# TASK_48: km_autonomous_agent - Self-Managing Automation Agents with Learning
**Created By**: Agent_1 (Advanced Enhancement) | **Priority**: HIGH | **Duration**: 8 hours
**Technique Focus**: Autonomous Systems + Design by Contract + Type Safety + Machine Learning + Self-Optimization
**Size Constraint**: Target <250 lines/module, Max 400 if splitting awkward
## 🚦 Status & Assignment
**Status**: COMPLETED ✅
**Assigned**: Agent_ADDER+
**Dependencies**: AI processing (TASK_40), Smart suggestions (TASK_41), All enterprise tasks
**Blocking**: Autonomous automation workflows and self-managing system capabilities
## 📖 Required Reading (Complete before starting)
- [ ] **AI Processing**: development/tasks/TASK_40.md - AI integration and intelligent processing
- [ ] **Smart Suggestions**: development/tasks/TASK_41.md - Learning algorithms and behavior analysis
- [ ] **Audit System**: development/tasks/TASK_43.md - Self-monitoring and compliance tracking
- [ ] **Plugin Ecosystem**: development/tasks/TASK_39.md - Extensible agent architecture
- [ ] **Foundation Architecture**: src/server/tools/ - Tool patterns for autonomous execution
## 🎯 Problem Analysis
**Classification**: Autonomous Intelligence Infrastructure Gap
**Gap Identified**: No self-managing agents, autonomous decision-making, or adaptive automation systems
**Impact**: Cannot create intelligent automation that learns, adapts, and operates independently without constant human intervention
<thinking>
Root Cause Analysis:
1. Current platform requires manual configuration and monitoring of all automation
2. No autonomous agents that can learn from experience and adapt behavior
3. Missing self-healing capabilities for automation failures and errors
4. Cannot optimize automation performance automatically based on patterns
5. No intelligent resource management or load balancing capabilities
6. Essential for creating truly autonomous automation that improves over time
7. Should integrate with AI processing and learning systems for intelligence
</thinking>
## ✅ Implementation Subtasks (Sequential completion)
### Phase 1: Architecture & Design
- [x] **Agent types**: Define branded types for autonomous agents, goals, and behaviors ✅
- [x] **Learning framework**: Machine learning integration for adaptive behavior ✅
- [x] **Decision engine**: Autonomous decision-making and goal prioritization ✅
### Phase 2: Core Agent System
- [x] **Agent lifecycle**: Creation, initialization, execution, and termination of autonomous agents ✅
- [x] **Goal management**: Dynamic goal setting, prioritization, and achievement tracking ✅
- [x] **Resource management**: Intelligent resource allocation and optimization ✅
- [x] **Communication**: Inter-agent communication and coordination protocols ✅
### Phase 3: Learning & Adaptation
- [x] **Experience learning**: Learn from successes and failures to improve performance ✅
- [x] **Pattern recognition**: Identify patterns in automation usage and optimize accordingly ✅
- [x] **Self-optimization**: Automatically adjust parameters and strategies for better results ✅
- [x] **Predictive planning**: Anticipate needs and proactively execute automation ✅
### Phase 4: Autonomous Operations
- [x] **Self-healing**: Automatic error detection, diagnosis, and recovery ✅
- [x] **Load balancing**: Intelligent distribution of work across available resources ✅
- [x] **Monitoring**: Continuous self-monitoring and health assessment ✅
- [x] **Scaling**: Automatic scaling of agents based on workload demands ✅
### Phase 5: Integration & Safety
- [x] **Safety constraints**: Robust safety mechanisms and constraint validation ✅
- [x] **Human oversight**: Configurable human approval for critical decisions ✅
- [x] **TESTING.md update**: Autonomous agent testing coverage and validation ✅
- [x] **Ethical compliance**: Ensure autonomous behavior aligns with ethical guidelines ✅
## 🔧 Implementation Files & Specifications
```
src/server/tools/autonomous_agent_tools.py # Main autonomous agent tool implementation
src/core/autonomous_systems.py # Autonomous agent type definitions
src/agents/agent_manager.py # Agent lifecycle and management
src/agents/decision_engine.py # Autonomous decision-making system
src/agents/learning_system.py # Agent learning and adaptation
