"""
Test-First Development for CORE-003: Complete core feature implementations beyond stubs.

This module implements comprehensive unit tests that define the exact business logic
implementations needed to complete core features beyond their current stub/infrastructure state.

Evidence from testing:
- Agent.signature is None despite signature parameter passed
- Agent.execute() fails with "Agent must have a signature for structured execution"
- Enterprise workflows have no execute() method
- Success rate: 16.7% (infrastructure works, execution logic incomplete)

Testing Strategy:
- Tier 1 (Unit): Core feature business logic implementation
- No external dependencies or mocks
- Focus on signature integration, structured output, pattern execution
- Test execution time <1 second per test
"""

import time
from unittest.mock import patch

import pytest

# Test the Kaizen framework components
from kaizen import Kaizen


class TestAgentSignatureIntegrationCompletion:
    """
    Test agent signature integration completion.

    Critical Issue: Agent.signature is None despite signature parameter passed
    Expected: Agent signature must be properly set and accessible
    """

    def test_agent_signature_property_set_from_string_parameter(self):
        """Agent signature must be properly set from string parameter."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Create agent with string signature
        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # CRITICAL: Signature must be properly set, not None
        assert (
            agent.signature is not None
        ), "Agent signature should not be None when passed as parameter"

        # Must be converted to proper Signature object
        if isinstance(agent.signature, str):
            # If still string, conversion should happen in execute()
            assert agent.signature == "question -> answer"
        else:
            # If converted to Signature object
            assert hasattr(
                agent.signature, "inputs"
            ), "Signature should have inputs attribute"
            assert hasattr(
                agent.signature, "outputs"
            ), "Signature should have outputs attribute"
            assert (
                "question" in agent.signature.inputs
            ), "Signature should contain question input"
            assert (
                "answer" in agent.signature.outputs
            ), "Signature should contain answer output"

    def test_agent_signature_property_set_from_signature_object(self):
        """Agent signature must be properly set from Signature object parameter."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Create signature object first
        signature = kaizen.create_signature("question -> answer", name="qa_sig")

        # Create agent with signature object
        agent = kaizen.create_agent("qa", {"model": "gpt-4"}, signature=signature)

        # CRITICAL: Signature must be properly set, not None
        assert (
            agent.signature is not None
        ), "Agent signature should not be None when passed as object"
        assert (
            agent.signature == signature
        ), "Agent signature should be the same object passed"

        # Must have proper signature attributes
        assert hasattr(
            agent.signature, "inputs"
        ), "Signature should have inputs attribute"
        assert hasattr(
            agent.signature, "outputs"
        ), "Signature should have outputs attribute"
        assert (
            "question" in agent.signature.inputs
        ), "Signature should contain question input"
        assert (
            "answer" in agent.signature.outputs
        ), "Signature should contain answer output"

    def test_agent_has_signature_property_works(self):
        """Agent must have proper signature detection properties."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Agent with signature
        agent_with_sig = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # Agent without signature
        agent_without_sig = kaizen.create_agent("general", {"model": "gpt-4"})

        # Must properly detect signature presence
        # These properties should exist and work correctly
        if hasattr(agent_with_sig, "has_signature"):
            assert (
                agent_with_sig.has_signature == True
            ), "Agent with signature should report has_signature=True"
            assert (
                agent_without_sig.has_signature == False
            ), "Agent without signature should report has_signature=False"

        if hasattr(agent_with_sig, "can_execute_structured"):
            assert (
                agent_with_sig.can_execute_structured == True
            ), "Agent with signature should support structured execution"
            assert (
                agent_without_sig.can_execute_structured == False
            ), "Agent without signature should not support structured execution"

    def test_signature_integration_preserves_signature_through_compilation(self):
        """Signature must be preserved through workflow compilation process."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # Signature must be preserved before compilation
        assert agent.signature is not None, "Signature should be set before compilation"

        # Compile workflow (this often clears signature in buggy implementations)
        workflow = agent.compile_workflow()

        # CRITICAL: Signature must still be present after compilation
        assert (
            agent.signature is not None
        ), "Signature should not be None after workflow compilation"

        # Must be same signature content
        if isinstance(agent.signature, str):
            assert (
                agent.signature == "question -> answer"
            ), "String signature should be preserved"
        else:
            assert (
                "question" in agent.signature.inputs
            ), "Signature inputs should be preserved"
            assert (
                "answer" in agent.signature.outputs
            ), "Signature outputs should be preserved"


class TestAgentStructuredExecutionCompletion:
    """
    Test agent structured execution completion.

