Add AICL example: 55_autonomous_agent.aicl

data/aicl/examples/55_autonomous_agent.aicl

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+# AICL Example: Autonomous AI Agent System
+# Production autonomous AI agent system implementing planning, tool use, memory management,
+# reasoning chains, self-reflection, and multi-agent coordination for complex task execution.
+
+Goal Build an autonomous AI agent system that can plan and execute complex multi-step tasks, use external tools, maintain persistent memory, perform chain-of-thought reasoning, self-reflect on its actions, and coordinate with other agents to accomplish goals that exceed any single agent's capability
+
+Constraint Agent must never execute destructive actions without explicit human confirmation
+Constraint Reasoning chains must be fully auditable and explainable at every step
+Constraint Agent memory must respect data retention policies and support selective forgetting
+Constraint Multi-agent coordination must prevent deadlock and ensure progress toward shared goals
+Constraint Agent must detect and report its own uncertainty and capability limits
+
+Risk Agent hallucination leading to incorrect tool calls with real-world consequences
+Recovery Implement output validation before tool execution; require confidence thresholds for action approval; maintain rollback capability for reversible actions; log full reasoning chain for audit
+
+Risk Infinite reasoning loops consuming excessive compute resources
+Recovery Enforce maximum reasoning depth and iteration count; implement progress detection with forced termination on stagnation; require step yield for long-running chains; monitor resource consumption
+
+Risk Tool execution failure cascading through multi-step plan
+Recovery Implement graceful degradation with alternative tool selection; maintain plan checkpointing for resumption; use transactional execution for reversible step groups; alert human supervisor on repeated failures
+
+Risk Memory pollution from incorrect or adversarial information
+Recovery Validate all memory entries before persistence; implement memory confidence scoring; enable selective memory quarantine; deploy periodic memory consistency auditing
+
+Risk Multi-agent coordination deadlock or conflicting actions
+Recovery Implement deadlock detection with timeout-based resolution; deploy centralized coordination for shared resources; use priority-based action arbitration; enforce communication heartbeat protocol
+
+Risk Agent exceeding authority boundaries during autonomous operation
+Recovery Define explicit capability boundaries with enforcement; implement permission escalation flow; audit all actions against authorization policy; require confirmation for high-impact operations
+
+Layer AgentCore
+    SubLayer: Planner
+    SubLayer: Reasoner
+    SubLayer: Executor
+Layer ToolUse
+    SubLayer: ToolRegistry
+    SubLayer: ToolExecutor
+    SubLayer: OutputValidator
+Layer Memory
+    SubLayer: WorkingMemory
+    SubLayer: LongTermMemory
+    SubLayer: EpisodicMemory
+Layer SelfReflection
+    SubLayer: PerformanceAssessor
+    SubLayer: StrategyAdaptor
+    SubLayer: UncertaintyEstimator
+Layer MultiAgent
+    SubLayer: AgentCoordinator
+    SubLayer: MessageRouter
+    SubLayer: ConsensusBuilder
+
+Validation Agent must produce auditable reasoning chains for every action taken
+Validation Tool execution must validate outputs against expected schemas before processing
+Validation Memory confidence scoring must be applied to all stored information
+Validation Agent must halt and report when uncertainty exceeds configured threshold
+Validation Multi-agent tasks must make measurable progress every N communication rounds
+Validation Destructive actions must require explicit human approval before execution
+Validation Agent must respect compute resource budgets and terminate on budget exhaustion
+Validation Self-reflection must identify at least 80% of agent's own errors in post-hoc analysis
+
+# Level 2 - Entities
+
+Entity AgentTask
+    taskId: string
+    taskDescription: string
+    goalSpecification: dict
+    constraints: list
+    priority: integer
+    deadline: datetime
+    parentTaskId: string
+    assignedAgentId: string
+    status: string
+    createdAt: datetime
+    completedAt: datetime
+
+Entity ReasoningStep
+    stepId: string
+    taskId: string
+    agentId: string
+    stepType: string
+    observation: dict
+    thought: string
+    action: dict
+    actionResult: dict
+    confidence: float
+    timestamp: datetime
+    depthLevel: integer
+    parentStepId: string
+
+Entity ToolDefinition
+    toolId: string
+    toolName: string
+    description: string