src/agents/goal_manager.py # Goal setting and achievement tracking
src/agents/resource_optimizer.py # Resource management and optimization
src/agents/communication_hub.py # Inter-agent communication
tests/tools/test_autonomous_agent_tools.py # Unit and integration tests
tests/property_tests/test_autonomous_systems.py # Property-based autonomy validation
```
### km_autonomous_agent Tool Specification
```python
@mcp.tool()
async def km_autonomous_agent(
operation: str, # create|start|stop|configure|monitor|optimize
agent_type: str = "general", # general|optimizer|monitor|learner|coordinator
agent_config: Optional[Dict] = None, # Agent configuration and parameters
goals: Optional[List[Dict]] = None, # Goals and objectives for the agent
learning_mode: bool = True, # Enable learning and adaptation
autonomy_level: str = "supervised", # manual|supervised|autonomous|full
resource_limits: Optional[Dict] = None, # Resource usage limits
safety_constraints: Optional[Dict] = None, # Safety rules and constraints
communication_enabled: bool = True, # Enable inter-agent communication
monitoring_interval: int = 60, # Self-monitoring interval in seconds
optimization_frequency: str = "hourly", # never|hourly|daily|weekly|adaptive
human_approval_required: bool = False, # Require human approval for actions
timeout: int = 300, # Agent operation timeout
ctx = None
) -> Dict[str, Any]:
```
### Autonomous Systems Type System
```python
from dataclasses import dataclass
from typing import List, Optional, Union, Dict, Any, Set, Callable, Type
from enum import Enum
from datetime import datetime, timedelta
import asyncio
import uuid
class AgentType(Enum):
"""Types of autonomous agents."""
GENERAL = "general" # General-purpose automation agent
OPTIMIZER = "optimizer" # Performance optimization agent
MONITOR = "monitor" # System monitoring and alerting agent
LEARNER = "learner" # Learning and pattern recognition agent
COORDINATOR = "coordinator" # Multi-agent coordination agent
HEALER = "healer" # Self-healing and recovery agent
PLANNER = "planner" # Predictive planning agent
RESOURCE_MANAGER = "resource_manager" # Resource management agent
class AutonomyLevel(Enum):
"""Levels of agent autonomy."""
MANUAL = "manual" # Manual control, no autonomous actions
SUPERVISED = "supervised" # Autonomous with human oversight
AUTONOMOUS = "autonomous" # Full autonomy within constraints
FULL = "full" # Maximum autonomy with minimal constraints
class GoalPriority(Enum):
"""Goal priority levels."""
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
EMERGENCY = "emergency"
class AgentStatus(Enum):
"""Agent operational status."""
CREATED = "created"
INITIALIZING = "initializing"
ACTIVE = "active"
LEARNING = "learning"
OPTIMIZING = "optimizing"
PAUSED = "paused"
ERROR = "error"
TERMINATED = "terminated"
@dataclass(frozen=True)
class AgentGoal:
"""Goal specification for autonomous agents."""
goal_id: str
description: str
priority: GoalPriority
target_metrics: Dict[str, float]
constraints: Dict[str, Any]
deadline: Optional[datetime] = None
success_criteria: List[str] = field(default_factory=list)
dependencies: List[str] = field(default_factory=list)
created_at: datetime = field(default_factory=datetime.utcnow)
@require(lambda self: len(self.goal_id) > 0)
@require(lambda self: len(self.description) > 0)
@require(lambda self: len(self.target_metrics) > 0)
def __post_init__(self):
pass
def is_overdue(self) -> bool:
"""Check if goal is past its deadline."""
return self.deadline is not None and datetime.utcnow() > self.deadline
def get_urgency_score(self) -> float:
"""Calculate urgency score based on priority and deadline."""
priority_weights = {
GoalPriority.LOW: 0.2,
GoalPriority.MEDIUM: 0.4,
GoalPriority.HIGH: 0.6,
GoalPriority.CRITICAL: 0.8,
GoalPriority.EMERGENCY: 1.0
}
base_score = priority_weights[self.priority]
# Adjust for deadline urgency
if self.deadline:
time_remaining = (self.deadline - datetime.utcnow()).total_seconds()
if time_remaining <= 0:
return 1.0 # Overdue goals get maximum urgency
elif time_remaining < 3600: # Less than 1 hour
return min(1.0, base_score * 1.5)
elif time_remaining < 86400: # Less than 1 day
return min(1.0, base_score * 1.2)
return base_score
@dataclass(frozen=True)
class AgentAction:
"""Action taken by an autonomous agent."""
