Software Design in the Age of AI: Architecting for Intelligence

As AI becomes integral to software systems, design principles must evolve to accommodate intelligent agents, complex data flows, and the unique challenges of machine learning systems. This involves fundamentally revisiting traditional software design through an AI-first lens.

The shift from building applications to architecting intelligence requires new patterns, methodologies, and considerations that go far beyond traditional software design principles.

1. Architectural Evolution: From Monoliths to Agentic Microservices

Traditional software design patterns remain relevant but now extend to accommodate AI components as first-class architectural citizens, requiring new approaches to modularity, scalability, and orchestration.

AI-First Microservices Architecture

Modern AI applications require a hybrid approach that combines traditional microservices with specialized AI agent services:

# Example: AI-first microservices architecture
from fastapi import FastAPI, HTTPException, BackgroundTasks
from pydantic import BaseModel, Field
from typing import Dict, List, Any, Optional, Union
from enum import Enum
import asyncio
import httpx
from datetime import datetime
import uuid

class ServiceType(Enum):
    TRADITIONAL = "traditional"
    AI_AGENT = "ai_agent"
    DATA_PIPELINE = "data_pipeline"
    ML_MODEL = "ml_model"

class ServiceStatus(Enum):
    HEALTHY = "healthy"
    DEGRADED = "degraded"
    UNHEALTHY = "unhealthy"

@BaseModel
class ServiceConfig:
    name: str
    service_type: ServiceType
    endpoint: str
    capabilities: List[str]
    dependencies: List[str] = Field(default_factory=list)
    health_check_endpoint: str = "/health"
    timeout_seconds: int = 30

@BaseModel
class AgentRequest:
    task_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    task_type: str
    payload: Dict[str, Any]
    priority: int = Field(default=1, ge=1, le=10)
    context: Optional[Dict[str, Any]] = None

@BaseModel
class AgentResponse:
    task_id: str
    success: bool
    result: Optional[Any] = None
    error_message: Optional[str] = None
    processing_time_ms: int
    agent_id: str

class AIOrchestrator:
    """Orchestrates AI agents and traditional services"""
    
    def __init__(self):
        self.services: Dict[str, ServiceConfig] = {}
        self.service_health: Dict[str, ServiceStatus] = {}
        self.load_balancer = LoadBalancer()
        self.circuit_breaker = CircuitBreaker()
    
    def register_service(self, config: ServiceConfig):
        """Register a service in the architecture"""
        self.services[config.name] = config
        self.service_health[config.name] = ServiceStatus.HEALTHY
        
        if config.service_type == ServiceType.AI_AGENT:
            self._setup_agent_monitoring(config)
    
    async def execute_workflow(self, workflow_definition: Dict[str, Any]) -> Dict[str, Any]:
        """Execute complex workflow involving multiple services"""
        workflow_id = str(uuid.uuid4())
        results = {"workflow_id": workflow_id, "steps": []}
        
        try:
            for step in workflow_definition.get("steps", []):
                step_result = await self._execute_step(step)
                results["steps"].append(step_result)
                
                # Check if this step's output should be input to next step
                if step.get("pass_output_to_next", False) and results["steps"]:
                    next_step_index = len(results["steps"])
                    if next_step_index < len(workflow_definition["steps"]):
                        next_step = workflow_definition["steps"][next_step_index]
                        next_step["payload"]["previous_result"] = step_result
            
            return {
                **results,
                "status": "completed",
                "total_steps": len(results["steps"])
            }
            
        except Exception as e:
            return {
                **results,
                "status": "failed",
                "error": str(e),
                "failed_at_step": len(results["steps"])
            }
    
    async def _execute_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
        """Execute individual workflow step"""
        service_name = step.get("service")
        task_type = step.get("task_type")
        payload = step.get("payload", {})
        
        if service_name not in self.services:
            raise ValueError(f"Service {service_name} not found")
        
        service_config = self.services[service_name]
        
        # Check service health before calling
        if self.service_health[service_name] != ServiceStatus.HEALTHY:
            # Try backup service or graceful degradation
            backup_result = await self._handle_service_degradation(service_name, step)
            if backup_result:
                return backup_result
            
            raise Exception(f"Service {service_name} is not healthy")
        