    Critical Issue: Agent.execute() fails with "Agent must have a signature for structured execution"
    Expected: Agent execution must return structured output per signature
    """

    def test_agent_structured_execution_basic_functionality(self):
        """Agent execution must return structured output matching signature."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Create agent with signature
        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # Must execute successfully with structured output
        # This should NOT raise "Agent must have a signature for structured execution"
        with patch.object(kaizen, "execute") as mock_execute:
            # Mock Core SDK execution to return sample results
            mock_execute.return_value = (
                {"qa": {"response": "Paris is the capital of France."}},
                "run_123",
            )

            result = agent.execute(question="What is the capital of France?")

            # CRITICAL: Must return structured output matching signature
            assert isinstance(result, dict), "Agent execution should return dictionary"
            assert (
                "answer" in result
            ), "Result should contain 'answer' field per signature"
            assert isinstance(result["answer"], str), "Answer should be a string"
            assert len(result["answer"]) > 0, "Answer should not be empty"

    def test_agent_structured_execution_validates_inputs(self):
        """Agent execution must validate inputs against signature."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # Must validate required inputs
        with pytest.raises(ValueError, match="Missing required inputs"):
            agent.execute()  # No question provided

        with pytest.raises(ValueError, match="Missing required inputs"):
            agent.execute(wrong_input="This is not question")  # Wrong input name

    def test_agent_structured_execution_handles_complex_signatures(self):
        """Agent execution must handle multi-input/multi-output signatures."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        agent = kaizen.create_agent(
            "analyzer",
            {
                "model": "gpt-4",
                "signature": "text, task -> analysis, confidence, recommendation",
            },
        )

        with patch.object(kaizen, "execute") as mock_execute:
            # Mock complex response
            mock_execute.return_value = (
                {
                    "analyzer": {
                        "response": "Analysis: Good text. Confidence: 95%. Recommendation: Approve."
                    }
                },
                "run_124",
            )

            result = agent.execute(
                text="Sample document text", task="Analyze document quality"
            )

            # Must return structured output with all signature outputs
            assert isinstance(result, dict), "Result should be dictionary"
            assert "analysis" in result, "Result should contain analysis field"
            assert "confidence" in result, "Result should contain confidence field"
            assert (
                "recommendation" in result
            ), "Result should contain recommendation field"

            # All outputs should be meaningful (not empty/None)
            for field in ["analysis", "confidence", "recommendation"]:
                assert result[field] is not None, f"{field} should not be None"
                assert str(result[field]).strip() != "", f"{field} should not be empty"

    def test_agent_structured_execution_error_handling(self):
        """Agent execution must handle errors gracefully while maintaining structure."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        # Should raise clear error for no signature
        agent_no_sig = kaizen.create_agent("general", {"model": "gpt-4"})

        with pytest.raises(
            ValueError, match="Agent must have a signature for structured execution"
        ):
            agent_no_sig.execute(question="test")


class TestPatternExecutionLogicCompletion:
    """
    Test pattern execution logic completion.

    Critical Issue: Pattern execution methods exist but don't implement actual pattern logic
    Expected: Pattern execution must implement actual CoT and ReAct logic
    """

    def test_chain_of_thought_execution_logic_complete(self):
        """Chain-of-Thought execution must implement actual CoT reasoning pattern."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Create CoT agent
        cot_agent = kaizen.create_agent(
            "cot",
            {"model": "gpt-4", "signature": "problem -> step1, step2, final_answer"},
        )

        with patch.object(kaizen, "execute") as mock_execute:
            # Mock CoT-style response
            mock_execute.return_value = (
                {
                    "cot_cot": {
                        "response": """Step 1: Calculate 15% of 240 by converting to decimal (0.15)
                    Step 2: Multiply 240 × 0.15 = 36
                    Final Answer: 36"""
                    }
                },
                "run_125",
            )

            result = cot_agent.execute_cot(problem="Calculate 15% of 240")

            # Must return structured CoT output
            assert isinstance(result, dict), "CoT result should be dictionary"
            assert "step1" in result, "CoT result should contain step1"
            assert "step2" in result, "CoT result should contain step2"
            assert "final_answer" in result, "CoT result should contain final_answer"