action_id: str
agent_id: str
action_type: str
parameters: Dict[str, Any]
goal_id: Optional[str] = None
rationale: Optional[str] = None
confidence: float = 0.5
estimated_impact: float = 0.0
resource_cost: Dict[str, float] = field(default_factory=dict)
safety_validated: bool = False
executed_at: Optional[datetime] = None
@require(lambda self: len(self.action_id) > 0)
@require(lambda self: len(self.agent_id) > 0)
@require(lambda self: 0.0 <= self.confidence <= 1.0)
def __post_init__(self):
pass
def is_high_confidence(self, threshold: float = 0.8) -> bool:
"""Check if action has high confidence."""
return self.confidence >= threshold
def get_risk_score(self) -> float:
"""Calculate risk score for the action."""
base_risk = 1.0 - self.confidence
# Adjust for estimated impact
if self.estimated_impact > 0.8:
base_risk *= 1.5
elif self.estimated_impact < 0.2:
base_risk *= 0.5
# Adjust for safety validation
if not self.safety_validated:
base_risk *= 2.0
return min(1.0, base_risk)
@dataclass(frozen=True)
class LearningExperience:
"""Learning experience for agent improvement."""
experience_id: str
agent_id: str
context: Dict[str, Any]
action_taken: AgentAction
outcome: Dict[str, Any]
success: bool
learning_value: float
timestamp: datetime = field(default_factory=datetime.utcnow)
@require(lambda self: len(self.experience_id) > 0)
@require(lambda self: len(self.agent_id) > 0)
@require(lambda self: 0.0 <= self.learning_value <= 1.0)
def __post_init__(self):
pass
def extract_patterns(self) -> Dict[str, Any]:
"""Extract learnable patterns from experience."""
patterns = {
"success_indicators": [],
"failure_indicators": [],
"context_factors": [],
"optimal_parameters": {}
}
if self.success:
patterns["success_indicators"] = list(self.context.keys())
patterns["optimal_parameters"] = self.action_taken.parameters.copy()
else:
patterns["failure_indicators"] = list(self.context.keys())
patterns["context_factors"] = [
key for key, value in self.context.items()
if isinstance(value, (int, float, bool, str))
]
return patterns
class AutonomousAgent:
"""Base autonomous agent with learning capabilities."""
def __init__(self, agent_id: str, agent_type: AgentType, config: Dict[str, Any]):
self.agent_id = agent_id
self.agent_type = agent_type
self.config = config
self.status = AgentStatus.CREATED
self.goals: List[AgentGoal] = []
self.experiences: List[LearningExperience] = []
self.learned_patterns: Dict[str, Any] = {}
self.performance_metrics: Dict[str, float] = {}
self.created_at = datetime.utcnow()
self.last_active = datetime.utcnow()
self.autonomy_level = AutonomyLevel(config.get('autonomy_level', 'supervised'))
self.safety_constraints = config.get('safety_constraints', {})
self.resource_limits = config.get('resource_limits', {})
self.human_approval_required = config.get('human_approval_required', False)
async def initialize(self) -> Either[AgentError, None]:
"""Initialize the autonomous agent."""
try:
self.status = AgentStatus.INITIALIZING
# Initialize AI processor if available
if 'ai_processor' in self.config:
self.ai_processor = self.config['ai_processor']
# Load learned patterns from previous sessions
await self._load_learned_patterns()
# Initialize performance metrics
self.performance_metrics = {
"goals_achieved": 0.0,
"success_rate": 0.0,
"learning_rate": 0.0,
"efficiency_score": 0.0,
"autonomy_score": 0.0
}
self.status = AgentStatus.ACTIVE
return Either.right(None)
except Exception as e:
self.status = AgentStatus.ERROR
return Either.left(AgentError.initialization_failed(str(e)))
async def add_goal(self, goal: AgentGoal) -> Either[AgentError, None]:
"""Add a new goal for the agent."""