+    inputSchema: dict
+    outputSchema: dict
+    requiredPermissions: list
+    riskLevel: string
+    rateLimit: dict
+    version: string
+    registeredAt: datetime
+
+Entity MemoryEntry
+    entryId: string
+    agentId: string
+    entryType: string
+    content: dict
+    confidence: float
+    source: string
+    timestamp: datetime
+    accessCount: integer
+    lastAccessedAt: datetime
+    expirationAt: datetime
+    tags: list
+    validated: boolean
+
+Entity AgentProfile
+    agentId: string
+    agentName: string
+    capabilities: list
+    toolAccess: list
+    authorityLevel: string
+    specializations: list
+    performanceMetrics: dict
+    communicationEndpoints: list
+    status: string
+    registeredAt: datetime
+
+Entity CoordinationMessage
+    messageId: string
+    senderId: string
+    recipientId: string
+    messageType: string
+    payload: dict
+    priority: integer
+    requiresAck: boolean
+    timestamp: datetime
+    ttl: string
+
+# Level 3 - Behaviors
+
+Behavior PlanTask
+    Input:
+        task: AgentTask
+        agentProfile: AgentProfile
+        planningConfig: dict
+    Output:
+        executionPlan: dict
+        planConfidence: float
+        resourceEstimate: dict
+    Action:
+        Decompose task into ordered sub-goals
+        Identify required tools and capabilities for each sub-goal
+        Estimate resource requirements and execution time
+        Generate dependency graph between sub-goals
+        Identify potential failure points and contingency plans
+        Validate plan against agent capability boundaries
+        Compute overall plan confidence score
+        Return execution plan with confidence and resource estimates
+
+Behavior ExecuteReasoning
+    Input:
+        task: AgentTask
+        executionPlan: dict
+        memoryContext: dict
+    Output:
+        reasoningChain: list
+        actionDecision: dict
+        confidence: float
+    Action:
+        Initialize reasoning chain with task context
+        For each step in execution plan:
+            Retrieve relevant memories for current context
+            Generate observation about current state
+            Form reasoning about next action
+            Evaluate confidence in proposed action
+            If confidence below threshold, seek clarification or alternative
+            Record step in reasoning chain
+        Return complete chain with final action decision
+
+Behavior UseTool
+    Input:
+        toolDefinition: ToolDefinition
+        toolInput: dict
+        executionContext: dict
+    Output:
+        toolOutput: dict
+        validationResult: dict
+        executionReport: dict
+    Action:
+        Validate tool input against schema
+        Check agent permissions for tool access
+        If risk level high, require human confirmation
+        Execute tool with timeout and error handling
+        Validate tool output against expected schema
+        Record tool usage in execution trace
+        If validation fails, attempt recovery or alternative tool
+        Return output with validation result
+
+Behavior StoreMemory
+    Input:
+        memoryEntry: MemoryEntry
+        storageConfig: dict
+    Output:
+        storedEntry: MemoryEntry
+        storageConfirmation: dict
+    Action:
+        Validate memory entry content and metadata
+        Compute confidence score based on source reliability
+        Check for conflicts with existing memories
+        If conflict, apply resolution strategy or flag for review
+        Apply data retention policy and set expiration
+        Store in appropriate memory tier (working, episodic, long-term)
+        Index entry for efficient retrieval
+        Return stored entry with confirmation
+
+Behavior ReflectOnPerformance
+    Input:
+        task: AgentTask
+        reasoningChain: list
+        outcome: dict
+    Output:
+        reflectionReport: dict
+        strategyAdjustments: dict
+        learnedLessons: list
+    Action:
+        Review reasoning chain for logical errors or biases
+        Evaluate outcome against expected results
+        Identify steps where confidence was misplaced
+        Assess tool usage effectiveness and alternatives
+        Generate lessons learned for future tasks
+        Propose strategy adjustments for similar future tasks
+        Store reflection insights in long-term memory
+        Return reflection report with actionable improvements
+
+Behavior CoordinateWithAgent
+    Input:
+        coordinationMessage: CoordinationMessage
+        multiAgentState: dict
+    Output:
+        coordinationResult: dict
+        updatedState: dict
+        responseMessage: CoordinationMessage