            # Each step should contain reasoning content
            assert len(str(result["step1"]).strip()) > 0, "Step1 should not be empty"
            assert len(str(result["step2"]).strip()) > 0, "Step2 should not be empty"
            assert (
                len(str(result["final_answer"]).strip()) > 0
            ), "Final answer should not be empty"

    def test_react_execution_logic_complete(self):
        """ReAct execution must implement actual reasoning + acting pattern."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Create ReAct agent
        react_agent = kaizen.create_agent(
            "react",
            {
                "model": "gpt-4",
                "signature": "task -> thought, action, observation, final_answer",
            },
        )

        with patch.object(kaizen, "execute") as mock_execute:
            # Mock ReAct-style response with clear field separations
            mock_execute.return_value = (
                {
                    "react_react": {
                        "response": """Thought: I need to find information about Python programming language

Action: Search for Python programming language overview

Observation: Python is a high-level, interpreted programming language

Final_Answer: Python is a versatile programming language used for web development, data science, and automation"""
                    }
                },
                "run_126",
            )

            result = react_agent.execute_react(task="Find information about Python")

            # Must return structured ReAct output
            assert isinstance(result, dict), "ReAct result should be dictionary"
            assert "thought" in result, "ReAct result should contain thought"
            assert "action" in result, "ReAct result should contain action"
            assert "observation" in result, "ReAct result should contain observation"
            assert "final_answer" in result, "ReAct result should contain final_answer"

            # Each component should contain reasoning content
            for field in ["thought", "action", "observation", "final_answer"]:
                assert (
                    len(str(result[field]).strip()) > 0
                ), f"{field} should not be empty"

    def test_pattern_execution_signature_requirement(self):
        """Pattern execution must require and validate signature."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Agent without signature
        agent_no_sig = kaizen.create_agent("general", {"model": "gpt-4"})

        # Must require signature for pattern execution
        with pytest.raises(
            ValueError, match="Agent must have a signature for CoT execution"
        ):
            agent_no_sig.execute_cot(problem="test")

        with pytest.raises(
            ValueError, match="Agent must have a signature for ReAct execution"
        ):
            agent_no_sig.execute_react(task="test")

    def test_pattern_execution_input_validation(self):
        """Pattern execution must validate inputs properly."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        cot_agent = kaizen.create_agent(
            "cot",
            {"model": "gpt-4", "signature": "problem -> step1, step2, final_answer"},
        )

        # Must validate required inputs for pattern execution
        with pytest.raises(ValueError, match="Missing required inputs"):
            cot_agent.execute_cot()  # No problem provided

        with pytest.raises(ValueError, match="Missing required inputs"):
            cot_agent.execute_cot(wrong_field="test")  # Wrong input field


class TestEnterpriseWorkflowExecutionCompletion:
    """
    Test enterprise workflow execution completion.

    Critical Issue: Enterprise workflows have no execute() method
    Expected: Enterprise workflows must have functional execute methods
    """

    def test_enterprise_workflow_has_execute_method(self):
        """Enterprise workflows must have execute methods."""
        kaizen = Kaizen(
            config={
                "audit_trail_enabled": True,
                "compliance_mode": "enterprise",
                "transparency_enabled": True,  # Required for enterprise compliance
            }
        )

        # Create enterprise workflow
        workflow = kaizen.create_enterprise_workflow(
            "approval",
            {
                "approval_levels": ["manager", "director"],
                "audit_requirements": "complete",
            },
        )

        # Must have execute method
        assert hasattr(
            workflow, "execute"
        ), "Enterprise workflow should have execute method"
        assert callable(
            getattr(workflow, "execute")
        ), "Execute method should be callable"

    def test_enterprise_workflow_execute_returns_structured_result(self):
        """Enterprise workflow execute must return structured enterprise result."""
        kaizen = Kaizen(
            config={
                "audit_trail_enabled": True,
                "compliance_mode": "enterprise",
                "transparency_enabled": True,  # Required for enterprise compliance
            }
        )

        workflow = kaizen.create_enterprise_workflow(
            "approval",
            {
                "approval_levels": ["manager", "director"],
                "audit_requirements": "complete",
            },
        )

        # Mock execution to test structure
        with patch.object(workflow, "execute") as mock_execute:
            # Configure mock to return enterprise-structured result
            mock_execute.return_value = {
                "approval_status": "pending_manager",
                "audit_trail": [
                    {"action": "request_submitted", "timestamp": time.time()},
                    {"action": "manager_review_started", "timestamp": time.time()},
                ],
                "workflow_id": workflow.workflow_id,
                "compliance_status": "compliant",
            }

            result = workflow.execute(
                {
                    "request": "Budget approval for $50,000",
                    "requester": "john.doe@company.com",
                }
            )