try:
# Validate goal constraints
if not self._validate_goal_constraints(goal):
return Either.left(AgentError.invalid_goal_constraints())
# Check for conflicting goals
conflicts = self._check_goal_conflicts(goal)
if conflicts:
return Either.left(AgentError.conflicting_goals(conflicts))
self.goals.append(goal)
await self._prioritize_goals()
return Either.right(None)
except Exception as e:
return Either.left(AgentError.goal_addition_failed(str(e)))
async def execute_autonomous_cycle(self) -> Either[AgentError, Dict[str, Any]]:
"""Execute one cycle of autonomous operation."""
try:
if self.status != AgentStatus.ACTIVE:
return Either.left(AgentError.agent_not_active())
cycle_start = datetime.utcnow()
actions_taken = []
# 1. Assess current situation
situation_assessment = await self._assess_situation()
# 2. Select highest priority goal
current_goal = await self._select_current_goal()
if not current_goal:
return Either.right({"status": "no_goals", "actions_taken": 0})
# 3. Plan actions to achieve goal
planned_actions = await self._plan_actions(current_goal, situation_assessment)
# 4. Execute actions with safety validation
for action in planned_actions:
if await self._validate_action_safety(action):
if self.human_approval_required and action.get_risk_score() > 0.5:
# Request human approval for high-risk actions
approval = await self._request_human_approval(action)
if not approval:
continue
execution_result = await self._execute_action(action)
actions_taken.append({
"action": action.action_type,
"result": execution_result.is_right(),
"goal": current_goal.goal_id
})
# Learn from the execution
await self._learn_from_action(action, execution_result, situation_assessment)
# 5. Update performance metrics
await self._update_performance_metrics()
# 6. Self-optimization
if len(self.experiences) > 10: # Need sufficient experience for optimization
await self._self_optimize()
cycle_duration = (datetime.utcnow() - cycle_start).total_seconds()
result = {
"status": "completed",
"actions_taken": len(actions_taken),
"current_goal": current_goal.goal_id,
"cycle_duration": cycle_duration,
"performance_metrics": self.performance_metrics.copy()
}
self.last_active = datetime.utcnow()
return Either.right(result)
except Exception as e:
return Either.left(AgentError.execution_cycle_failed(str(e)))
async def _assess_situation(self) -> Dict[str, Any]:
"""Assess current situation and context."""
return {
"timestamp": datetime.utcnow().isoformat(),
"agent_status": self.status.value,
"active_goals": len([g for g in self.goals if not g.is_overdue()]),
"resource_usage": await self._get_resource_usage(),
"recent_performance": self._get_recent_performance(),
"system_health": await self._check_system_health()
}
async def _select_current_goal(self) -> Optional[AgentGoal]:
"""Select the highest priority goal to work on."""
active_goals = [g for g in self.goals if not g.is_overdue()]
if not active_goals:
return None
# Sort by urgency score
active_goals.sort(key=lambda g: g.get_urgency_score(), reverse=True)
return active_goals[0]
async def _plan_actions(self, goal: AgentGoal, situation: Dict[str, Any]) -> List[AgentAction]:
"""Plan actions to achieve the specified goal."""
planned_actions = []
# Use AI processor for intelligent action planning if available
if hasattr(self, 'ai_processor') and self.ai_processor:
ai_prompt = f"""
Plan actions to achieve this goal: {goal.description}
Target metrics: {goal.target_metrics}
Current situation: {situation}
Agent type: {self.agent_type.value}
Generate 1-3 specific, actionable steps that can help achieve this goal.
Consider safety constraints and resource limits.
"""
try:
ai_response = await self.ai_processor.generate_text(
ai_prompt,
style="technical",
max_length=300
)
if ai_response.is_right():
# Parse AI response into actions
action_plan = ai_response.get_right()
actions = self._parse_action_plan(action_plan, goal)
planned_actions.extend(actions)
except Exception:
# Fall back to rule-based planning
pass
# Rule-based planning as fallback or primary method
if not planned_actions:
planned_actions = await self._rule_based_planning(goal, situation)
return planned_actions[:3] # Limit to 3 actions per cycle
async def _rule_based_planning(self, goal: AgentGoal, situation: Dict[str, Any]) -> List[AgentAction]:
"""Rule-based action planning."""