        # Execute based on service type
        if service_config.service_type == ServiceType.AI_AGENT:
            return await self._call_ai_agent(service_config, task_type, payload)
        else:
            return await self._call_traditional_service(service_config, task_type, payload)
    
    async def _call_ai_agent(self, config: ServiceConfig, task_type: str, payload: Dict[str, Any]) -> Dict[str, Any]:
        """Call AI agent service with specialized handling"""
        request = AgentRequest(
            task_type=task_type,
            payload=payload
        )
        
        start_time = datetime.now()
        
        async with httpx.AsyncClient(timeout=config.timeout_seconds) as client:
            try:
                response = await client.post(
                    f"{config.endpoint}/agent/execute",
                    json=request.dict()
                )
                response.raise_for_status()
                
                processing_time = int((datetime.now() - start_time).total_seconds() * 1000)
                
                agent_response = AgentResponse(**response.json())
                agent_response.processing_time_ms = processing_time
                
                return {
                    "service": config.name,
                    "type": "ai_agent",
                    "task_id": agent_response.task_id,
                    "success": agent_response.success,
                    "result": agent_response.result,
                    "processing_time_ms": processing_time
                }
                
            except httpx.TimeoutException:
                await self._handle_agent_timeout(config.name, request)
                raise Exception(f"Agent {config.name} timed out")
            
            except httpx.HTTPStatusError as e:
                await self._handle_agent_error(config.name, request, str(e))
                raise Exception(f"Agent {config.name} returned error: {e}")
    
    async def _call_traditional_service(self, config: ServiceConfig, task_type: str, payload: Dict[str, Any]) -> Dict[str, Any]:
        """Call traditional microservice"""
        async with httpx.AsyncClient(timeout=config.timeout_seconds) as client:
            response = await client.post(
                f"{config.endpoint}/execute",
                json={"task_type": task_type, "payload": payload}
            )
            response.raise_for_status()
            
            return {
                "service": config.name,
                "type": "traditional",
                "result": response.json(),
                "processing_time_ms": int(response.elapsed.total_seconds() * 1000)
            }
    
    async def _handle_service_degradation(self, service_name: str, step: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Handle degraded service with fallback strategies"""
        # Implement circuit breaker, fallback services, or graceful degradation
        fallback_service = step.get("fallback_service")
        
        if fallback_service and fallback_service in self.services:
            if self.service_health[fallback_service] == ServiceStatus.HEALTHY:
                step_copy = step.copy()
                step_copy["service"] = fallback_service
                return await self._execute_step(step_copy)
        
        # Implement graceful degradation
        degraded_response = step.get("degraded_response")
        if degraded_response:
            return {
                "service": service_name,
                "type": "degraded",
                "result": degraded_response,
                "processing_time_ms": 0
            }
        
        return None
    
    def _setup_agent_monitoring(self, config: ServiceConfig):
        """Setup specialized monitoring for AI agents"""
        # Monitor agent-specific metrics
        pass
    
    async def _handle_agent_timeout(self, agent_name: str, request: AgentRequest):
        """Handle AI agent timeout scenarios"""
        # Log timeout, potentially retry with different parameters
        pass
    
    async def _handle_agent_error(self, agent_name: str, request: AgentRequest, error: str):
        """Handle AI agent errors"""
        # Log error, update agent health status if needed
        pass

class LoadBalancer:
    """Load balancer for AI services"""
    
    def __init__(self):
        self.service_instances: Dict[str, List[str]] = {}
        self.round_robin_counters: Dict[str, int] = {}
    
    def add_instance(self, service_name: str, endpoint: str):
        """Add service instance"""
        if service_name not in self.service_instances:
            self.service_instances[service_name] = []
            self.round_robin_counters[service_name] = 0
        
        self.service_instances[service_name].append(endpoint)
    
    def get_instance(self, service_name: str) -> str:
        """Get next instance using round-robin"""
        if service_name not in self.service_instances:
            raise ValueError(f"No instances for service {service_name}")
        
        instances = self.service_instances[service_name]
        if not instances:
            raise ValueError(f"No healthy instances for service {service_name}")
        
        counter = self.round_robin_counters[service_name]
        instance = instances[counter % len(instances)]
        self.round_robin_counters[service_name] = (counter + 1) % len(instances)
        
        return instance

class CircuitBreaker:
    """Circuit breaker for AI services"""
    
    def __init__(self, failure_threshold: int = 5, recovery_timeout: int = 60):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count: Dict[str, int] = {}
        self.last_failure_time: Dict[str, datetime] = {}
        self.circuit_open: Dict[str, bool] = {}
    
    def is_circuit_open(self, service_name: str) -> bool:
        """Check if circuit is open for service"""
        if service_name not in self.circuit_open:
            return False
        