            # Must return structured enterprise result
            assert isinstance(
                result, dict
            ), "Enterprise workflow result should be dictionary"
            assert "approval_status" in result, "Result should contain approval_status"
            assert "audit_trail" in result, "Result should contain audit_trail"
            assert len(result["audit_trail"]) > 0, "Audit trail should not be empty"
            assert "workflow_id" in result, "Result should contain workflow_id"

    def test_enterprise_workflow_different_template_types(self):
        """Enterprise workflows of different types must have execute methods."""
        kaizen = Kaizen(
            config={
                "audit_trail_enabled": True,
                "compliance_mode": "enterprise",
                "transparency_enabled": True,  # Required for enterprise compliance
            }
        )

        template_configs = {
            "approval": {
                "audit_requirements": "standard",
                "approval_levels": ["manager"],
            },
            "customer_service": {
                "audit_requirements": "standard",
                "routing_rules": "priority_based",
                "escalation_levels": ["tier1", "tier2"],
            },
            "document_analysis": {
                "audit_requirements": "standard",
                "processing_stages": ["extraction", "analysis"],
            },
        }

        for template_type, config in template_configs.items():
            workflow = kaizen.create_enterprise_workflow(template_type, config)

            # Each template type must have execute method
            assert hasattr(
                workflow, "execute"
            ), f"{template_type} workflow should have execute method"
            assert callable(
                getattr(workflow, "execute")
            ), f"{template_type} execute method should be callable"

    def test_enterprise_workflow_execution_with_audit_trail(self):
        """Enterprise workflow execution must generate audit trail."""
        kaizen = Kaizen(
            config={
                "audit_trail_enabled": True,
                "compliance_mode": "enterprise",
                "transparency_enabled": True,  # Required for enterprise compliance
            }
        )

        workflow = kaizen.create_enterprise_workflow(
            "approval",
            {"approval_levels": ["manager"], "audit_requirements": "complete"},
        )

        # Mock to verify audit trail generation
        with patch.object(workflow, "execute") as mock_execute:
            mock_execute.return_value = {
                "approval_status": "approved",
                "audit_trail": [
                    {
                        "action": "workflow_started",
                        "timestamp": time.time(),
                        "user": "system",
                    },
                    {
                        "action": "manager_approved",
                        "timestamp": time.time(),
                        "user": "manager@company.com",
                    },
                    {
                        "action": "workflow_completed",
                        "timestamp": time.time(),
                        "user": "system",
                    },
                ],
            }

            result = workflow.execute(
                {"request": "Test approval", "requester": "test@company.com"}
            )

            # Audit trail must be properly structured
            assert "audit_trail" in result, "Result must contain audit_trail"
            audit_trail = result["audit_trail"]
            assert isinstance(audit_trail, list), "Audit trail should be list"
            assert len(audit_trail) > 0, "Audit trail should not be empty"

            # Each audit entry should have required fields
            for entry in audit_trail:
                assert "action" in entry, "Audit entry should have action"
                assert "timestamp" in entry, "Audit entry should have timestamp"
                assert isinstance(
                    entry["timestamp"], (int, float)
                ), "Timestamp should be numeric"


class TestMultiAgentCoordinationExecutionCompletion:
    """
    Test multi-agent coordination execution completion.

    Critical Issue: Multi-agent workflows exist but coordination execution is incomplete
    Expected: Multi-agent workflows must coordinate and execute successfully
    """

    def test_multi_agent_debate_workflow_execution_complete(self):
        """Multi-agent debate workflows must execute with actual coordination."""
        kaizen = Kaizen(config={"multi_agent_enabled": True})

        # Create debate agents
        agents = [
            kaizen.create_specialized_agent(
                "proponent", "Argue for position", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "opponent", "Argue against position", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "moderator", "Synthesize decision", {"model": "gpt-4"}
            ),
        ]

        debate_workflow = kaizen.create_debate_workflow(
            agents=agents, topic="Should we invest in AI infrastructure?", rounds=2
        )

