first commit

app_factory/agents/pm_agent.py

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+"""Project Manager Agent - Expands user prompts into structured PRDs and handles clarification requests."""
+
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+
+from app_factory.core.claude_client import ClaudeSDKClient
+
+
+class PMAgent:
+    """Agent responsible for PRD generation, clarification handling, and project planning."""
+
+    def __init__(
+        self,
+        api_key: str = None,
+        auth_token: str = None,
+        model: str = "claude-opus-4-6",
+        debug: bool = False,
+        observability=None,
+    ):
+        self.model = model
+        self.input_tokens = 0
+        self.output_tokens = 0
+        self._prompts_dir = Path(__file__).resolve().parent.parent / "prompts"
+        self.observability = observability
+
+        resolved_key = api_key or os.environ.get("ANTHROPIC_API_KEY")
+        resolved_auth = auth_token or os.environ.get("ANTHROPIC_AUTH_TOKEN")
+        self.client = ClaudeSDKClient(
+            api_key=resolved_key,
+            auth_token=resolved_auth,
+            enable_debug=debug,
+        )
+
+    def _load_template(self, template_name: str) -> str:
+        """Load a prompt template file from app_factory/prompts/."""
+        path = self._prompts_dir / template_name
+        return path.read_text()
+
+    async def expand_prompt_to_prd(self, user_input: str) -> str:
+        """Expand a user prompt into a structured PRD using Claude.
+
+        Returns markdown with sections: Objective, Core Requirements,
+        Technical Architecture, Tech Stack, Success Criteria, Non-Functional Requirements.
+        """
+        system_prompt = self._load_template("pm_prd_expansion.txt")
+
+        response = await self.client.complete(
+            prompt=user_input,
+            model=self.model,
+            system_prompt=system_prompt,
+            max_turns=100,
+            observability=self.observability,
+            agent_name="pm_agent",
+            task_id="expand_prd",
+        )
+
+        self.input_tokens += response.input_tokens
+        self.output_tokens += response.output_tokens
+        if self.observability:
+            self.observability.log_token_usage(
+                "pm_agent",
+                "expand_prd",
+                input_tokens=response.input_tokens,
+                output_tokens=response.output_tokens,
+                model=self.model,
+            )
+
+        return response.text
+
+    async def handle_clarification_request(self, clarification: dict) -> str:
+        """Handle a clarification request from a downstream agent.
+
+        Args:
+            clarification: dict with keys requesting_agent, task_id, question, context.
+
+        Returns:
+            Clarification response string. If the question requires human input,
+            prompts the user and returns their answer.
+        """
+        template = self._load_template("pm_clarification.txt")
+        prompt = template.format(
+            requesting_agent=clarification.get("requesting_agent", "unknown"),
+            task_id=clarification.get("task_id", "N/A"),
+            question=clarification.get("question", ""),
+            context=clarification.get("context", ""),
+        )
+
+        response = await self.client.complete(
+            prompt=prompt,
+            model=self.model,
+            max_turns=100,
+            observability=self.observability,
+            agent_name="pm_agent",
+            task_id=f"clarification:{clarification.get('task_id', 'N/A')}",
+        )
+
+        self.input_tokens += response.input_tokens
+        self.output_tokens += response.output_tokens
+        if self.observability:
+            self.observability.log_token_usage(
+                "pm_agent",
+                f"clarification:{clarification.get('task_id', 'N/A')}",
+                input_tokens=response.input_tokens,
+                output_tokens=response.output_tokens,
+                model=self.model,
+            )
+
+        answer = response.text.strip()
+
+        if "ESCALATE_TO_HUMAN" in answer:
+            human_answer = input(
+                f"[PMAgent] Clarification needed for {clarification.get('requesting_agent', 'agent')} "
+                f"(task {clarification.get('task_id', 'N/A')}): "
+                f"{clarification.get('question', '')}\n> "
+            )
+            return human_answer
+
+        return answer
+
+    def update_prd(self, prd_path: str, updates: str):
+        """Append updates to an existing PRD file with a versioned header."""
+        timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+        header = f"\n\n---\n## PRD Update - {timestamp}\n\n"
+
+        with open(prd_path, "a") as f:
+            f.write(header)
+            f.write(updates)
+
+    def get_token_usage(self) -> dict:
+        """Return cumulative token usage."""
+        return {
+            "input_tokens": self.input_tokens,
+            "output_tokens": self.output_tokens,
+            "total_tokens": self.input_tokens + self.output_tokens,
+        }