        if self.circuit_open[service_name]:
            # Check if recovery timeout has passed
            last_failure = self.last_failure_time.get(service_name)
            if last_failure:
                time_since_failure = (datetime.now() - last_failure).seconds
                if time_since_failure > self.recovery_timeout:
                    self.circuit_open[service_name] = False
                    self.failure_count[service_name] = 0
                    return False
            
            return True
        
        return False
    
    def record_success(self, service_name: str):
        """Record successful call"""
        self.failure_count[service_name] = 0
        self.circuit_open[service_name] = False
    
    def record_failure(self, service_name: str):
        """Record failed call"""
        self.failure_count[service_name] = self.failure_count.get(service_name, 0) + 1
        self.last_failure_time[service_name] = datetime.now()
        
        if self.failure_count[service_name] >= self.failure_threshold:
            self.circuit_open[service_name] = True

# Example usage
async def demo_ai_orchestrator():
    orchestrator = AIOrchestrator()
    
    # Register services
    orchestrator.register_service(ServiceConfig(
        name="research_agent",
        service_type=ServiceType.AI_AGENT,
        endpoint="http://localhost:8001",
        capabilities=["research", "analysis", "summarization"]
    ))
    
    orchestrator.register_service(ServiceConfig(
        name="writer_agent", 
        service_type=ServiceType.AI_AGENT,
        endpoint="http://localhost:8002",
        capabilities=["writing", "editing", "content_creation"]
    ))
    
    orchestrator.register_service(ServiceConfig(
        name="data_service",
        service_type=ServiceType.TRADITIONAL,
        endpoint="http://localhost:8003",
        capabilities=["data_retrieval", "storage"]
    ))
    
    # Define complex workflow
    workflow = {
        "name": "content_creation_pipeline",
        "steps": [
            {
                "service": "data_service",
                "task_type": "fetch_topic_data",
                "payload": {"topic": "AI trends 2025"},
                "pass_output_to_next": True
            },
            {
                "service": "research_agent", 
                "task_type": "analyze_trends",
                "payload": {"analysis_depth": "comprehensive"},
                "pass_output_to_next": True
            },
            {
                "service": "writer_agent",
                "task_type": "create_article",
                "payload": {"style": "technical", "length": "medium"},
                "fallback_service": "simple_writer"
            }
        ]
    }
    
    # Execute workflow
    result = await orchestrator.execute_workflow(workflow)
    print(f"Workflow completed: {result['status']}")
    print(f"Total steps: {result['total_steps']}")

# asyncio.run(demo_ai_orchestrator())

Container Orchestration for AI Workloads

AI workloads have unique requirements that traditional container orchestration must accommodate:

2. Designing for Data and Context: The RAG Paradigm

The Retrieval Augmented Generation (RAG) pattern represents a fundamental shift in how we think about software design, emphasizing that RAG is fundamentally an information architecture problem, not just an AI problem.

User Query Auditing: Understanding Real Needs

Effective RAG design starts with understanding user behavior through comprehensive query auditing:

# Example: Query auditing and analysis system
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set
from datetime import datetime, timedelta
from collections import Counter, defaultdict
import re
from enum import Enum

class QueryType(Enum):
    FACTUAL = "factual"
    PROCEDURAL = "procedural"
    CONCEPTUAL = "conceptual"
    COMPARATIVE = "comparative"
    TROUBLESHOOTING = "troubleshooting"

@dataclass
class QueryAuditRecord:
    query: str
    timestamp: datetime
    user_id: str
    session_id: str
    results_count: int
    user_clicked_result: bool
    user_satisfaction_score: Optional[float] = None
    query_type: Optional[QueryType] = None
    processing_time_ms: int = 0

class QueryAuditor:
    """Comprehensive query auditing and analysis system"""
    
    def __init__(self):
        self.query_records: List[QueryAuditRecord] = []
        self.query_patterns: Dict[str, int] = Counter()
        self.zero_result_queries: Set[str] = set()
        self.high_frequency_queries: Dict[str, int] = Counter()
    
    def record_query(self, record: QueryAuditRecord):
        """Record a new query for analysis"""
        self.query_records.append(record)
        self.query_patterns[record.query.lower()] += 1
        
        if record.results_count == 0:
            self.zero_result_queries.add(record.query.lower())
        
        # Classify query type
        record.query_type = self._classify_query(record.query)
    
    def _classify_query(self, query: str) -> QueryType:
        """Classify query type using pattern matching"""
        query_lower = query.lower()
        
        # Procedural queries (how-to)
        if any(phrase in query_lower for phrase in ['how to', 'how do', 'steps to', 'tutorial']):
            return QueryType.PROCEDURAL
        