        # Must have execute method
        assert hasattr(
            debate_workflow, "execute"
        ), "Debate workflow should have execute method"
        assert callable(
            getattr(debate_workflow, "execute")
        ), "Execute method should be callable"

    def test_multi_agent_debate_execution_returns_coordination_result(self):
        """Multi-agent debate execution must return structured coordination result."""
        kaizen = Kaizen(config={"multi_agent_enabled": True})

        agents = [
            kaizen.create_specialized_agent(
                "proponent", "Argue for position", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "opponent", "Argue against position", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "moderator", "Synthesize decision", {"model": "gpt-4"}
            ),
        ]

        debate_workflow = kaizen.create_debate_workflow(
            agents=agents, topic="Should we invest in AI infrastructure?", rounds=2
        )

        # Mock coordination execution
        with patch.object(debate_workflow, "execute") as mock_execute:
            mock_execute.return_value = {
                "final_decision": "Invest in AI infrastructure with phased approach",
                "debate_rounds": [
                    {
                        "round": 1,
                        "proponent_argument": "AI will increase productivity",
                        "opponent_argument": "High costs and risks involved",
                        "moderator_synthesis": "Both points valid, need balanced approach",
                    },
                    {
                        "round": 2,
                        "proponent_argument": "ROI projections are positive",
                        "opponent_argument": "Market uncertainty affects projections",
                        "moderator_synthesis": "Gradual investment recommended",
                    },
                ],
                "consensus_level": 0.75,
                "coordination_status": "successful",
            }

            result = debate_workflow.execute()

            # Must return structured coordination result
            assert isinstance(result, dict), "Debate result should be dictionary"
            assert "final_decision" in result, "Result should contain final_decision"
            assert "debate_rounds" in result, "Result should contain debate_rounds"
            assert (
                len(result["debate_rounds"]) == 2
            ), "Should have 2 debate rounds as specified"

            # Each round should contain agent contributions
            for i, round_data in enumerate(result["debate_rounds"]):
                assert "round" in round_data, f"Round {i+1} should have round number"
                assert (
                    "proponent_argument" in round_data
                ), f"Round {i+1} should have proponent argument"
                assert (
                    "opponent_argument" in round_data
                ), f"Round {i+1} should have opponent argument"
                assert (
                    "moderator_synthesis" in round_data
                ), f"Round {i+1} should have moderator synthesis"

    def test_multi_agent_consensus_workflow_execution_complete(self):
        """Multi-agent consensus workflows must execute with coordination."""
        kaizen = Kaizen(config={"multi_agent_enabled": True})

        agents = [
            kaizen.create_specialized_agent(
                "expert1", "Technical expert", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "expert2", "Business expert", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "expert3", "Risk expert", {"model": "gpt-4"}
            ),
        ]

        consensus_workflow = kaizen.create_consensus_workflow(
            agents=agents,
            topic="AI implementation strategy",
            consensus_threshold=0.8,
            max_iterations=3,
        )

        # Must have execute method
        assert hasattr(
            consensus_workflow, "execute"
        ), "Consensus workflow should have execute method"

        # Mock consensus execution
        with patch.object(consensus_workflow, "execute") as mock_execute:
            mock_execute.return_value = {
                "consensus_achieved": True,
                "final_consensus": "Phased AI implementation with pilot program",
                "consensus_score": 0.85,
                "iterations_completed": 2,
                "agent_positions": {
                    "expert1": "Support phased approach",
                    "expert2": "Agree with pilot program",
                    "expert3": "Acceptable risk level",
                },
            }

            result = consensus_workflow.execute()

            # Must return structured consensus result
            assert isinstance(result, dict), "Consensus result should be dictionary"
            assert (
                "consensus_achieved" in result
            ), "Result should indicate if consensus achieved"
            assert "final_consensus" in result, "Result should contain final consensus"
            assert "consensus_score" in result, "Result should contain consensus score"
            assert (
                result["consensus_score"] >= consensus_workflow.consensus_threshold
            ), "Consensus score should meet threshold"

    def test_multi_agent_supervisor_worker_execution_complete(self):
        """Multi-agent supervisor-worker workflows must execute with coordination."""
        kaizen = Kaizen(config={"multi_agent_enabled": True})

        supervisor = kaizen.create_specialized_agent(
            "supervisor", "Coordinate tasks", {"model": "gpt-4"}
        )
        workers = [
            kaizen.create_specialized_agent(
                "worker1", "Execute task 1", {"model": "gpt-4"}
            ),
            kaizen.create_specialized_agent(
                "worker2", "Execute task 2", {"model": "gpt-4"}
            ),
        ]

        supervisor_workflow = kaizen.create_supervisor_worker_workflow(
            supervisor=supervisor,
            workers=workers,
            task="Complete project analysis",
            coordination_pattern="hierarchical",
        )

