1import json
2from typing import Dict, List
3
4class RunLoopController:
5    """Run-loop prompting with safe exit criteria"""
6
7    def __init__(self, max_iterations: int = 10):
8        self.max_iterations = max_iterations
9        self.loop_template = """
10You are in an execution loop. At each step, you must decide to either:
111. CONTINUE - Take another action toward the goal
122. FINISH - You have completed the task successfully
13
14Current goal: {goal}
15Step: {current_step}/{max_steps}
16Previous actions: {action_history}
17Current observations: {observations}
18
19Think step by step:
201. Have I achieved the goal? If yes, respond with FINISH
212. What is the next logical action? If unclear, respond with FINISH
223. If continuing, specify exactly one action
23
24Response format:
25{{"decision": "CONTINUE|FINISH", "reasoning": "...", "action": "..."}}
26"""
27
28    async def execute_loop(self, goal: str, agent_core) -> Dict:
29        """Execute run-loop with safe termination"""
30
31        action_history = []
32        observations = []
33
34        for step in range(self.max_iterations):
35            prompt = self.loop_template.format(
36                goal=goal,
37                current_step=step + 1,
38                max_steps=self.max_iterations,
39                action_history=action_history[-3:],  # Last 3 actions for context
40                observations=observations[-3:]
41            )
42
43            response = await agent_core.llm.generate(prompt)
44
45            try:
46                decision_data = json.loads(response)
47
48                if decision_data.get("decision") == "FINISH":
49                    return {
50                        "status": "completed",
51                        "reason": decision_data.get("reasoning", ""),
52                        "total_steps": step + 1
53                    }
54                elif decision_data.get("decision") == "CONTINUE":
55                    # Execute the specified action
56                    action_result = await self._execute_action(
57                        decision_data.get("action", ""), agent_core
58                    )
59                    action_history.append(decision_data.get("action", ""))
60                    observations.append(action_result)
61                else:
62                    # Invalid decision format - force finish
63                    return {"status": "error", "reason": "Invalid decision format"}
64
65            except json.JSONDecodeError:
66                # Parsing failed - force finish for safety
67                return {"status": "error", "reason": "Response parsing failed"}
68
69        # Max iterations reached
70        return {"status": "timeout", "reason": f"Exceeded {self.max_iterations} steps"}
71
72    async def _execute_action(self, action: str, agent_core) -> str:
73        """Execute action via tool registry"""
74        raise NotImplementedError("Connect to ToolRegistry.execute() in production")
75

1import json
2from typing import Dict
3
4class SemanticRouter:
5    """Input classification and dispatch to specialized handlers"""
6
7    def __init__(self):
8        self.routing_template = """
9Classify the user input into exactly one category:
10
11Categories:
12- SUPPORT: Customer service, account issues, troubleshooting
13- CODING: Software development, debugging, technical questions
14- DATA_REQUEST: Information lookup, research, analysis
15- DESTRUCTIVE: Account deletion, data removal, security actions
16- GENERAL: Casual conversation, greetings, unclear requests
17
18User input: "{user_input}"
19
20Response format (JSON only):
21{{`category`: `CATEGORY_NAME`, `confidence`: 0.95, `reasoning`: `brief explanation`}}
22"""
23
24    async def route_request(self, user_input: str, llm_client) -> Dict:
25        """Route user input to appropriate handler"""
26
27        prompt = self.routing_template.format(user_input=user_input)
28        response = await llm_client.generate(prompt)
29
30        try:
31            routing_data = json.loads(response)
32            category = routing_data.get("category", "GENERAL")
33
34            # Route to specialized sub-prompts based on category
35            if category == "SUPPORT":
36                return await self._handle_support_request(user_input, llm_client)
37            elif category == "CODING":
38                return await self._handle_coding_request(user_input, llm_client)
39            elif category == "DESTRUCTIVE":
40                return await self._handle_destructive_request(user_input, llm_client)
41            else:
42                return await self._handle_general_request(user_input, llm_client)
43
44        except json.JSONDecodeError:
45            # Default to general handler if parsing fails
46            return await self._handle_general_request(user_input, llm_client)
47
48    async def _handle_support_request(self, input_text: str, llm_client) -> Dict:
49        """Specialized prompt for customer support scenarios"""
50        support_prompt = f"""
51You are a customer support agent. For this request: "{input_text}"
52