        # Troubleshooting queries
        if any(phrase in query_lower for phrase in ['error', 'problem', 'issue', 'not working', 'broken']):
            return QueryType.TROUBLESHOOTING
        
        # Comparative queries
        if any(phrase in query_lower for phrase in ['vs', 'versus', 'compare', 'difference', 'better']):
            return QueryType.COMPARATIVE
        
        # Conceptual queries
        if any(phrase in query_lower for phrase in ['what is', 'explain', 'concept', 'theory']):
            return QueryType.CONCEPTUAL
        
        # Default to factual
        return QueryType.FACTUAL
    
    def analyze_query_patterns(self, days_back: int = 30) -> Dict[str, Any]:
        """Analyze query patterns to inform RAG improvements"""
        cutoff_date = datetime.now() - timedelta(days=days_back)
        recent_queries = [r for r in self.query_records if r.timestamp >= cutoff_date]
        
        if not recent_queries:
            return {"error": "No recent queries found"}
        
        # Analyze zero-result queries
        zero_result_analysis = self._analyze_zero_results(recent_queries)
        
        # Analyze high-frequency queries
        frequency_analysis = self._analyze_frequency_patterns(recent_queries)
        
        # Analyze query types
        type_analysis = self._analyze_query_types(recent_queries)
        
        # Identify content gaps
        content_gaps = self._identify_content_gaps(recent_queries)
        
        # User satisfaction analysis
        satisfaction_analysis = self._analyze_satisfaction(recent_queries)
        
        return {
            "analysis_period": f"{days_back} days",
            "total_queries": len(recent_queries),
            "unique_queries": len(set(r.query.lower() for r in recent_queries)),
            "zero_result_analysis": zero_result_analysis,
            "frequency_analysis": frequency_analysis,
            "query_type_analysis": type_analysis,
            "content_gaps": content_gaps,
            "satisfaction_analysis": satisfaction_analysis,
            "recommendations": self._generate_recommendations(recent_queries)
        }
    
    def _analyze_zero_results(self, queries: List[QueryAuditRecord]) -> Dict[str, Any]:
        """Analyze queries that returned zero results"""
        zero_result_queries = [q for q in queries if q.results_count == 0]
        total_zero = len(zero_result_queries)
        zero_percentage = (total_zero / len(queries)) * 100 if queries else 0
        
        # Group similar zero-result queries
        zero_patterns = Counter(q.query.lower() for q in zero_result_queries)
        
        return {
            "total_zero_result_queries": total_zero,
            "zero_result_percentage": round(zero_percentage, 2),
            "most_common_zero_queries": zero_patterns.most_common(10),
            "zero_result_themes": self._extract_themes(zero_result_queries)
        }
    
    def _analyze_frequency_patterns(self, queries: List[QueryAuditRecord]) -> Dict[str, Any]:
        """Analyze high-frequency query patterns"""
        query_counts = Counter(q.query.lower() for q in queries)
        high_freq_threshold = max(3, len(queries) * 0.01)  # At least 3 or 1% of queries
        
        high_frequency = {q: count for q, count in query_counts.items() if count >= high_freq_threshold}
        
        return {
            "high_frequency_threshold": high_freq_threshold,
            "high_frequency_queries": dict(Counter(high_frequency).most_common(15)),
            "repeated_query_percentage": round((sum(high_frequency.values()) / len(queries)) * 100, 2)
        }
    
    def _analyze_query_types(self, queries: List[QueryAuditRecord]) -> Dict[str, Any]:
        """Analyze distribution of query types"""
        type_counts = Counter(q.query_type for q in queries if q.query_type)
        
        return {
            "query_type_distribution": {qtype.value: count for qtype, count in type_counts.items()},
            "dominant_query_type": type_counts.most_common(1)[0][0].value if type_counts else None
        }
    
    def _identify_content_gaps(self, queries: List[QueryAuditRecord]) -> List[str]:
        """Identify content gaps based on query analysis"""
        gaps = []
        
        # Check for high zero-result rate
        zero_rate = len([q for q in queries if q.results_count == 0]) / len(queries)
        if zero_rate > 0.15:  # More than 15% zero results
            gaps.append("High zero-result rate indicates missing content")
        
        # Check for low satisfaction with existing results
        satisfied_queries = [q for q in queries if q.user_satisfaction_score and q.user_satisfaction_score >= 4.0]
        if len(satisfied_queries) / len([q for q in queries if q.user_satisfaction_score]) < 0.7:
            gaps.append("Low user satisfaction indicates content quality issues")
        