+    Action:
+        Parse coordination message and assess relevance
+        Evaluate impact on current task execution
+        Resolve any conflicts with planned actions
+        Contribute to shared goal progress
+        Build consensus on shared decisions if required
+        Acknowledge message and provide status update
+        Return coordination result with updated state
+
+# Level 4 - Conditions
+
+Condition: ConfidenceBelowThreshold
+    When agent confidence in next action falls below 0.6 threshold
+    Then halt execution; seek human clarification; attempt alternative approach; log uncertainty event for strategy improvement
+
+Condition: ResourceBudgetExhausted
+    When cumulative compute or token usage exceeds allocated budget
+    Then checkpoint current state; summarize progress so far; request budget extension or terminate gracefully; report incomplete work
+
+Condition: DestructiveActionProposed
+    When planned action is classified as destructive or irreversible
+    Then pause execution; present action details to human supervisor; require explicit approval before proceeding; log approval with reasoning
+
+Condition: CoordinationDeadlock
+    When multi-agent coordination detects circular dependency or no progress for N rounds
+    Then escalate to coordinating agent; apply timeout-based resolution; propose simplified coordination plan; alert human supervisor if unresolved
+
+Condition: MemoryConflictDetected
+    When new memory entry contradicts existing high-confidence memory
+    Then flag conflict for resolution; compare source reliability scores; quarantine conflicting entries; request human verification if critical
+
+Condition: ToolExecutionTimeout
+    When tool execution exceeds configured timeout duration
+    Then terminate tool execution; attempt retry with backoff; select alternative tool if available; record timeout in execution trace
+
+# Level 5 - Events
+
+Event: TaskStarted
+    On agent beginning task execution
+    Action: initialize execution context; load relevant memories; emit progress heartbeat; activate monitoring
+
+Event: ReasoningStepCompleted
+    On each reasoning step finishing
+    Action: record step in audit trail; update progress tracking; check confidence thresholds; evaluate need for course correction
+
+Event: ToolExecuted
+    On tool execution completing with result
+    Action: validate output; update execution trace; store result in working memory; emit usage metrics
+
+Event: ReflectionCompleted
+    On self-reflection analysis finished
+    Action: store learned lessons; update strategy parameters; adjust confidence calibration; notify coordination layer
+
+Event: CoordinationMessageReceived
+    On message from another agent received
+    Action: parse and assess message; update shared state; respond with acknowledgment; integrate information into reasoning
+
+Event: AgentHalted
+    On agent voluntarily halting due to uncertainty or safety concern
+    Action: checkpoint state; generate summary of progress; alert human supervisor; log halt reason for analysis
+
+Event: TaskCompleted
+    On task execution finished with final result
+    Action: trigger self-reflection; store episodic memory; report results; release resources; update performance metrics
+
+# Level 6 - Concurrency
+
+Parallel:
+    Planning and memory retrieval simultaneously
+    Multiple tool executions with independent resources
+    Reasoning chain generation alongside memory updates
+    Multi-agent message processing concurrent with task execution
+    Self-reflection analysis in background during task execution
+    Memory indexing parallel with storage operations
+
+# Level 7 - Optimization
+
+Optimize: Reasoning efficiency and accuracy
+    Priority: Minimize reasoning steps while maintaining accuracy; cache similar reasoning patterns; use hierarchical planning for complex tasks
+
+Optimize: Tool selection and execution
+    Priority: Select most reliable tool for each action; parallelize independent tool calls; cache tool results for repeated queries
+
+Optimize: Memory retrieval relevance
+    Priority: Prioritize high-confidence recent memories; use semantic similarity for retrieval; compress older memories with summarization
+
+# Level 8 - Learning
+
+Learn: Planning strategy from task outcomes
+    Goal: Generate more effective execution plans with fewer failures
+    Adapt: Planning heuristics, decomposition strategy, and contingency selection
+    Based: Historical task outcomes, plan efficiency metrics, and failure pattern analysis
+
+Learn: Tool selection optimization
+    Goal: Select optimal tools considering reliability, latency, and output quality