531. Identify the customer's primary issue
542. Suggest up to 3 specific troubleshooting steps
553. Escalate to human if the issue involves: billing, legal, or complex technical problems
56
57Response as JSON: {{"issue_type": "...", "steps": [...], "escalate": boolean}}
58"""
59        response = await llm_client.generate(support_prompt)
60        return {"category": "support", "response": response}
61
62    async def _handle_coding_request(self, input_text: str, llm_client) -> Dict:
63        """Specialized prompt for coding assistance"""
64        # Implementation for coding requests
65        return {"category": "coding", "response": "Coding assistance provided"}
66
67    async def _handle_destructive_request(self, input_text: str, llm_client) -> Dict:
68        """Handle destructive actions with extra validation"""
69        # Implementation with additional safety checks
70        return {"category": "destructive", "response": "Destructive action requires confirmation"}
71
72    async def _handle_general_request(self, input_text: str, llm_client) -> Dict:
73        """Handle general conversations"""
74        # Implementation for general chat
75        return {"category": "general", "response": "General response provided"}
76

1import json
2from typing import Dict, List, Optional, Any
3from pydantic import BaseModel, ValidationError
4
5class AgentResponse(BaseModel):
6    """Structured response schema for agent outputs"""
7    action_type: str
8    parameters: Dict[str, Any]
9    confidence: float
10    reasoning: str
11    next_steps: Optional[List[str]] = None
12
13class StructuredResponseHandler:
14    """Enforce JSON schema validation for all agent responses"""
15
16    def __init__(self):
17        self.schema_template = """
18You must respond with valid JSON matching this exact schema:
19{{
20  `action_type`: `string (required)`,
21  `parameters`: {{`key`: `value`}},
22  `confidence`: 0.95,
23  `reasoning`: `string explaining your decision`,
24  `next_steps`: [`optional`, `array`, `of`, `strings`]
25}}
26
27User request: {user_input}
28Available actions: {available_actions}
29
30JSON response:
31"""
32
33    async def get_structured_response(self, user_input: str,
34                                     available_actions: List[str],
35                                     llm_client) -> AgentResponse:
36        """Get validated structured response from LLM"""
37
38        prompt = self.schema_template.format(
39            user_input=user_input,
40            available_actions=available_actions
41        )
42
43        max_retries = 3
44        for attempt in range(max_retries):
45            try:
46                response = await llm_client.generate(prompt)
47
48                # Parse and validate against Pydantic schema
49                response_data = json.loads(response)
50                validated_response = AgentResponse(**response_data)
51
52                return validated_response
53
54            except (json.JSONDecodeError, ValidationError) as e:
55                if attempt == max_retries - 1:
56                    # Final attempt failed - return safe default
57                    return AgentResponse(
58                        action_type="error",
59                        parameters={"error": str(e)},
60                        confidence=0.0,
61                        reasoning=f"Failed to parse response after {max_retries} attempts"
62                    )
63
64                # Retry with error feedback
65                prompt += f"\n\nPrevious attempt failed with error: {str(e)}. Please provide valid JSON."
66

1import json
2from typing import Dict, List, Optional, Any
3
4class PromptHygienizer:
5    """Standardized prompt formatting with clear boundaries"""
6
7    @staticmethod
8    def format_system_prompt(capabilities: List[str], limitations: List[str]) -> str:
9        """Declarative intent with clear capability boundaries"""
10        return f"""
11--- SYSTEM CONTEXT ---
12You are an AI agent with these specific capabilities:
13{chr(10).join('• ' + cap for cap in capabilities)}
14
15Important limitations:
16{chr(10).join('• ' + lim for lim in limitations)}
17
18--- INSTRUCTIONS ---
191. Always operate within your defined capabilities
202. Use structured responses with JSON formatting
213. Explain your reasoning for each decision
224. Request clarification when inputs are ambiguous
23
24--- TOOLS AVAILABLE ---
25Use this exact format for tool calls:
26<tool_name>
27<parameter_name>value</parameter_name>
28</tool_name>
29
30--- RESPONSE FORMAT ---
31Always structure responses as JSON with required fields
32"""
33
34    @staticmethod
35    def format_user_message(user_input: str, context: Dict = None) -> str:
36        """Structured user message with clear boundaries"""
37        formatted_message = f"""
38--- USER REQUEST ---
39{user_input}
40"""
41
42        if context:
43            formatted_message += f"""
44--- CONTEXT ---
45{json.dumps(context, indent=2)}
46"""
47
48        formatted_message += "\n--- AGENT RESPONSE ---\n"
49        return formatted_message
50

1import json
2from typing import Dict, List, Optional, Any
3
