Testing Agent Decisions

# Testing agent decisions with a mock LLM
import pytest
from unittest.mock import MagicMock, patch

# The agent function we want to test
def agent_decide_next_action(goal: str, state: dict, available_tools: list) -> dict:
    """
    In production: calls an LLM.
    In tests: mocked to return predictable decisions.
    Returns: {"action": str, "args": dict}
    """
    # This would normally call the LLM
    raise NotImplementedError("Use mock in tests")

class TestAgentDecisions:
    """Test that the agent makes correct decisions in known scenarios."""

    def test_research_task_starts_with_search(self):
        """Given a research goal and no prior steps, agent should search first."""
        mock_llm_response = {"action": "web_search", "args": {"query": "Python AI frameworks"}}

        with patch("agent_decide_next_action", return_value=mock_llm_response):
            result = mock_llm_response  # Simulating the mock

        assert result["action"] == "web_search"
        assert "query" in result["args"]
        assert len(result["args"]["query"]) > 0

    def test_agent_finishes_when_goal_achieved(self):
        """Agent should return FINISH when goal_achieved is in state."""
        state = {
            "goal": "Find Python frameworks",
            "data": {"goal_achieved": True, "result": "Django, FastAPI"},
            "history": [{"action": "web_search"}, {"action": "summarise"}]
        }
        mock_response = {"action": "FINISH", "args": {}}

        with patch("agent_decide_next_action", return_value=mock_response):
            result = mock_response

        assert result["action"] == "FINISH"

    def test_agent_escalates_after_max_retries(self):
        """Agent should escalate when the retry count exceeds the limit."""
        state = {
            "goal": "Get product data",
            "data": {},
            "retry_counts": {"get_product": 3}
        }
        mock_response = {"action": "ESCALATE",
                         "args": {"reason": "Max retries exceeded"}}

        with patch("agent_decide_next_action", return_value=mock_response):
            result = mock_response

        assert result["action"] == "ESCALATE"

# Run simple assertions (no pytest required for this demo)
print("Decision tests simulated:")
print("  ✓ research task starts with search")
print("  ✓ agent finishes when goal achieved")
print("  ✓ agent escalates after max retries")