actions = []
# Basic goal-based action planning
if self.agent_type == AgentType.OPTIMIZER:
# Focus on optimization actions
if "performance" in goal.description.lower():
action = AgentAction(
action_id=str(uuid.uuid4()),
agent_id=self.agent_id,
action_type="analyze_performance",
parameters={"metrics": goal.target_metrics},
goal_id=goal.goal_id,
confidence=0.8,
estimated_impact=0.6
)
actions.append(action)
elif self.agent_type == AgentType.MONITOR:
# Focus on monitoring actions
action = AgentAction(
action_id=str(uuid.uuid4()),
agent_id=self.agent_id,
action_type="check_system_health",
parameters={"components": ["all"]},
goal_id=goal.goal_id,
confidence=0.9,
estimated_impact=0.4
)
actions.append(action)
return actions
def _parse_action_plan(self, action_plan: str, goal: AgentGoal) -> List[AgentAction]:
"""Parse AI-generated action plan into structured actions."""
actions = []
# Simple parsing - in production this would be more sophisticated
lines = action_plan.split('\n')
for i, line in enumerate(lines[:3]): # Limit to 3 actions
if line.strip() and not line.startswith('#'):
action = AgentAction(
action_id=str(uuid.uuid4()),
agent_id=self.agent_id,
action_type="ai_planned_action",
parameters={"description": line.strip()},
goal_id=goal.goal_id,
confidence=0.7,
estimated_impact=0.5,
rationale=f"AI-planned action {i+1}"
)
actions.append(action)
return actions
async def _validate_action_safety(self, action: AgentAction) -> bool:
"""Validate action against safety constraints."""
# Check safety constraints
for constraint_type, constraint_value in self.safety_constraints.items():
if constraint_type == "max_risk_score":
if action.get_risk_score() > constraint_value:
return False
elif constraint_type == "forbidden_actions":
if action.action_type in constraint_value:
return False
elif constraint_type == "resource_limits":
for resource, limit in constraint_value.items():
if action.resource_cost.get(resource, 0) > limit:
return False
# Mark as safety validated
object.__setattr__(action, 'safety_validated', True)
return True
async def _execute_action(self, action: AgentAction) -> Either[AgentError, Dict[str, Any]]:
"""Execute the specified action."""
try:
# Record execution time
object.__setattr__(action, 'executed_at', datetime.utcnow())
# Simulate action execution - in production this would call actual tools
execution_result = {
"action_id": action.action_id,
"success": True,
"output": f"Executed {action.action_type} successfully",
"duration": 1.5,
"resources_used": action.resource_cost
}
return Either.right(execution_result)
except Exception as e:
return Either.left(AgentError.action_execution_failed(str(e)))
async def _learn_from_action(self, action: AgentAction, result: Either[AgentError, Dict[str, Any]],
context: Dict[str, Any]) -> None:
"""Learn from action execution for future improvement."""
try:
success = result.is_right()
outcome = result.get_right() if success else {"error": str(result.get_left())}
# Calculate learning value based on outcome
learning_value = 0.8 if success else 0.6 # Both success and failure provide learning
experience = LearningExperience(
experience_id=str(uuid.uuid4()),
agent_id=self.agent_id,
context=context,
action_taken=action,
outcome=outcome,
success=success,
learning_value=learning_value
)
self.experiences.append(experience)
# Extract and store patterns
patterns = experience.extract_patterns()
await self._update_learned_patterns(patterns)
# Limit experience history to prevent memory bloat
if len(self.experiences) > 1000:
self.experiences = self.experiences[-500:] # Keep most recent 500
except Exception as e:
# Learning failure shouldn't break the agent
pass
async def _self_optimize(self) -> None:
"""Perform self-optimization based on learned patterns."""
try:
self.status = AgentStatus.OPTIMIZING
# Analyze recent experiences for optimization opportunities
recent_experiences = self.experiences[-50:] # Last 50 experiences
success_rate = sum(1 for exp in recent_experiences if exp.success) / len(recent_experiences)
# Adjust parameters based on performance
if success_rate < 0.7:
# Low success rate - be more conservative
self.config['confidence_threshold'] = min(0.9, self.config.get('confidence_threshold', 0.5) + 0.1)
elif success_rate > 0.9:
# High success rate - can be more aggressive
self.config['confidence_threshold'] = max(0.3, self.config.get('confidence_threshold', 0.5) - 0.1)
# Update performance metrics
self.performance_metrics['success_rate'] = success_rate
self.performance_metrics['learning_rate'] = len(recent_experiences) / 50.0
self.status = AgentStatus.ACTIVE
except Exception as e:
self.status = AgentStatus.ACTIVE # Continue operation even if optimization fails
class AgentManager:
"""Management system for autonomous agents."""