4class HallucinationMitigation:
5    """Multiple validation layers to catch and correct hallucinations"""
6
7    def __init__(self, knowledge_base, fact_checker):
8        self.knowledge_base = knowledge_base
9        self.fact_checker = fact_checker
10
11    async def validate_response(self, agent_response: str,
12                               original_query: str) -> Dict:
13        """Multi-layer validation pipeline"""
14
15        validation_results = {
16            "schema_valid": False,
17            "factually_accurate": False,
18            "contextually_relevant": False,
19            "final_score": 0.0,
20            "corrections": []
21        }
22
23        # Layer 1: Schema validation
24        try:
25            parsed_response = json.loads(agent_response)
26            validation_results["schema_valid"] = True
27        except json.JSONDecodeError:
28            validation_results["corrections"].append("Invalid JSON format")
29            return validation_results
30
31        # Layer 2: Fact checking against knowledge base
32        facts_mentioned = await self._extract_factual_claims(agent_response)
33        fact_accuracy = await self._verify_facts(facts_mentioned)
34        validation_results["factually_accurate"] = fact_accuracy > 0.8
35
36        if not validation_results["factually_accurate"]:
37            validation_results["corrections"].append("Potential factual inaccuracies detected")
38
39        # Layer 3: Relevance check
40        relevance_score = await self._check_relevance(agent_response, original_query)
41        validation_results["contextually_relevant"] = relevance_score > 0.7
42
43        if not validation_results["contextually_relevant"]:
44            validation_results["corrections"].append("Response may be off-topic")
45
46        # Calculate final validation score
47        validation_results["final_score"] = (
48            0.3 * validation_results["schema_valid"] +
49            0.4 * validation_results["factually_accurate"] +
50            0.3 * validation_results["contextually_relevant"]
51        )
52
53        return validation_results
54
55    async def _extract_factual_claims(self, text: str) -> List[str]:
56        """Extract factual claims from text for verification"""
57        # Simplified implementation - use NLP entity extraction in production
58        # Split text into sentences and filter for factual statements
59        sentences = text.split('.')
60        factual_claims = [s.strip() for s in sentences if len(s.strip()) > 10 and any(
61            keyword in s.lower() for keyword in ['is', 'was', 'has', 'can', 'will', 'according to']
62        )]
63        return factual_claims[:5]  # Limit to first 5 claims
64
65    async def _check_relevance(self, response: str, query: str) -> float:
66        """Check response relevance to original query"""
67        # Simplified implementation - use semantic similarity in production
68        response_words = set(response.lower().split())
69        query_words = set(query.lower().split())
70
71        if not response_words or not query_words:
72            return 0.0
73
74        overlap = response_words.intersection(query_words)
75        return len(overlap) / len(query_words)
76
77    async def _verify_facts(self, facts: List[str]) -> float:
78        """Verify factual claims against reliable sources"""
79        if not facts:
80            return 1.0
81
82        verified_count = 0
83        for fact in facts:
84            # Check against knowledge base
85            kb_result = await self.knowledge_base.verify_fact(fact)
86            if kb_result.confidence > 0.8:
87                verified_count += 1
88            else:
89                # Secondary check with external fact checker
90                ext_result = await self.fact_checker.verify(fact)
91                if ext_result.verified:
92                    verified_count += 1
93
94        return verified_count / len(facts)
95

1import json
2from typing import Dict, List, Optional, Any
3from datetime import datetime
4from dataclasses import dataclass
5from abc import ABC, abstractmethod
6
7@dataclass
8class MemoryEntry:
9    """Base memory entry with temporal metadata"""
10    id: str
11    content: Dict
12    timestamp: datetime
13    memory_type: str
14    validity_start: Optional[datetime] = None
15    validity_end: Optional[datetime] = None
16
17class MemoryStore(ABC):
18    """Abstract base class for memory implementations"""
19
20    @abstractmethod
21    async def store(self, entry: MemoryEntry) -> str:
22        pass
23
24    @abstractmethod
25    async def retrieve(self, query: str, limit: int = 10) -> List[MemoryEntry]:
26        pass
27
28    @abstractmethod
29    async def update(self, entry_id: str, content: Dict) -> bool:
30        pass
31
32class VectorMemoryStore(MemoryStore):
33    """Vector database implementation for semantic memory"""
34
35    def __init__(self, vector_db_client, embedding_model):
36        self.client = vector_db_client
37        self.embedding_model = embedding_model
38
39    async def store(self, entry: MemoryEntry) -> str:
40        """Store memory entry with vector embeddings"""
41        embedding = await self.embedding_model.embed(str(entry.content))
42
43        doc = {
44            "id": entry.id,
45            "content": entry.content,
46            "embedding": embedding,
47            "timestamp": entry.timestamp.isoformat(),
48            "memory_type": entry.memory_type,
49            "validity_start": entry.validity_start.isoformat() if entry.validity_start else None,
50            "validity_end": entry.validity_end.isoformat() if entry.validity_end else None
51        }
52
53        await self.client.upsert([doc])
54        return entry.id
55
56    async def retrieve(self, query: str, limit: int = 10) -> List[MemoryEntry]:
57        """Retrieve semantically similar memories"""
58        query_embedding = await self.embedding_model.embed(query)
59
60        results = await self.client.query(
61            vector=query_embedding,
62            top_k=limit,
63            include_metadata=True
64        )
65
66        return [self._deserialize_entry(result) for result in results.matches]
67
68    def _deserialize_entry(self, result) -> MemoryEntry:
69        """Convert database result to MemoryEntry"""
70        metadata = result.metadata
71
72        # Validate required fields exist
73        required_fields = ["id", "content", "timestamp", "memory_type"]
74        for field in required_fields:
75            if field not in metadata:
76                raise ValueError(f"Memory corruption: missing required field '{field}'")
77
78        # Safely parse timestamps with corruption protection
79        try:
80            timestamp = datetime.fromisoformat(metadata["timestamp"])
81        except (ValueError, TypeError) as e:
82            raise ValueError(f"Memory corruption: invalid timestamp format '{metadata['timestamp']}'") from e
83
84        validity_start_str = metadata.get("validity_start")
85        validity_end_str = metadata.get("validity_end")
86
87        validity_start = None
88        validity_end = None
89
90        if validity_start_str:
91            try:
92                validity_start = datetime.fromisoformat(validity_start_str)
93            except (ValueError, TypeError):
94                pass  # Invalid format, keep as None
95
96        if validity_end_str:
97            try:
98                validity_end = datetime.fromisoformat(validity_end_str)
99            except (ValueError, TypeError):
100                pass  # Invalid format, keep as None
101
102        return MemoryEntry(
103            id=metadata["id"],
104            content=metadata["content"],
105            timestamp=timestamp,
106            memory_type=metadata["memory_type"],
107            validity_start=validity_start,
108            validity_end=validity_end
109        )
110
111
112

1import asyncio
2import json
3import logging
4import uuid
5from enum import Enum
6from typing import Any, Dict, List, Optional
7from datetime import datetime
8
9class PlanningMode(Enum):
10    REACT = "react"
11    PLAN_EXECUTE = "plan_execute"
12
13class AgentAction:
14    """Represents an action the agent can take"""
15    def __init__(self, tool_name: str, parameters: Dict[str, Any], reasoning: str = ""):
16        self.tool_name = tool_name
17        self.parameters = parameters
18        self.reasoning = reasoning
19        self.result: Optional[Any] = None
20        self.error: Optional[str] = None
21
22class AgentPlan:
23    """Represents a multi-step plan"""
24    def __init__(self, goal: str, steps: List[str]):
25        self.goal = goal
26        self.steps = steps
27        self.current_step = 0
28        self.completed_steps: List[bool] = [False] * len(steps)
29
30class AgentPlanner:
31    """Core planning and reasoning engine"""
32
33    def __init__(self, llm_client, tool_registry, memory_store, mode: PlanningMode = PlanningMode.REACT):
34        self.llm = llm_client
35        self.tools = tool_registry
36        self.memory = memory_store
37        self.mode = mode
38        self.logger = logging.getLogger(__name__)
39
40    async def execute_goal(self, goal: str, max_iterations: int = 10) -> Dict[str, Any]:
41        """Execute a goal using the configured planning mode"""
42
43        if self.mode == PlanningMode.REACT:
44            return await self._execute_react(goal, max_iterations)
45        else:
46            return await self._execute_plan_and_execute(goal)
47
48    def _parse_action(self, action_text: str) -> Optional[AgentAction]:
49        """Parse LLM response into structured action"""
50        if ":" in action_text and "{" in action_text:
51            try:
52                parts = action_text.split(":", 1)
53                tool_name = parts[0].strip()
54                params_str = parts[1].strip()
55
56                # Use JSON parsing instead of eval for security
57                try:
58                    parameters = json.loads(params_str)
59                except json.JSONDecodeError:
60                    # Fallback for simple parameter strings
61                    parameters = {"input": params_str}
62
63                return AgentAction(tool_name, parameters)
