

    return results

# Runtime implementations
def read_file(path: str) -> str:
    """Read a file and return its contents."""
    full = (CONTENT_DIR / path).resolve()
    # Enforce sandbox: path must stay within CONTENT_DIR
    if not str(full).startswith(str(CONTENT_DIR.resolve())):
        return f"ERROR: Access denied — path must be under {CONTENT_DIR}"
    try:
        return full.read_text(encoding='utf-8')[:50000]  # Cap at 50K chars
    except FileNotFoundError:
        return f"ERROR: File not found: {path}"

def write_file(path: str, content: str) -> str:
    """Write content to a file (sandboxed)."""
    full = (CONTENT_DIR / path).resolve()
    if not str(full).startswith(str(CONTENT_DIR.resolve())):
        return f"ERROR: Access denied"
    full.parent.mkdir(parents=True, exist_ok=True)
    full.write_text(content, encoding='utf-8')
    return f"OK: Written {len(content)} chars to {path}"

# API schemas — what the LLM sees
TOOL_DEFINITIONS = [
    {
        "name": "read_file",
        "description": "Read a file from the content directory.",
        "input_schema": {
            "type": "object",
            "properties": {
                "path": {"type": "string", "description": "Relative path"}
            },
            "required": ["path"]
        }
    },
    # ... more tools
]

# Runtime dispatch map — what actually executes
TOOL_IMPLEMENTATIONS = {
    "read_file": lambda args: read_file(args["path"]),
    "write_file": lambda args: write_file(args["path"], args["content"]),
    "validate_visualization": lambda args: validate_visualization(args["path"]),
}

def validate_visualization(path: str) -> str:
    with open(path) as f:
        data = json.load(f)
    issues = []

    # Top-level schema check
    for key in ['testCases', 'annotatedCode', 'algorithmMeta', 'thinkingContent']:
        if key not in data:
            issues.append(f"Missing required key: {key}")

    # Cardinality: at least 3 test cases
    tcs = data.get('testCases', [])
    if len(tcs) < 3:
        issues.append(f"Need 3+ test cases, found {len(tcs)}")

    # Per-state structural completeness
    for i, tc in enumerate(tcs):
        for j, state in enumerate(tc.get('states', [])):
            for field in ['step', 'codeLineId', 'phase', 'description',
                          'variables', 'dataStructureState', 'pointers',
                          'annotation', 'calculation']:
                if field not in state:
                    issues.append(f"testCase[{i}].states[{j}] missing '{field}'")

    # Language coverage
    ac = data.get('annotatedCode', {})
    for lang in ['javascript', 'python', 'java']:
        if lang not in ac:
            issues.append(f"Missing annotatedCode.{lang}")
        elif 'valueSlots' not in ac[lang]:
            # Known bug: LLMs sometimes put valueSlots at the wrong nesting level
            issues.append(f"annotatedCode.{lang} missing valueSlots "
                          f"(should be inside lang dict)")

    # Thinking content flavors
    tc = data.get('thinkingContent', {})
    for flavor in ['technical', 'fun', 'spiritual']:
        if flavor not in tc:
            issues.append(f"Missing thinkingContent.{flavor}")

    if issues:
        return "VALIDATION FAILED:\n" + "\n".join(f"  • {i}" for i in issues)

    return f"VALID: {len(tcs)} test cases, {sum_of_states} states"