def __init__(self):
self.agents: Dict[str, AutonomousAgent] = {}
self.agent_coordinator = AgentCoordinator()
self.resource_manager = ResourceManager()
async def create_agent(self, agent_type: AgentType, config: Dict[str, Any]) -> Either[AgentError, str]:
"""Create new autonomous agent."""
try:
agent_id = f"{agent_type.value}_{datetime.utcnow().timestamp()}"
# Create agent instance
agent = AutonomousAgent(agent_id, agent_type, config)
# Initialize agent
init_result = await agent.initialize()
if init_result.is_left():
return init_result
# Register with coordinator
await self.agent_coordinator.register_agent(agent)
# Store agent
self.agents[agent_id] = agent
return Either.right(agent_id)
except Exception as e:
return Either.left(AgentError.agent_creation_failed(str(e)))
async def start_agent_execution(self, agent_id: str) -> Either[AgentError, None]:
"""Start autonomous execution for agent."""
try:
if agent_id not in self.agents:
return Either.left(AgentError.agent_not_found(agent_id))
agent = self.agents[agent_id]
# Start autonomous execution loop
asyncio.create_task(self._agent_execution_loop(agent))
return Either.right(None)
except Exception as e:
return Either.left(AgentError.execution_start_failed(str(e)))
async def _agent_execution_loop(self, agent: AutonomousAgent) -> None:
"""Continuous execution loop for autonomous agent."""
while agent.status == AgentStatus.ACTIVE:
try:
# Execute one autonomous cycle
cycle_result = await agent.execute_autonomous_cycle()
if cycle_result.is_left():
# Handle execution errors
await self._handle_agent_error(agent, cycle_result.get_left())
# Wait before next cycle
await asyncio.sleep(60) # 1 minute between cycles
except Exception as e:
# Handle unexpected errors
await self._handle_agent_error(agent, AgentError.unexpected_error(str(e)))
await asyncio.sleep(300) # Wait 5 minutes before retry
# Placeholder classes for supporting systems
class AgentCoordinator:
"""Inter-agent coordination and communication."""
async def register_agent(self, agent: AutonomousAgent) -> None:
"""Register agent for coordination."""
pass
async def coordinate_agents(self, agents: List[AutonomousAgent]) -> None:
"""Coordinate actions between multiple agents."""
pass
class ResourceManager:
"""Resource allocation and optimization for agents."""
async def allocate_resources(self, agent_id: str, resource_request: Dict[str, float]) -> Either[AgentError, Dict[str, float]]:
"""Allocate resources to agent."""
pass
async def optimize_resource_usage(self) -> None:
"""Optimize resource usage across all agents."""
pass
```
## 🔒 Security Implementation
```python
class AutonomousSafetyValidator:
"""Safety validation for autonomous agent operations."""
def validate_agent_action(self, agent: AutonomousAgent, action: AgentAction) -> Either[AgentError, None]:
"""Validate agent action for safety compliance."""
# Check autonomy level restrictions
if agent.autonomy_level == AutonomyLevel.MANUAL:
return Either.left(AgentError.manual_mode_action_blocked())
# Validate action against safety constraints
risk_score = action.get_risk_score()
max_risk = agent.safety_constraints.get('max_risk_score', 0.8)
if risk_score > max_risk:
return Either.left(AgentError.action_too_risky(risk_score, max_risk))
# Check resource limits
for resource, usage in action.resource_cost.items():
limit = agent.resource_limits.get(resource, float('inf'))
if usage > limit:
return Either.left(AgentError.resource_limit_exceeded(resource, usage, limit))
return Either.right(None)
def validate_goal_safety(self, goal: AgentGoal) -> Either[AgentError, None]:
"""Validate goal for safety compliance."""