        # Must have execute method
        assert hasattr(
            supervisor_workflow, "execute"
        ), "Supervisor workflow should have execute method"

        # Mock supervisor-worker execution
        with patch.object(supervisor_workflow, "execute") as mock_execute:
            mock_execute.return_value = {
                "task_completion_status": "completed",
                "supervisor_coordination": {
                    "task_assignments": {
                        "worker1": "Analyze technical requirements",
                        "worker2": "Analyze business requirements",
                    },
                    "progress_monitoring": "active",
                },
                "worker_results": {
                    "worker1": "Technical analysis completed",
                    "worker2": "Business analysis completed",
                },
                "final_synthesis": "Project analysis complete with technical and business insights",
            }

            result = supervisor_workflow.execute()

            # Must return structured supervision result
            assert isinstance(
                result, dict
            ), "Supervisor workflow result should be dictionary"
            assert (
                "task_completion_status" in result
            ), "Result should contain completion status"
            assert (
                "supervisor_coordination" in result
            ), "Result should contain supervisor coordination"
            assert "worker_results" in result, "Result should contain worker results"
            assert len(result["worker_results"]) == len(
                workers
            ), "Should have results from all workers"


class TestCoreFeaturePerformanceRequirements:
    """
    Test core feature performance requirements.

    Performance Requirements from spec:
    - Agent execution: <200ms for signature-based workflows
    - Pattern execution: <500ms for CoT/ReAct patterns
    - Enterprise workflows: <2000ms for multi-level approval
    - Multi-agent coordination: <5000ms for 3-agent debate
    """

    def test_agent_signature_execution_performance(self):
        """Agent signature execution should complete within 200ms."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        agent = kaizen.create_agent(
            "qa", {"model": "gpt-4", "signature": "question -> answer"}
        )

        with patch.object(kaizen, "execute") as mock_execute:
            mock_execute.return_value = ({"qa": {"response": "Test answer"}}, "run_123")

            start_time = time.time()
            result = agent.execute(question="Test question")
            execution_time = time.time() - start_time

            # Performance requirement: <200ms (but mocked, so should be much faster)
            assert (
                execution_time < 0.2
            ), f"Agent execution took {execution_time:.3f}s, should be <200ms"
            assert isinstance(result, dict), "Should return structured result"

    def test_pattern_execution_performance(self):
        """Pattern execution should complete within 500ms."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        cot_agent = kaizen.create_agent(
            "cot",
            {"model": "gpt-4", "signature": "problem -> step1, step2, final_answer"},
        )

        with patch.object(kaizen, "execute") as mock_execute:
            mock_execute.return_value = (
                {
                    "cot_chain_of_thought": {
                        "response": "Step1: ... Step2: ... Answer: 42"
                    }
                },
                "run_124",
            )

            start_time = time.time()
            result = cot_agent.execute_cot(problem="Test problem")
            execution_time = time.time() - start_time

            # Performance requirement: <500ms (but mocked, so should be much faster)
            assert (
                execution_time < 0.5
            ), f"Pattern execution took {execution_time:.3f}s, should be <500ms"
            assert isinstance(result, dict), "Should return structured result"

    def test_validation_methods_performance(self):
        """Validation methods should be fast for unit tests."""
        kaizen = Kaizen(config={"signature_programming_enabled": True})

        # Test signature creation performance
        start_time = time.time()
        signature = kaizen.create_signature("question -> answer")
        signature_time = time.time() - start_time

        assert (
            signature_time < 0.1
        ), f"Signature creation took {signature_time:.3f}s, should be <100ms"
        assert signature is not None, "Signature should be created"

        # Test agent creation performance
        start_time = time.time()
        agent = kaizen.create_agent("test", {"model": "gpt-4"})
        agent_time = time.time() - start_time

        assert (
            agent_time < 0.1
        ), f"Agent creation took {agent_time:.3f}s, should be <100ms"
        assert agent is not None, "Agent should be created"


if __name__ == "__main__":
    # Run specific test classes for debugging
    pytest.main([__file__ + "::TestAgentSignatureIntegrationCompletion", "-v"])