        # Check for repeated queries (users not finding what they need)
        query_counts = Counter(q.query.lower() for q in queries)
        repeated = sum(1 for count in query_counts.values() if count > 2)
        if repeated / len(query_counts) > 0.2:
            gaps.append("High query repetition suggests incomplete answers")
        
        return gaps
    
    def _analyze_satisfaction(self, queries: List[QueryAuditRecord]) -> Dict[str, Any]:
        """Analyze user satisfaction with query results"""
        satisfaction_queries = [q for q in queries if q.user_satisfaction_score is not None]
        
        if not satisfaction_queries:
            return {"error": "No satisfaction data available"}
        
        avg_satisfaction = sum(q.user_satisfaction_score for q in satisfaction_queries) / len(satisfaction_queries)
        click_through_rate = len([q for q in queries if q.user_clicked_result]) / len(queries)
        
        return {
            "average_satisfaction": round(avg_satisfaction, 2),
            "total_rated_queries": len(satisfaction_queries),
            "click_through_rate": round(click_through_rate * 100, 2),
            "high_satisfaction_queries": len([q for q in satisfaction_queries if q.user_satisfaction_score >= 4.0])
        }
    
    def _extract_themes(self, queries: List[QueryAuditRecord]) -> List[str]:
        """Extract themes from queries using simple keyword analysis"""
        all_words = []
        for query in queries:
            # Simple tokenization and cleaning
            words = re.findall(r'\b[a-zA-Z]{3,}\b', query.query.lower())
            all_words.extend(words)
        
        # Remove common stop words
        stop_words = {'the', 'and', 'for', 'are', 'but', 'not', 'you', 'all', 'can', 'had', 'was', 'one', 'our', 'has', 'how', 'what', 'when', 'where', 'who', 'why'}
        filtered_words = [word for word in all_words if word not in stop_words]
        
        # Get most common themes
        word_counts = Counter(filtered_words)
        return [word for word, count in word_counts.most_common(10)]
    
    def _generate_recommendations(self, queries: List[QueryAuditRecord]) -> List[str]:
        """Generate actionable recommendations based on analysis"""
        recommendations = []
        
        # Zero result queries
        zero_rate = len([q for q in queries if q.results_count == 0]) / len(queries)
        if zero_rate > 0.10:
            recommendations.append(f"Address high zero-result rate ({zero_rate:.1%}) by expanding content coverage")
        
        # Query type distribution
        type_counts = Counter(q.query_type for q in queries if q.query_type)
        if type_counts:
            dominant_type = type_counts.most_common(1)[0][0]
            if dominant_type == QueryType.PROCEDURAL:
                recommendations.append("Focus on creating more step-by-step guides and tutorials")
            elif dominant_type == QueryType.TROUBLESHOOTING:
                recommendations.append("Expand troubleshooting documentation and error resolution guides")
        
        # Satisfaction issues
        satisfaction_queries = [q for q in queries if q.user_satisfaction_score is not None]
        if satisfaction_queries:
            avg_satisfaction = sum(q.user_satisfaction_score for q in satisfaction_queries) / len(satisfaction_queries)
            if avg_satisfaction < 3.5:
                recommendations.append("Improve content quality to address low user satisfaction")
        
        # High frequency queries
        query_counts = Counter(q.query.lower() for q in queries)
        high_freq = [q for q, count in query_counts.items() if count >= 5]
        if high_freq:
            recommendations.append(f"Optimize content for {len(high_freq)} high-frequency queries")
        
        return recommendations

# Example usage
def demo_query_auditing():
    auditor = QueryAuditor()
    
    # Simulate query records
    sample_queries = [
        QueryAuditRecord("how to deploy kubernetes", datetime.now(), "user1", "session1", 5, True, 4.5),
        QueryAuditRecord("kubernetes error pod crash", datetime.now(), "user2", "session2", 0, False, 2.0),
        QueryAuditRecord("docker vs kubernetes", datetime.now(), "user3", "session3", 8, True, 4.0),
        QueryAuditRecord("what is service mesh", datetime.now(), "user4", "session4", 3, False, 3.5),
        QueryAuditRecord("how to deploy kubernetes", datetime.now(), "user1", "session5", 5, True, 4.5),
    ]
    
    for query in sample_queries:
        auditor.record_query(query)
    
    analysis = auditor.analyze_query_patterns()
    print("Query Analysis Results:")
    print(f"Total queries: {analysis['total_queries']}")
    print(f"Zero result rate: {analysis['zero_result_analysis']['zero_result_percentage']}%")
    print("Recommendations:")
    for rec in analysis['recommendations']:
        print(f"- {rec}")

# demo_query_auditing()

Advanced RAG Patterns: Beyond Simple Retrieval

Modern RAG implementations require sophisticated patterns to handle complex information needs:

3. Agentic Design Patterns: Autonomy and Collaboration

Designing AI agents introduces fundamentally new paradigms that require careful consideration of autonomy, tool usage, and multi-agent coordination.