+    Adapt: Tool preference rankings and selection criteria
+    Based: Tool execution success rates, latency history, and output quality assessments
+
+Learn: Confidence calibration
+    Goal: Accurately estimate confidence such that calibrated confidence matches actual success rate
+    Adapt: Confidence scoring parameters and threshold values
+    Based: Comparison of predicted confidence vs actual outcomes across historical decisions
+
+Learn: Memory relevance scoring
+    Goal: Retrieve most relevant memories for each reasoning context
+    Adapt: Memory retrieval weights and recency-importance trade-off parameters
+    Based: Memory access patterns, reasoning step outcomes, and retrieval effectiveness metrics
+
+Learn: Multi-agent coordination patterns
+    Goal: Develop efficient coordination strategies that minimize communication overhead
+    Adapt: Communication frequency, message content selection, and consensus strategy
+    Based: Coordination task outcomes, communication costs, and team performance metrics
+
+# Level 9 - Security
+
+Security:
+    Encrypt: All agent memory entries at rest using AES-256 with per-agent encryption keys
+    Encrypt: Inter-agent communication with TLS 1.3 and message signing for authenticity
+    Protect: Tool execution with permission enforcement and destructive action confirmation gates
+    Protect: Reasoning chain integrity with append-only audit logging and tamper detection
+    Protect: Agent authority boundaries with signed capability certificates and runtime enforcement
+    Encrypt: Task specifications containing sensitive information with field-level encryption
+    Protect: Coordination messages from eavesdropping and injection with authenticated encryption
+
+# Level 10 - Native
+
+Native: python
+{
+import hashlib
+import json
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Any, Tuple
+from dataclasses import dataclass, field
+
+@dataclass
+class ReasoningChain:
+    steps: List[Dict] = field(default_factory=list)
+    max_depth: int = 50
+    max_iterations: int = 100
+
+    def add_step(self, step_type: str, observation: Dict, thought: str,
+                 action: Dict, confidence: float, parent_id: Optional[str] = None) -> Dict:
+        step = {
+            "step_id": hashlib.sha256(
+                f"{step_type}:{len(self.steps)}:{datetime.utcnow().isoformat()}".encode()
+            ).hexdigest()[:16],
+            "step_type": step_type,
+            "observation": observation,
+            "thought": thought,
+            "action": action,
+            "confidence": confidence,
+            "timestamp": datetime.utcnow().isoformat(),
+            "depth_level": self._compute_depth(parent_id),
+            "parent_step_id": parent_id
+        }
+        self.steps.append(step)
+        return step
+
+    def _compute_depth(self, parent_id: Optional[str]) -> int:
+        if not parent_id:
+            return 0
+        for step in self.steps:
+            if step["step_id"] == parent_id:
+                return step["depth_level"] + 1
+        return 0
+
+    def is_within_limits(self) -> bool:
+        return len(self.steps) < self.max_iterations
+
+    def get_summary(self) -> Dict:
+        return {
+            "total_steps": len(self.steps),
+            "avg_confidence": sum(s["confidence"] for s in self.steps) / max(len(self.steps), 1),
+            "step_types": list(set(s["step_type"] for s in self.steps)),
+            "max_depth": max((s["depth_level"] for s in self.steps), default=0)
+        }
+
+@dataclass
+class ToolRegistry:
+    tools: Dict[str, Dict] = field(default_factory=dict)
+
+    def register(self, tool_id: str, name: str, description: str,
+                 input_schema: Dict, output_schema: Dict,
+                 permissions: List[str], risk_level: str = "low") -> Dict:
+        tool = {
+            "tool_id": tool_id,
+            "tool_name": name,
+            "description": description,
+            "input_schema": input_schema,
+            "output_schema": output_schema,
+            "required_permissions": permissions,
+            "risk_level": risk_level,
+            "version": "1.0",
+            "registered_at": datetime.utcnow().isoformat()
+        }
+        self.tools[tool_id] = tool
+        return tool
+
+    def validate_input(self, tool_id: str, inputs: Dict) -> Tuple[bool, List[str]]:
+        tool = self.tools.get(tool_id)
+        if not tool:
+            return False, [f"Tool {tool_id} not found"]
+        schema = tool["input_schema"]
+        errors = []
+        for required_field, field_type in schema.get("required", {}).items():
+            if required_field not in inputs:
+                errors.append(f"Missing required field: {required_field}")
+            elif field_type == "string" and not isinstance(inputs[required_field], str):
+                errors.append(f"Field {required_field} must be string")
+            elif field_type == "integer" and not isinstance(inputs[required_field], int):
+                errors.append(f"Field {required_field} must be integer")
+        return len(errors) == 0, errors
+
+    def check_permission(self, tool_id: str, agent_permissions: List[str]) -> bool:
+        tool = self.tools.get(tool_id)
+        if not tool:
+            return False
+        required = set(tool["required_permissions"])
+        available = set(agent_permissions)
+        return required.issubset(available)
+
+@dataclass
+class AgentMemory:
+    working_memory: Dict[str, Dict] = field(default_factory=dict)
+    episodic_memory: List[Dict] = field(default_factory=list)
+    long_term_memory: Dict[str, List[Dict]] = field(default_factory=dict)
+    max_working_size: int = 100
+
+    def store(self, entry_type: str, content: Dict, confidence: float,
+              source: str, tags: List[str] = None) -> Dict:
+        entry = {
+            "entry_id": hashlib.sha256(
+                f"{entry_type}:{datetime.utcnow().isoformat()}".encode()
+            ).hexdigest()[:16],
+            "entry_type": entry_type,
+            "content": content,
+            "confidence": confidence,
+            "source": source,
+            "timestamp": datetime.utcnow().isoformat(),
+            "access_count": 0,
+            "tags": tags or [],
+            "validated": confidence >= 0.8
+        }
+
+        if entry_type == "working":
+            if len(self.working_memory) >= self.max_working_size:
+                self._evict_working_memory()
+            self.working_memory[entry["entry_id"]] = entry
+        elif entry_type == "episodic":
+            self.episodic_memory.append(entry)
+        elif entry_type == "long_term":
+            for tag in (tags or ["default"]):
+                self.long_term_memory.setdefault(tag, []).append(entry)
+
+        return entry
+
+    def retrieve(self, query: str, limit: int = 5, min_confidence: float = 0.5) -> List[Dict]:
+        results = []
+        for entry in self.working_memory.values():
+            if entry["confidence"] >= min_confidence:
+                results.append(entry)
+        results.sort(key=lambda x: x["confidence"], reverse=True)
+        return results[:limit]
+
+    def _evict_working_memory(self) -> None:
+        if not self.working_memory:
+            return
+        oldest_key = min(self.working_memory, key=lambda k: self.working_memory[k]["access_count"])
+        evicted = self.working_memory.pop(oldest_key)
+        self.episodic_memory.append(evicted)
+
+@dataclass
+class SelfReflector:
+    error_detection_rate_target: float = 0.8
+
+    def reflect(self, reasoning_chain: ReasoningChain, outcome: Dict) -> Dict:
+        steps = reasoning_chain.steps
+        low_confidence_steps = [s for s in steps if s["confidence"] < 0.6]
+        errors_identified = []
+
+        for step in steps:
+            if step["confidence"] < 0.5:
+                errors_identified.append({
+                    "step_id": step["step_id"],
+                    "issue": "low_confidence",
+                    "confidence": step["confidence"],
+                    "thought": step["thought"]
+                })
+
+        success = outcome.get("success", False)
+        lessons = []
+        if not success and low_confidence_steps:
+            lessons.append("Low confidence steps correlated with task failure")
+        if not success:
+            lessons.append("Review planning phase for missed dependencies")
+
+        return {
+            "total_steps": len(steps),
+            "low_confidence_steps": len(low_confidence_steps),
+            "errors_identified": errors_identified,
+            "lessons_learned": lessons,
+            "outcome_success": success,
+            "recommended_adjustments": self._generate_adjustments(steps, outcome)
+        }
+
+    def _generate_adjustments(self, steps: List[Dict], outcome: Dict) -> List[str]:
+        adjustments = []
+        if not outcome.get("success", False):
+            adjustments.append("Increase confidence threshold before action execution")
+            adjustments.append("Add more contingency plans in execution strategy")
+        avg_confidence = sum(s["confidence"] for s in steps) / max(len(steps), 1)
+        if avg_confidence < 0.7:
+            adjustments.append("Improve observation quality before reasoning steps")
+        return adjustments
+}