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System Design for Agentic AI Projects: What to Do, What Not to Do, and When

Agentic AI, where independent AI agents make complex decisions or orchestrate workflows, is revolutionizing business processes. Yet architecting such systems brings fresh design challenges and high stakes. Here are actionable principles, informed by real-world builds and strategic lessons, for designing robust, scalable Agentic AI solutions, complete with Python pseudocode examples to illustrate implementation patterns.

What to Do: Core System Design Principles Anchor to Business Value

Start by defining explicit, measurable business outcomes for the AI system. Every agent’s function should map directly to a high-impact organizational goal. Avoid speculative builds; only add autonomy where it demonstrably amplifies value and innovation.

class BusinessAlignedAgent:
    def __init__(self, name, business_kpi, success_threshold):
        self.name = name
        self.business_kpi = business_kpi  # e.g., "customer_satisfaction_score"
        self.success_threshold = success_threshold  # e.g., 0.85
        self.metrics_tracker = MetricsTracker()

    def execute_task(self, task_input):
        result = self.process_task(task_input)
        # Always measure business impact
        kpi_value = self.metrics_tracker.measure_kpi(self.business_kpi, result)

        if kpi_value < self.success_threshold:
            self.escalate_to_human(result, kpi_value)

        return result
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Use Clear Orchestration and Task Decomposition

Architect systems with a two-tier model: a primary (“supervisor”) agent that owns orchestration and specialized subagents that execute well-bounded, stateless tasks. Decompose complex operations either vertically (sequential dependency) or horizontally (parallel independence), based on the workflow.

class AgentOrchestrator:
    def __init__(self):
        self.subagents = {
            'data_processor': DataProcessorAgent(),
            'validator': ValidatorAgent(),
            'reporter': ReporterAgent()
        }

    def execute_workflow(self, task):
        # Sequential decomposition example
        if task.type == "data_analysis":
            raw_data = self.subagents['data_processor'].process(task.input)
            validated_data = self.subagents['validator'].validate(raw_data)
            report = self.subagents['reporter'].generate_report(validated_data)
            return report

        # Parallel decomposition example
        elif task.type == "multi_source_research":
            parallel_tasks = task.split_into_parallel()
            results = []

            for subtask in parallel_tasks:
                agent = self.select_agent_for_task(subtask)
                results.append(agent.execute_async(subtask))

            return self.merge_results(results)
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Favor Statelessness and Modularity

Design subagents as pure functions, with no internal state and no reliance on conversation history. This ensures predictable, parallel execution, easy troubleshooting, reproducible outputs, and streamlined caching. Isolate and test each module rigorously before integration.

class StatelessAgent:
    def __init__(self, tools, config):
        self.tools = tools
        self.config = config
        # No conversation state stored here

    def process(self, task_input, context=None):
        """Pure function - same input always produces same output"""
        # Extract only necessary context
        relevant_context = self.extract_relevant_context(context, task_input)

        # Process without side effects
        result = self.execute_logic(task_input, relevant_context)

        # Return structured output with metadata
        return {
            'result': result,
            'confidence': self.calculate_confidence(result),
            'reasoning': self.explain_reasoning(),
            'metadata': {
                'processing_time': self.get_processing_time(),
                'tools_used': self.get_tools_used()
            }
        }
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Explicit Protocols and Structured Data

All agent-to-agent communication should use strict, structured formats: specify objective, input data, expected output, constraints, and success criteria. Responses must report status, reason codes, results, and relevant metadata.

from dataclasses import dataclass
from typing import Dict, Any, List
from enum import Enum

class TaskStatus(Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"
    ESCALATED = "escalated"

@dataclass
class AgentTask:
    objective: str
    input_data: Dict[str, Any]
    constraints: List[str]
    expected_output_format: str
    priority: int = 1
    timeout_seconds: int = 300

@dataclass
class AgentResponse:
    status: TaskStatus
    result: Dict[str, Any]
    confidence_score: float
    reasoning: str
    metadata: Dict[str, Any]
    error_details: str = None
    next_actions: List[str] = None

class CommunicationProtocol:
    def send_task(self, agent_id: str, task: AgentTask) -> str:
        """Send task to agent, return task_id"""
        pass

    def get_response(self, task_id: str) -> AgentResponse:
        """Get agent response for task"""
        pass
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Integrate Monitoring, Observability, and Human Oversight Early

From the outset, design dashboards and trace logs to capture every action, error, and decision path. Incorporate humans-in-the-loop to review high-stakes outcomes, intervene on edge cases, and absorb critical feedback for continual refinement.