64            except Exception as e:
65                self.logger.error(f"Failed to parse action: {e}")
66        return None
67
68    async def _execute_react(self, goal: str, max_iterations: int) -> Dict[str, Any]:
69        """ReAct implementation: interleaved reasoning and acting"""
70
71        context = {"goal": goal, "observations": [], "actions": [], "thoughts": []}
72
73        for iteration in range(max_iterations):
74            # Retrieve relevant memories
75            relevant_memories = await self.memory.retrieve(goal, limit=5)
76            context["memories"] = [mem.content for mem in relevant_memories]
77
78            # Generate reasoning
79            thought_prompt = self._build_react_prompt(context, "thought")
80            thought = await self.llm.generate(thought_prompt)
81            context["thoughts"].append(thought)
82
83            # Determine if action is needed
84            action_prompt = self._build_react_prompt(context, "action")
85            action_response = await self.llm.generate(action_prompt)
86
87            if "FINISH" in action_response:
88                break
89
90            # Parse and execute action
91            action = self._parse_action(action_response)
92            if action:
93                try:
94                    result = await self.tools.execute(action.tool_name, action.parameters)
95                    action.result = result
96                    context["actions"].append(action)
97                    context["observations"].append(result)
98
99                    # Store experience in memory
100                    await self._store_experience(goal, action, result)
101
102                except Exception as e:
103                    action.error = str(e)
104                    context["observations"].append(f"Error: {e}")
105                    self.logger.error(f"Action execution failed: {e}")
106
107        return {
108            "success": "FINISH" in action_response if 'action_response' in locals() else False,
109            "context": context,
110            "iterations": iteration + 1
111        }
112
113    def _build_react_prompt(self, context: Dict, prompt_type: str) -> str:
114        """Build ReAct-style prompts for reasoning"""
115        base_context = f"""
116        Goal: {context['goal']}
117        Previous thoughts: {context['thoughts'][-3:] if context['thoughts'] else 'None'}
118        Previous actions: {[a.tool_name for a in context['actions'][-3:]] if context['actions'] else 'None'}
119        Recent observations: {context['observations'][-3:] if context['observations'] else 'None'}
120        Relevant memories: {context.get('memories', [])}
121        """
122
123        if prompt_type == "thought":
124            return base_context + "\nThought: What should I consider next?"
125        elif prompt_type == "action":
126            return base_context + f"\nAvailable tools: {list(self.tools.list_tools())}\nAction: (tool_name: parameters) or FINISH"
127
128    async def _store_experience(self, goal: str, action: AgentAction, result: Any):
129        """Store successful experiences in memory"""
130        experience = MemoryEntry(
131            id=str(uuid.uuid4()),
132            content={
133                "goal": goal,
134                "action": action.tool_name,
135                "parameters": action.parameters,
136                "result": str(result)[:1000],  # Truncate long results
137                "success": action.error is None
138            },
139            timestamp=datetime.now(),
140            memory_type="episodic"
141        )
142        await self.memory.store(experience)
143

1import logging
2from typing import Dict, List, Optional, Any
3from datetime import datetime
4
5class AgentHealthMonitor:
6    """Availability: Health monitoring and fault tolerance for agent systems"""
7
8    def __init__(self, alert_thresholds: Dict[str, float]):
9        self.alert_thresholds = alert_thresholds
10        self.agent_health_data = {}
11        self.circuit_breakers = {}
12
13    async def monitor_agent_health(self, agent_id: str, execution_metrics: Dict):
14        """Monitor agent performance and trigger alerts for degradation"""
15
16        health_score = self._calculate_health_score(execution_metrics)
17        self.agent_health_data[agent_id] = {
18            "health_score": health_score,
19            "last_check": datetime.now(),
20            "metrics": execution_metrics
21        }
22
23        # Check for health degradation
24        if health_score < self.alert_thresholds["critical_health"]:
25            await self._trigger_health_alert(agent_id, "critical", health_score)
26            await self._initiate_recovery_procedure(agent_id)
27        elif health_score < self.alert_thresholds["warning_health"]:
28            await self._trigger_health_alert(agent_id, "warning", health_score)
29
30    def _calculate_health_score(self, metrics: Dict) -> float:
31        """Calculate composite health score from multiple metrics
32
33        Weights based on SRE best practices:
34        - Success rate: 40% (primary indicator of agent effectiveness)