Agent Architecture: The Think-Act-Observe Loop

Effective agents follow a continuous cycle of reasoning, action, and observation that must be architected for reliability and performance:

# Example: Production-ready agent architecture
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Dict, List, Any, Optional, Callable, Union
from enum import Enum
import asyncio
import json
import logging
from datetime import datetime
import uuid

class AgentState(Enum):
    IDLE = "idle"
    THINKING = "thinking"
    ACTING = "acting"
    OBSERVING = "observing"
    ERROR = "error"

class ToolResult:
    def __init__(self, success: bool, result: Any = None, error: str = None):
        self.success = success
        self.result = result
        self.error = error

@dataclass
class AgentMemory:
    """Agent's memory structure"""
    working_memory: List[Dict[str, Any]] = field(default_factory=list)
    long_term_memory: Dict[str, Any] = field(default_factory=dict)
    tool_usage_history: List[Dict[str, Any]] = field(default_factory=list)
    performance_metrics: Dict[str, float] = field(default_factory=dict)

class Tool(ABC):
    """Abstract base class for agent tools"""
    
    @property
    @abstractmethod
    def name(self) -> str:
        pass
    
    @property 
    @abstractmethod
    def description(self) -> str:
        pass
    
    @property
    @abstractmethod
    def parameters_schema(self) -> Dict[str, Any]:
        pass
    
    @abstractmethod
    async def execute(self, **kwargs) -> ToolResult:
        pass

class WebSearchTool(Tool):
    """Web search tool implementation"""
    
    @property
    def name(self) -> str:
        return "web_search"
    
    @property
    def description(self) -> str:
        return "Search the web for current information"
    
    @property
    def parameters_schema(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "max_results": {"type": "integer", "default": 5}
            },
            "required": ["query"]
        }
    
    async def execute(self, query: str, max_results: int = 5) -> ToolResult:
        """Execute web search (mock implementation)"""
        try:
            await asyncio.sleep(0.5)  # Simulate API call
            
            results = [
                {"title": f"Result {i+1} for {query}", "url": f"http://example.com/{i+1}"}
                for i in range(max_results)
            ]
            
            return ToolResult(success=True, result=results)
        except Exception as e:
            return ToolResult(success=False, error=str(e))

class FileReadTool(Tool):
    """File reading tool implementation"""
    
    @property
    def name(self) -> str:
        return "read_file"
    
    @property
    def description(self) -> str:
        return "Read contents of a file"
    
    @property
    def parameters_schema(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "filepath": {"type": "string", "description": "Path to file to read"}
            },
            "required": ["filepath"]
        }
    
    async def execute(self, filepath: str) -> ToolResult:
        """Execute file read"""
        try:
            # In production, add security checks for file access
            with open(filepath, 'r') as f:
                content = f.read()
            return ToolResult(success=True, result=content)
        except FileNotFoundError:
            return ToolResult(success=False, error=f"File not found: {filepath}")
        except PermissionError:
            return ToolResult(success=False, error=f"Permission denied: {filepath}")
        except Exception as e:
            return ToolResult(success=False, error=str(e))

class BaseAgent:
    """Base agent implementing the think-act-observe loop"""
    
    def __init__(self, agent_id: str, llm_client, tools: List[Tool] = None):
        self.agent_id = agent_id
        self.llm_client = llm_client
        self.tools: Dict[str, Tool] = {tool.name: tool for tool in (tools or [])}
        self.memory = AgentMemory()
        self.state = AgentState.IDLE
        self.logger = logging.getLogger(f"agent_{agent_id}")
        self.max_iterations = 10
        self.current_task = None
    
    async def execute_task(self, task: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Execute a task using think-act-observe loop"""
        self.current_task = task
        self.state = AgentState.THINKING
        
        task_id = str(uuid.uuid4())
        execution_log = []
        
        try:
            # Initialize task in working memory
            self.memory.working_memory.append({
                "type": "task_start",
                "task": task,
                "context": context or {},
                "timestamp": datetime.now().isoformat(),
                "task_id": task_id
            })
            
            iterations = 0
            while iterations < self.max_iterations:
                iterations += 1
                