import logging
from opentelemetry import trace
from datetime import datetime

class ObservableAgent:
    def __init__(self, name):
        self.name = name
        self.tracer = trace.get_tracer(__name__)
        self.logger = logging.getLogger(f"agent.{name}")
        self.metrics_collector = MetricsCollector()

    def execute_with_observability(self, task):
        # Create distributed trace
        with self.tracer.start_as_current_span(f"agent_{self.name}_execute") as span:
            span.set_attribute("agent.name", self.name)
            span.set_attribute("task.type", task.type)

            start_time = datetime.now()

            try:
                # Log task start
                self.logger.info(f"Starting task: {task.objective}")

                # Execute task with monitoring
                result = self.monitored_execute(task)

                # Record success metrics
                self.metrics_collector.record_success(
                    agent_name=self.name,
                    task_type=task.type,
                    duration=(datetime.now() - start_time).total_seconds(),
                    confidence=result.confidence_score
                )

                # Check if human oversight needed
                if self.requires_human_review(result):
                    return self.escalate_to_human(task, result)

                return result

            except Exception as e:
                # Record failure metrics
                self.metrics_collector.record_failure(
                    agent_name=self.name,
                    task_type=task.type,
                    error=str(e)
                )

                span.set_attribute("error", True)
                span.set_attribute("error.message", str(e))

                self.logger.error(f"Task failed: {e}")
                raise

    def requires_human_review(self, result):
        """Determine if result needs human oversight"""
        return (result.confidence_score < 0.7 or 
                "high_stakes" in result.metadata or
                result.status == TaskStatus.ESCALATED)
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What Not to Do: Preventable Pitfalls Do Not Over-Engineer Hierarchies

More than two levels of agent delegation often result in convoluted debugging, unclear accountability, and diminished returns. Resist deep trees of subagents; complexity compounds risk.

class OverEngineeredSystem:  # DON'T DO THIS
    def __init__(self):
        # Too many levels - hard to debug and maintain
        self.level1_orchestrator = MainOrchestrator([
            SubOrchestrator([
                SpecializedAgent([
                    MicroAgent(), MicroAgent()
                ]),
                SpecializedAgent([
                    MicroAgent(), MicroAgent()
                ])
            ])
        ])

# PREFER - Simple two-level hierarchy
class SimpleSystem:  # DO THIS INSTEAD
    def __init__(self):
        self.orchestrator = MainOrchestrator()
        self.agents = {
            'processor': ProcessorAgent(),
            'validator': ValidatorAgent(),
            'reporter': ReporterAgent()
        }
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Avoid Context Creep and Unbounded State

Do not simply pass all conversation history or business context to every agent. Instead, use the minimal, strictly relevant context needed for the immediate task.

class ContextManager:
    def __init__(self):
        self.context_store = {}
        self.context_filters = {
            'data_processor': ['data_schema', 'processing_rules'],
            'validator': ['validation_rules', 'error_thresholds'],
            'reporter': ['report_template', 'audience_type']
        }

    def get_filtered_context(self, agent_type, task):
        """Return only relevant context for specific agent"""
        full_context = self.context_store.get(task.session_id, {})

        # Filter to only relevant keys for this agent
        relevant_keys = self.context_filters.get(agent_type, [])
        filtered_context = {k: v for k, v in full_context.items() 
                          if k in relevant_keys}

        # Add task-specific context
        filtered_context.update(task.get_immediate_context())

        return filtered_context
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When to Do What: Phased Execution Discovery & Design

Interview stakeholders, clarify business KPIs, and identify the smallest, highest-value opportunity for autonomy. Map out explicit agent roles and approval boundaries.

class ProjectDiscovery:
    def __init__(self):
        self.stakeholders = []
        self.business_requirements = {}
        self.agent_specifications = {}

    def conduct_stakeholder_interviews(self):
        for stakeholder in self.stakeholders:
            requirements = self.interview(stakeholder)
            self.business_requirements[stakeholder.role] = requirements

    def identify_automation_opportunities(self):
        opportunities = []

        for process in self.business_requirements['processes']:
            if (process.is_repetitive() and 
                process.has_clear_success_criteria() and
                process.risk_level < 'high'):
                opportunities.append(process)

        # Prioritize by value and feasibility
        return sorted(opportunities, 
                     key=lambda x: x.business_value / x.complexity_score)
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Prototype & Evaluate

Build a minimal orchestration layer and 1–3 specialized subagents. Rigorously test each in isolation and as an integrated chain. Validate statelessness and boundary clarity with sample workflows.

class PrototypeValidator:
    def __init__(self, agents, test_cases):
        self.agents = agents
        self.test_cases = test_cases
        self.test_results = {}

    def run_isolation_tests(self):
        """Test each agent individually"""
        for agent_name, agent in self.agents.items():
            for test_case in self.test_cases[agent_name]:
                result = self.test_agent_isolation(agent, test_case)
                self.test_results[f"{agent_name}_isolation"] = result

    def run_integration_tests(self):
        """Test agent interactions and workflows"""
        for workflow in self.test_cases['workflows']:
            result = self.test_workflow_integration(workflow)
            self.test_results[f"workflow_{workflow.name}"] = result

    def validate_statelessness(self, agent):
        """Ensure same input produces same output"""
        test_input = self.generate_test_input()

