fast-next-template/backend/app/tasks/agent.py

# app/tasks/agent.py
"""
Agent execution tasks for Syndarix.

These tasks handle the lifecycle of AI agent instances:
- Spawning new agent instances from agent types
- Executing agent steps (LLM calls, tool execution)
- Terminating agent instances

Tasks are routed to the 'agent' queue for dedicated processing.
"""

import logging
from typing import Any

from app.celery_app import celery_app

logger = logging.getLogger(__name__)


@celery_app.task(bind=True, name="app.tasks.agent.run_agent_step")
def run_agent_step(
    self,
    agent_instance_id: str,
    context: dict[str, Any],
) -> dict[str, Any]:
    """
    Execute a single step of an agent's workflow.

    This task performs one iteration of the agent loop:
    1. Load agent instance state
    2. Call LLM with context and available tools
    3. Execute tool calls if any
    4. Update agent state
    5. Return result for next step or completion

    Args:
        agent_instance_id: UUID of the agent instance
        context: Current execution context including:
            - messages: Conversation history
            - tools: Available tool definitions
            - state: Agent state data
            - metadata: Project/task metadata

    Returns:
        dict with status and agent_instance_id
    """
    logger.info(
        f"Running agent step for instance {agent_instance_id} with context keys: {list(context.keys())}"
    )

    # TODO: Implement actual agent step execution
    # This will involve:
    # 1. Loading agent instance from database
    # 2. Calling LLM provider (via litellm or anthropic SDK)
    # 3. Processing tool calls through MCP servers
    # 4. Updating agent state in database
    # 5. Scheduling next step if needed

    return {
        "status": "pending",
        "agent_instance_id": agent_instance_id,
    }


@celery_app.task(bind=True, name="app.tasks.agent.spawn_agent")
def spawn_agent(
    self,
    agent_type_id: str,
    project_id: str,
    initial_context: dict[str, Any],
) -> dict[str, Any]:
    """
    Spawn a new agent instance from an agent type.

    This task creates a new agent instance:
    1. Load agent type configuration (model, expertise, personality)
    2. Create agent instance record in database
    3. Initialize agent state with project context
    4. Start first agent step

    Args:
        agent_type_id: UUID of the agent type template
        project_id: UUID of the project this agent will work on
        initial_context: Starting context including:
            - goal: High-level objective
            - constraints: Any limitations or requirements
            - assigned_issues: Issues to work on
            - autonomy_level: FULL_CONTROL, MILESTONE, or AUTONOMOUS

    Returns:
        dict with status, agent_type_id, and project_id
    """
    logger.info(f"Spawning agent of type {agent_type_id} for project {project_id}")

    # TODO: Implement agent spawning
    # This will involve:
    # 1. Loading agent type from database
    # 2. Creating agent instance record
    # 3. Setting up MCP tool access
    # 4. Initializing agent state
    # 5. Kicking off first step

    return {
        "status": "spawned",
        "agent_type_id": agent_type_id,
        "project_id": project_id,
    }


@celery_app.task(bind=True, name="app.tasks.agent.terminate_agent")
def terminate_agent(
    self,
    agent_instance_id: str,
    reason: str,
) -> dict[str, Any]:
    """
    Terminate an agent instance.

    This task gracefully shuts down an agent:
    1. Mark agent instance as terminated
    2. Save final state for audit
    3. Release any held resources
    4. Notify relevant subscribers

    Args:
        agent_instance_id: UUID of the agent instance
        reason: Reason for termination (completion, error, manual, budget)

    Returns:
        dict with status and agent_instance_id
    """
    logger.info(f"Terminating agent instance {agent_instance_id} with reason: {reason}")

    # TODO: Implement agent termination
    # This will involve:
    # 1. Loading agent instance
    # 2. Updating status to terminated
    # 3. Saving termination reason
    # 4. Cleaning up any pending tasks
    # 5. Sending termination event

    return {
        "status": "terminated",
        "agent_instance_id": agent_instance_id,
    }