                # Think: Reason about next action
                thought = await self._think()
                execution_log.append({"step": "think", "iteration": iterations, "content": thought})
                
                # Check if task is complete
                if thought.get("task_complete", False):
                    result = thought.get("final_result", "Task completed")
                    break
                
                # Act: Execute planned action
                if "action" in thought:
                    self.state = AgentState.ACTING
                    action_result = await self._act(thought["action"])
                    execution_log.append({"step": "act", "iteration": iterations, "content": action_result})
                    
                    # Observe: Process the action result
                    self.state = AgentState.OBSERVING
                    observation = await self._observe(action_result)
                    execution_log.append({"step": "observe", "iteration": iterations, "content": observation})
                    
                    # Update working memory with observation
                    self.memory.working_memory.append({
                        "type": "observation",
                        "content": observation,
                        "timestamp": datetime.now().isoformat(),
                        "iteration": iterations
                    })
                
                # Prevent infinite loops
                if len(self.memory.working_memory) > 50:
                    result = "Task terminated: Maximum memory limit reached"
                    break
            else:
                result = "Task terminated: Maximum iterations reached"
            
            self.state = AgentState.IDLE
            return {
                "task_id": task_id,
                "result": result,
                "iterations": iterations,
                "execution_log": execution_log,
                "final_memory": self.memory.working_memory[-5:]  # Last 5 memory items
            }
            
        except Exception as e:
            self.state = AgentState.ERROR
            self.logger.error(f"Task execution failed: {str(e)}")
            return {
                "task_id": task_id,
                "error": str(e),
                "execution_log": execution_log
            }
    
    async def _think(self) -> Dict[str, Any]:
        """Think about the next action to take"""
        # Prepare context from working memory
        recent_memory = self.memory.working_memory[-5:]  # Last 5 items
        context = "\n".join([
            f"{item['type']}: {json.dumps(item.get('content', item), indent=2)}"
            for item in recent_memory
        ])
        
        # Available tools description
        tools_description = "\n".join([
            f"- {name}: {tool.description}"
            for name, tool in self.tools.items()
        ])
        
        thinking_prompt = f"""
You are an AI agent working on this task: {self.current_task}

Available tools:
{tools_description}

Recent context and memory:
{context}

Based on the task and context, decide your next action. Respond with JSON containing:
- "reasoning": Your thought process
- "action": If you need to use a tool, specify {{"tool": "tool_name", "parameters": {{...}}}}
- "task_complete": true if the task is finished
- "final_result": If task_complete is true, provide the final result

Think step by step about what information you need and what action to take next.
"""
        
        try:
            # This would call your LLM client
            response = await self._call_llm(thinking_prompt)
            
            # Parse JSON response
            thought = json.loads(response)
            
            self.memory.working_memory.append({
                "type": "thought",
                "content": thought,
                "timestamp": datetime.now().isoformat()
            })
            
            return thought
            
        except Exception as e:
            self.logger.error(f"Thinking failed: {str(e)}")
            return {
                "reasoning": f"Error in thinking: {str(e)}",
                "task_complete": True,
                "final_result": f"Task failed due to thinking error: {str(e)}"
            }
    
    async def _act(self, action: Dict[str, Any]) -> Dict[str, Any]:
        """Execute an action using available tools"""
        if "tool" not in action or "parameters" not in action:
            return {"error": "Invalid action format"}
        
        tool_name = action["tool"]
        parameters = action["parameters"]
        
        if tool_name not in self.tools:
            return {"error": f"Tool '{tool_name}' not available"}
        
        tool = self.tools[tool_name]
        
        try:
            # Record tool usage
            usage_record = {
                "tool": tool_name,
                "parameters": parameters,
                "timestamp": datetime.now().isoformat()
            }
            
            result = await tool.execute(**parameters)
            
            usage_record["success"] = result.success
            usage_record["result"] = result.result if result.success else result.error
            
            self.memory.tool_usage_history.append(usage_record)
            
            return {
                "tool_used": tool_name,
                "success": result.success,
                "result": result.result,
                "error": result.error
            }
            
        except Exception as e:
            self.logger.error(f"Action execution failed: {str(e)}")
            return {
                "tool_used": tool_name,
                "success": False,
                "error": str(e)
            }
    
    async def _observe(self, action_result: Dict[str, Any]) -> Dict[str, Any]:
        """Observe and interpret the results of an action"""
        observation_prompt = f"""
You just executed an action with this result:
{json.dumps(action_result, indent=2)}