35        - Response latency: 30% (user experience impact)
36        - Error rate: 20% (system reliability)
37        - Resource usage: 10% (operational efficiency)
38        """
39        weights = {
40            "success_rate": 0.4,
41            "response_latency": 0.3,
42            "error_rate": 0.2,
43            "resource_usage": 0.1
44        }
45
46        # Normalize metrics to 0-1 scale
47        normalized_success = metrics.get("success_rate", 0.5)
48        normalized_latency = max(0, 1 - (metrics.get("avg_latency_ms", 1000) / 5000))
49        normalized_errors = max(0, 1 - metrics.get("error_rate", 0.5))
50        normalized_resources = max(0, 1 - metrics.get("cpu_usage", 0.5))
51
52        health_score = (
53            weights["success_rate"] * normalized_success +
54            weights["response_latency"] * normalized_latency +
55            weights["error_rate"] * normalized_errors +
56            weights["resource_usage"] * normalized_resources
57        )
58
59        return health_score
60
61    async def _trigger_health_alert(self, agent_id: str, severity: str, score: float):
62        """Trigger health alert notification"""
63        # Implementation for alert system integration
64        pass
65
66    async def _initiate_recovery_procedure(self, agent_id: str):
67        """Availability: Automatic recovery mechanisms"""
68        # Implement graceful degradation
69        await self._enable_simple_mode(agent_id)
70        # Restart agent with basic configuration
71        await self._restart_agent_safely(agent_id)
72
73    async def _enable_simple_mode(self, agent_id: str):
74        """Enable simplified operation mode"""
75        # Implementation for fallback mode
76        pass
77
78    async def _restart_agent_safely(self, agent_id: str):
79        """Safe agent restart procedure"""
80        # Implementation for safe restart
81        pass
82
83
84

1import logging
2from typing import Dict, List, Optional, Any, Callable
3
4class ExtensibleToolRegistry:
5    """Extensibility: Dynamic tool integration for agents"""
6
7    def __init__(self):
8        self.tools = {}
9        self.tool_schemas = {}
10        self.security_policies = {}
11
12    def register_tool(self, tool_name: str, tool_impl: Callable,
13                     schema: Dict, security_policy: Dict):
14        """Extensibility: Register new tools at runtime"""
15
16        # Validate tool implementation
17        if not self._validate_tool_interface(tool_impl):
18            raise ValueError(f"Tool {tool_name} does not implement required interface")
19
20        # Security: Apply access controls
21        if not self._validate_security_policy(security_policy):
22            raise ValueError(f"Invalid security policy for tool {tool_name}")
23
24        self.tools[tool_name] = tool_impl
25        self.tool_schemas[tool_name] = schema
26        self.security_policies[tool_name] = security_policy
27
28        logging.info(f"Registered new tool: {tool_name}")
29
30    async def execute(self, tool_name: str, parameters: Dict,
31                     agent_id: str = None, context: Dict = None) -> Any:
32        """Security: Controlled tool execution with authorization"""
33
34        # Security: Verify agent authorization
35        if agent_id and not self._check_agent_authorization(agent_id, tool_name):
36            raise PermissionError(f"Agent {agent_id} not authorized for tool {tool_name}")
37
38        # Security: Validate input parameters
39        if not self._validate_parameters(tool_name, parameters):
40            raise ValueError(f"Invalid parameters for tool {tool_name}")
41
42        # Execute with sandboxing
43        try:
44            result = await self._execute_sandboxed(tool_name, parameters, context or {})
45
46            # Audit logging for security
47            await self._log_tool_execution(agent_id, tool_name, parameters, result)
48
49            return result
50
51        except Exception as e:
52            # Security: Log potential security violations
53            await self._log_security_event(agent_id, tool_name, str(e))
54            raise
55
56    def list_tools(self) -> List[str]:
57        """Return list of available tools"""
58        return list(self.tools.keys())
59
60    def _validate_tool_interface(self, tool_impl: Callable) -> bool:
61        """Maintainability: Ensure consistent tool interfaces"""
62        # Check for required methods and signatures
63        required_methods = ["execute", "validate_input", "get_schema"]
64        return all(hasattr(tool_impl, method) for method in required_methods)
65
66    def _validate_security_policy(self, policy: Dict) -> bool:
67        """Validate security policy structure"""
68        required_fields = ["allowed_agents", "rate_limit", "audit_level"]
69        return all(field in policy for field in required_fields)
70
71    def _check_agent_authorization(self, agent_id: str, tool_name: str) -> bool:
72        """Check if agent is authorized to use tool"""
73        policy = self.security_policies.get(tool_name, {})
74        allowed_agents = policy.get("allowed_agents", [])