# Check for potentially dangerous goals
dangerous_keywords = ['delete', 'destroy', 'hack', 'exploit', 'bypass']
if any(keyword in goal.description.lower() for keyword in dangerous_keywords):
return Either.left(AgentError.dangerous_goal_detected())
return Either.right(None)
```
## 🧪 Property-Based Testing Strategy
```python
from hypothesis import given, strategies as st
@given(st.sampled_from([AgentType.GENERAL, AgentType.OPTIMIZER, AgentType.MONITOR]))
def test_agent_type_properties(agent_type):
"""Property: All agent types should have valid configurations."""
config = {
"autonomy_level": "supervised",
"safety_constraints": {"max_risk_score": 0.8},
"resource_limits": {"cpu": 50.0, "memory": 1024}
}
agent = AutonomousAgent(f"test_{agent_type.value}", agent_type, config)
assert agent.agent_type == agent_type
assert agent.autonomy_level == AutonomyLevel.SUPERVISED
assert agent.status == AgentStatus.CREATED
@given(st.floats(min_value=0.0, max_value=1.0), st.floats(min_value=0.0, max_value=1.0))
def test_agent_action_properties(confidence, estimated_impact):
"""Property: Agent actions should handle valid confidence and impact ranges."""
action = AgentAction(
action_id="test_action",
agent_id="test_agent",
action_type="test_operation",
parameters={"test": "value"},
confidence=confidence,
estimated_impact=estimated_impact
)
assert action.confidence == confidence
assert action.estimated_impact == estimated_impact
assert 0.0 <= action.get_risk_score() <= 1.0
assert isinstance(action.is_high_confidence(), bool)
@given(st.sampled_from([GoalPriority.LOW, GoalPriority.MEDIUM, GoalPriority.HIGH]))
def test_goal_priority_properties(priority):
"""Property: Goals should handle all priority levels correctly."""
goal = AgentGoal(
goal_id="test_goal",
description="Test goal description",
priority=priority,
target_metrics={"success_rate": 0.9}
)
assert goal.priority == priority
urgency = goal.get_urgency_score()
assert 0.0 <= urgency <= 1.0
# Higher priority should generally mean higher urgency
if priority == GoalPriority.HIGH:
assert urgency >= 0.5
elif priority == GoalPriority.LOW:
assert urgency <= 0.5
```
## 🏗️ Modularity Strategy
- **autonomous_agent_tools.py**: Main MCP tool interface (<250 lines)
- **autonomous_systems.py**: Core autonomous agent type definitions (<400 lines)
- **agent_manager.py**: Agent lifecycle and management (<300 lines)
- **decision_engine.py**: Autonomous decision-making system (<250 lines)
- **learning_system.py**: Agent learning and adaptation (<250 lines)
- **goal_manager.py**: Goal setting and achievement tracking (<200 lines)
- **resource_optimizer.py**: Resource management and optimization (<200 lines)
- **communication_hub.py**: Inter-agent communication (<150 lines)
## ✅ Success Criteria
- Complete autonomous agent system with learning, adaptation, and self-optimization
- Intelligent goal management with dynamic prioritization and achievement tracking
- Autonomous decision-making with safety constraints and human oversight options
- Self-healing capabilities with automatic error detection and recovery
- Resource optimization and intelligent load balancing across agents
- Machine learning integration for continuous improvement and pattern recognition
- Property-based tests validate autonomous behavior and safety mechanisms
- Performance: <5s agent initialization, <10s decision-making, <60s autonomous cycles
- Integration with AI processing (TASK_40) for intelligent planning and reasoning
- Documentation: Complete autonomous agent guide with safety and ethical guidelines
- TESTING.md shows 95%+ test coverage with all safety and autonomy tests passing
- Tool enables truly autonomous automation that learns, adapts, and operates independently
## 🔄 Integration Points
- **TASK_40 (km_ai_processing)**: AI-powered decision-making and intelligent planning
- **TASK_41 (km_smart_suggestions)**: Learning algorithms and behavior analysis
- **TASK_43 (km_audit_system)**: Autonomous operation auditing and compliance tracking
- **TASK_39 (km_plugin_ecosystem)**: Extensible agent architecture and custom capabilities
- **ALL EXISTING TOOLS**: Autonomous execution and optimization of all automation capabilities
## 📋 Notes
- This creates truly autonomous automation that can operate independently
- Safety is paramount - robust constraints and validation mechanisms are essential
- Learning capabilities enable continuous improvement and adaptation
- Human oversight options provide control and approval for critical decisions
- Resource management ensures efficient operation without system overload
- Success here transforms static automation into intelligent, self-managing systems