Analyze the result and provide an observation in JSON format:
- "success": Whether the action was successful
- "key_information": Important information extracted from the result
- "next_step_suggestion": What should be done next based on this result
- "confidence": Your confidence in the result (0.0 to 1.0)
"""
        
        try:
            response = await self._call_llm(observation_prompt)
            observation = json.loads(response)
            
            return observation
            
        except Exception as e:
            return {
                "success": action_result.get("success", False),
                "key_information": str(action_result),
                "next_step_suggestion": "Continue with next action",
                "confidence": 0.5,
                "observation_error": str(e)
            }
    
    async def _call_llm(self, prompt: str) -> str:
        """Call LLM with the given prompt"""
        # This is a placeholder - implement actual LLM call
        # In production, this would use your LLM client
        await asyncio.sleep(0.1)  # Simulate API call
        
        # Mock response based on prompt content
        if "task_complete" in prompt.lower():
            return json.dumps({
                "reasoning": "I have gathered sufficient information to complete the task",
                "task_complete": True,
                "final_result": "Task completed successfully with mock result"
            })
        else:
            return json.dumps({
                "reasoning": "I need to search for information about the task",
                "action": {"tool": "web_search", "parameters": {"query": "sample query"}},
                "task_complete": False
            })
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get agent performance metrics"""
        total_tool_uses = len(self.memory.tool_usage_history)
        successful_tool_uses = sum(1 for use in self.memory.tool_usage_history if use.get("success"))
        
        return {
            "total_tool_uses": total_tool_uses,
            "successful_tool_uses": successful_tool_uses,
            "tool_success_rate": successful_tool_uses / max(total_tool_uses, 1),
            "working_memory_size": len(self.memory.working_memory),
            "current_state": self.state.value
        }

# Example usage
async def demo_agent_architecture():
    # Create tools
    tools = [WebSearchTool(), FileReadTool()]
    
    # Create agent
    agent = BaseAgent("demo_agent", None, tools)  # LLM client would be passed here
    
    # Execute task
    result = await agent.execute_task(
        "Research the latest developments in AI safety and summarize key findings",
        context={"urgency": "high", "audience": "technical"}
    )
    
    print("Agent execution result:")
    print(f"Task ID: {result.get('task_id')}")
    print(f"Result: {result.get('result', result.get('error'))}")
    print(f"Iterations: {result.get('iterations', 0)}")
    
    # Get performance metrics
    metrics = agent.get_performance_metrics()
    print(f"Performance: {metrics}")

# asyncio.run(demo_agent_architecture())

4. DevOps for AI Systems (MLOps): Designing for Production Readiness

Robust software design for AI must integrate MLOps practices from the outset, ensuring systems are production-ready, maintainable, and scalable.

Comprehensive Monitoring and Observability

AI systems require specialized monitoring that goes beyond traditional application metrics:

5. The Model Context Protocol (MCP): Standardizing Integration

The adoption of standardized protocols like MCP represents a fundamental shift in AI system design, enabling "build once, integrate everywhere" approaches that dramatically reduce integration complexity.

MCP Design Benefits

Design Principles for AI-First Software

Successful AI system design requires adherence to specific principles that account for the unique characteristics of intelligent systems:

The AI Design Manifesto

Implementation Strategy

Successfully implementing AI-first design requires a phased approach:

Future of AI-Centric Software Design

As AI becomes more sophisticated and prevalent, software design must anticipate and adapt to emerging trends:

Emerging Design Considerations

• Neuro-Symbolic Integration: Combining neural networks with symbolic reasoning systems
• Federated AI Systems: Distributed intelligence across multiple organizations and environments
• Quantum-Classical Hybrid: Preparing for quantum-enhanced AI capabilities
• Biological-Digital Interface: Designing for brain-computer interface integration
• Autonomous System Governance: Self-regulating AI systems with built-in ethical frameworks

Conclusion

Software design in the age of AI represents a fundamental paradigm shift from building applications to architecting intelligence itself. Success requires embracing new patterns, protocols, and principles while maintaining focus on reliability, scalability, and maintainability.

The key is recognizing that AI is not just another feature to add to existing systems—it's a foundational capability that requires rethinking architecture, data flow, user interaction, and system behavior from the ground up.

Organizations that master AI-centric design principles will create systems that don't just perform tasks, but truly understand, reason, and collaborate with humans in meaningful ways. The future belongs to software that is designed for intelligence from day one.