75        return "*" in allowed_agents or agent_id in allowed_agents
76
77    def _validate_parameters(self, tool_name: str, parameters: Dict) -> bool:
78        """Validate parameters against tool schema"""
79        schema = self.tool_schemas.get(tool_name, {})
80        # Simplified validation - use proper JSON schema validation in production
81        required_params = schema.get("required", [])
82        return all(param in parameters for param in required_params)
83
84    async def _execute_sandboxed(self, tool_name: str, parameters: Dict, context: Dict) -> Any:
85        """Execute tool in sandboxed environment"""
86        tool = self.tools[tool_name]
87        return await tool.execute(parameters, context)
88
89    async def _log_tool_execution(self, agent_id: str, tool_name: str, parameters: Dict, result: Any):
90        """Log tool execution for audit trail"""
91        # Implementation for audit logging
92        pass
93
94    async def _log_security_event(self, agent_id: str, tool_name: str, error: str):
95        """Log security-related events"""
96        # Implementation for security event logging
97        pass
98
99
100

1import asyncio
2import time
3import statistics
4from typing import Dict, List, Tuple, Optional, Any
5from dataclasses import dataclass
6
7@dataclass
8class BenchmarkResult:
9    """Individual benchmark test result"""
10    test_id: str
11    success: bool
12    latency_ms: float
13    accuracy_score: float
14    error_message: Optional[str] = None
15
16class AgentBenchmarkSuite:
17    """Comprehensive benchmarking suite for agentic AI systems"""
18
19    def __init__(self, agent, test_cases: List[Dict]):
20        self.agent = agent
21        self.test_cases = test_cases
22
23    async def run_benchmark(self, iterations: int = 3) -> Dict[str, any]:
24        """Run comprehensive benchmark suite"""
25
26        results = []
27
28        for test_case in self.test_cases:
29            test_results = []
30
31            # Run each test multiple times for reliability measurement
32            for iteration in range(iterations):
33                result = await self._run_single_test(test_case, iteration)
34                test_results.append(result)
35
36            results.extend(test_results)
37
38        return self._analyze_results(results)
39
40    async def _run_single_test(self, test_case: Dict, iteration: int) -> BenchmarkResult:
41        """Execute a single benchmark test"""
42
43        test_id = f"{test_case['id']}_iter_{iteration}"
44        start_time = time.time()
45
46        try:
47            # Execute agent task
48            result = await self.agent.execute_goal(
49                goal=test_case['goal'],
50                max_iterations=test_case.get('max_iterations', 10)
51            )
52
53            latency = (time.time() - start_time) * 1000
54
55            # Evaluate result quality
56            accuracy = self._evaluate_accuracy(result, test_case['expected'])
57            success = accuracy >= test_case.get('success_threshold', 0.8)
58
59            return BenchmarkResult(
60                test_id=test_id,
61                success=success,
62                latency_ms=latency,
63                accuracy_score=accuracy
64            )
65
66        except Exception as e:
67            return BenchmarkResult(
68                test_id=test_id,
69                success=False,
70                latency_ms=(time.time() - start_time) * 1000,
71                accuracy_score=0.0,
72                error_message=str(e)
73            )
74
75    def _evaluate_accuracy(self, actual_result: Dict, expected_result: Dict) -> float:
76        """Evaluate accuracy of agent result against expected outcome"""
77
78        if not actual_result.get('success', False):
79            return 0.0
80
81        # Compare key metrics based on test type
82        accuracy_scores = []
83
84        # Goal completion accuracy
85        if 'goal_completed' in expected_result:
86            goal_score = 1.0 if actual_result.get('success') == expected_result['goal_completed'] else 0.0
87            accuracy_scores.append(goal_score)
88
89        # Output quality assessment
90        if 'expected_output' in expected_result:
91            output_similarity = self._calculate_similarity(
92                actual_result.get('final_output', ''),
93                expected_result['expected_output']
94            )
95            accuracy_scores.append(output_similarity)
96
97        # Tool usage accuracy
98        if 'expected_tools' in expected_result:
99            tools_used = set(action.tool_name for action in actual_result.get('context', {}).get('actions', []))
100            expected_tools = set(expected_result['expected_tools'])
101            tool_accuracy = len(tools_used.intersection(expected_tools)) / len(expected_tools) if expected_tools else 1.0
102            accuracy_scores.append(tool_accuracy)
103
104        return statistics.mean(accuracy_scores) if accuracy_scores else 0.0
105
106    def _calculate_similarity(self, text1: str, text2: str) -> float:
107        """Calculate semantic similarity between texts"""
