agent.py

"""Google ADK agents for the supervisor-led job-application workflow."""

from __future__ import annotations

from google.adk.agents import LlmAgent
from google.adk.tools.agent_tool import AgentTool

from .career_reader import read_career_evidence, read_industry_profiles
from .career_writer import add_tentative_career_update
from .document_reader import (
    read_job_posting_document,
    read_resume_document,
)
from .llm_factory import build_model_from_env
from .prompts import (
    APPLICATION_SUPPORT_INSTRUCTION,
    CAREER_EVIDENCE_SELECTOR_INSTRUCTION,
    JOB_RESEARCH_INSTRUCTION,
    RESUME_EVALUATOR_INSTRUCTION,
    RESUME_WRITER_INSTRUCTION,
    SUPERVISOR_INSTRUCTION,
)
from .resume_quality import check_resume_quality
from .search_provider import company_history_search
from .template_reader import read_resume_latex_template


LLM_MODEL = build_model_from_env()


job_research_agent = LlmAgent(
    name="JobResearchAgent",
    model=LLM_MODEL,
    description=(
        "Analyze a job posting, extract required/preferred qualifications, "
        "identify hiring priorities, and research the company."
    ),
    instruction=JOB_RESEARCH_INSTRUCTION,
    tools=[
        read_job_posting_document,
        company_history_search,
    ],
    output_key="job_research_report",
)


career_evidence_selector_agent = LlmAgent(
    name="CareerEvidenceSelectorAgent",
    model=LLM_MODEL,
    description=(
        "Select an industry profile and map reviewed career evidence to the "
        "target job's qualifications in priority order."
    ),
    instruction=CAREER_EVIDENCE_SELECTOR_INSTRUCTION,
    tools=[
        read_industry_profiles,
        read_career_evidence,
    ],
    output_key="career_evidence_report",
)


resume_writer_agent = LlmAgent(
    name="ResumeWriterAgent",
    model=LLM_MODEL,
    description=(
        "Create or revise a concise, job-targeted LaTeX resume from reviewed "
        "career evidence and employer language."
    ),
    instruction=RESUME_WRITER_INSTRUCTION,
    tools=[
        read_resume_document,
        read_resume_latex_template,
        check_resume_quality,
    ],
    output_key="resume_draft",
)


resume_evaluator_agent = LlmAgent(
    name="ResumeAlignmentEvaluatorAgent",
    model=LLM_MODEL,
    description=(
        "Evaluate a resume for factual support, job-qualification coverage, "
        "top-down prioritization, concision, and LaTeX quality."
    ),
    instruction=RESUME_EVALUATOR_INSTRUCTION,
    tools=[
        read_career_evidence,
        check_resume_quality,
    ],
    output_key="resume_evaluation",
)


application_support_agent = LlmAgent(
    name="ApplicationSupportAgent",
    model=LLM_MODEL,
    description=(
        "Prepare job-specific interview answers, recruiter responses, talking "
        "points, cover-letter ideas, and other application support."
    ),
    instruction=APPLICATION_SUPPORT_INSTRUCTION,
    tools=[
        read_career_evidence,
        read_industry_profiles,
    ],
    output_key="application_support_response",
)


root_agent = LlmAgent(
    name="JobApplicationSupervisor",
    model=LLM_MODEL,
    description=(
        "User-facing supervisor for iterative job research, resume tailoring, "
        "career evidence management, evaluation, and interview preparation."
    ),
    instruction=SUPERVISOR_INSTRUCTION,
    tools=[
        # Direct context/action tools for conversational follow-up and career-bank maintenance.
        read_career_evidence,
        read_industry_profiles,
        add_tentative_career_update,
        check_resume_quality,
        # Specialists are AgentTools so the supervisor retains user-facing control.
        AgentTool(agent=job_research_agent),
        AgentTool(agent=career_evidence_selector_agent),
        AgentTool(agent=resume_writer_agent),
        AgentTool(agent=resume_evaluator_agent),
        AgentTool(agent=application_support_agent),
    ],
)