add-agent

Before doing anything else, check that /k9aif:configure has been run. If K9AIF_PROJECT_ROOT and K9AIF_APP_NAME are not set, refuse and say:

"Please run /k9aif:configure first to set your project root and app name." Do not proceed until init has been run.

K9-AIF: Add Agent

Scaffold a complete new agent for the K9-AIF framework. The user provides: <AgentName> <AppName> (e.g. ClaimsTriageAgent MyApp).

Before creating any files:

1. Check whether examples/<AppName>/ already exists.
2. If it does NOT exist, ask the user to confirm before proceeding:

   "The app folder examples/<AppName>/ does not exist yet. Do you want to create a new app structure, or did you mean a different app name? Existing apps: (list folders under examples/)"

3. Only proceed once the user confirms the app name.

What to create

1. Agent YAML — examples/<AppName>/agents/yaml/<agent_name_lower>.yaml

name: <AgentName>
class: <AgentName>

description: >
  <one paragraph — what this agent does>

pattern: reasoning
model: reasoning

role: >
  You are a ...

goal: >
  Your goal is to ...

instructions:
  - Instruction one
  - Instruction two

output_schema:
  result: string
  confidence: float

governance:
  pre_process: true
  post_process: false

2. Python class — examples/<AppName>/agents/src/<agent_name_lower>.py

from typing import Any, Dict, Optional
from k9_aif_abb.k9_core.agent.base_agent import BaseAgent
from k9_aif_abb.k9_inference.models.inference_request import InferenceRequest
from k9_aif_abb.k9_utils.llm_invoke import llm_invoke


class <AgentName>(BaseAgent):

    layer = "<AppName> <AgentName> SBB"

    def __init__(self, config: Optional[Dict[str, Any]] = None, monitor=None, **kwargs):
        super().__init__(config or {}, monitor=monitor, **kwargs)

    def execute(self, payload: Dict[str, Any]) -> Dict[str, Any]:
        prompt = (
            f"Role: {self.config.get('role', '')}\n"
            f"Goal: {self.config.get('goal', '')}\n\n"
            f"Input: {payload}"
        )
        req = InferenceRequest(
            prompt=prompt,
            task_type=self.config.get("model", "general"),
            metadata={"agent": "<AgentName>"},
        )
        try:
            resp = llm_invoke(self.config, req)
        except RuntimeError as exc:
            self.logger.error("[%s] LLM unavailable: %s", self.layer, exc)
            return {"agent": "<AgentName>", "output": "[WARN] LLM unavailable", "confidence": 0.0}

        self.publish_event({"type": "<AgentName>Completed", "agent": "<AgentName>"})
        return {
            "agent": "<AgentName>",
            "output": resp.output.strip(),
            "model_used": resp.model_alias,
        }