108        # Simplified implementation - use proper semantic similarity in production
109        words1 = set(text1.lower().split())
110        words2 = set(text2.lower().split())
111
112        if not words1 and not words2:
113            return 1.0
114        if not words1 or not words2:
115            return 0.0
116
117        intersection = words1.intersection(words2)
118        union = words1.union(words2)
119
120        return len(intersection) / len(union)
121
122    def _analyze_results(self, results: List[BenchmarkResult]) -> Dict[str, any]:
123        """Analyze benchmark results and generate comprehensive report"""
124
125        successful_results = [r for r in results if r.success]
126
127        # Basic statistics
128        success_rate = len(successful_results) / len(results) if results else 0.0
129        avg_latency = statistics.mean([r.latency_ms for r in results]) if results else 0.0
130        p95_latency = statistics.quantiles([r.latency_ms for r in results], n=20)[18] if len(results) >= 20 else 0.0
131        avg_accuracy = statistics.mean([r.accuracy_score for r in successful_results]) if successful_results else 0.0
132
133        # Reliability metrics
134        pass_at_k_scores = self._calculate_pass_at_k(results)
135
136        # Error analysis
137        error_types = {}
138        for result in results:
139            if not result.success and result.error_message:
140                error_type = result.error_message.split(':')[0]
141                error_types[error_type] = error_types.get(error_type, 0) + 1
142
143        return {
144            "overall_metrics": {
145                "success_rate": success_rate,
146                "average_latency_ms": avg_latency,
147                "p95_latency_ms": p95_latency,
148                "average_accuracy": avg_accuracy,
149                "total_tests": len(results),
150                "successful_tests": len(successful_results)
151            },
152            "reliability_metrics": {
153                "pass_at_1": pass_at_k_scores.get(1, 0.0),
154                "pass_at_3": pass_at_k_scores.get(3, 0.0),
155                "pass_at_5": pass_at_k_scores.get(5, 0.0)
156            },
157            "error_analysis": error_types,
158            "detailed_results": [
159                {
160                    "test_id": r.test_id,
161                    "success": r.success,
162                    "latency_ms": r.latency_ms,
163                    "accuracy": r.accuracy_score,
164                    "error": r.error_message
165                }
166                for r in results
167            ]
168        }
169
170    def _calculate_pass_at_k(self, results: List[BenchmarkResult]) -> Dict[int, float]:
171        """Calculate pass@k metrics for reliability assessment"""
172
173        # Group results by test case
174        test_groups = {}
175        for result in results:
176            test_base = result.test_id.split('_iter_')[0]
177            if test_base not in test_groups:
178                test_groups[test_base] = []
179            test_groups[test_base].append(result.success)
180
181        pass_at_k = {}
182        for k in [1, 3, 5]:
183            if k <= min(len(group) for group in test_groups.values()):
184                successes = 0
185                total = 0
186
187                for group in test_groups.values():
188                    # Check if at least one of first k attempts succeeded
189                    if any(group[:k]):
190                        successes += 1
191                    total += 1
192
193                pass_at_k[k] = successes / total if total > 0 else 0.0
194
195        return pass_at_k
196

1version: '3.8'
2services:
3  agentic-ai-app:
4    build: .
5    ports:
6      - "8000:8000"
7    environment:
8      - OPENAI_API_KEY=${OPENAI_API_KEY}
9      - VECTOR_DB_URL=http://weaviate:8080
10      - GRAPH_DB_URL=bolt://neo4j:7687
11      - REDIS_URL=redis://redis:6379
12    depends_on:
13      - weaviate
14      - neo4j
15      - redis
16    volumes:
17      - ./config:/app/config
18      - ./logs:/app/logs
19
20  weaviate:
21    image: semitechnologies/weaviate:1.22.4
22    ports:
23      - "8080:8080"
24    environment:
25      QUERY_DEFAULTS_LIMIT: 25
26      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
27      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
28      DEFAULT_VECTORIZER_MODULE: 'none'
29      ENABLE_MODULES: 'text2vec-openai'
30    volumes:
31      - weaviate_data:/var/lib/weaviate
32
33  neo4j:
34    image: neo4j:5.13
35    ports:
36      - "7474:7474"
37      - "7687:7687"
38    environment:
39      NEO4J_AUTH: neo4j/password
40      NEO4J_PLUGINS: '["apoc"]'
41      NEO4J_apoc_export_file_enabled: true
42      NEO4J_apoc_import_file_enabled: true
43    volumes:
44      - neo4j_data:/data
45      - neo4j_logs:/logs
46
47  redis:
48    image: redis:7.2-alpine
49    ports:
50      - "6379:6379"
51    volumes:
52      - redis_data:/data
53    command: redis-server --appendonly yes
54
55volumes:
56  weaviate_data:
57  neo4j_data:
58  neo4j_logs:
59  redis_data:
60