        # Run same input multiple times
        results = [agent.process(test_input) for _ in range(3)]

        # All results should be identical (stateless)
        return all(r == results[0] for r in results)
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Incremental Extension

Expand agent count, memory, and privileges only after robust monitoring and human-in-the-loop review demonstrate sustained, safe performance. Each new extension must undergo regression and error-handling tests.

class SafeExpansionManager:
    def __init__(self, current_system):
        self.current_system = current_system
        self.safety_metrics = SafetyMetrics()
        self.approval_gate = ApprovalGate()

    def propose_expansion(self, new_capability):
        # Check current system health
        if not self.safety_metrics.is_system_healthy():
            return False, "Current system showing issues"

        # Run safety analysis
        risk_assessment = self.analyze_expansion_risk(new_capability)

        if risk_assessment.risk_level > 'medium':
            return False, f"Risk too high: {risk_assessment.details}"

        # Require human approval for expansion
        approval = self.approval_gate.request_approval(
            expansion=new_capability,
            risk_assessment=risk_assessment,
            current_performance=self.safety_metrics.get_performance_summary()
        )

        return approval, "Ready for controlled rollout"

    def controlled_rollout(self, new_capability):
        """Gradual deployment with monitoring"""
        # Start with 5% of traffic
        self.deploy_to_percentage(new_capability, 5)

        # Monitor for 24 hours
        if self.monitor_for_duration(hours=24):
            # Increase to 25%
            self.deploy_to_percentage(new_capability, 25)
            # Continue gradual expansion...
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What to Do When: Handling Change and Failure On System Drift or Error

Instantly trigger rollback, escalate to human review, and only proceed to retrain agents with the learnings post-mortem. Never ignore recurring anomalies, investigate, audit, and patch.

class ErrorRecoverySystem:
    def __init__(self):
        self.error_detector = ErrorDetector()
        self.rollback_manager = RollbackManager()
        self.human_escalation = HumanEscalationSystem()
        self.retry_policy = RetryPolicy()

    def handle_system_error(self, error, context):
        # Immediate containment
        if error.severity == 'critical':
            self.rollback_manager.immediate_rollback()
            self.human_escalation.emergency_alert(error, context)

        # Retry with exponential backoff for transient errors
        elif error.is_transient():
            return self.retry_with_backoff(error.failed_operation, context)

        # Pattern analysis for recurring errors
        elif self.error_detector.is_recurring_pattern(error):
            self.escalate_for_investigation(error)

        # Standard error recovery
        else:
            return self.standard_recovery(error, context)

    def retry_with_backoff(self, operation, context, max_retries=3):
        """Implement exponential backoff retry pattern"""
        for attempt in range(max_retries):
            try:
                return operation.execute(context)
            except Exception as e:
                if attempt == max_retries - 1:
                    # Final attempt failed
                    self.human_escalation.notify(e, context)
                    raise

                # Exponential backoff: 2^attempt seconds
                wait_time = 2 ** attempt
                time.sleep(wait_time)

    def post_mortem_analysis(self, error_incident):
        """Analyze failures and improve system"""
        analysis = {
            'root_cause': self.analyze_root_cause(error_incident),
            'prevention_measures': self.identify_prevention_measures(error_incident),
            'system_improvements': self.suggest_improvements(error_incident)
        }

        # Update agent training with lessons learned
        self.update_agent_training(analysis)
        return analysis
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Amidst New Constraints

When regulations or requirements shift, update agent privileges, audit trails, and test compliance before resuming autonomous actions.

class ComplianceManager:
    def __init__(self):
        self.regulation_tracker = RegulationTracker()
        self.audit_system = AuditSystem()
        self.constraint_engine = ConstraintEngine()

    def handle_regulatory_change(self, new_regulation):
        # Immediate system pause for critical changes
        if new_regulation.impact_level == 'critical':
            self.pause_autonomous_operations()

        # Update constraint engine
        new_constraints = self.regulation_tracker.translate_to_constraints(new_regulation)
        self.constraint_engine.update_constraints(new_constraints)

        # Re-validate all agents against new constraints
        validation_results = self.validate_agents_compliance()

        if all(result.compliant for result in validation_results):
            self.resume_operations_with_new_constraints()
        else:
            self.remediate_non_compliant_agents(validation_results)

    def continuous_compliance_monitoring(self):
        """Ongoing compliance checking"""
        while self.system_is_running():
            for agent in self.active_agents():
                compliance_check = self.audit_system.check_compliance(
                    agent, self.constraint_engine.current_constraints
                )

                if not compliance_check.passed:
                    self.handle_compliance_violation(agent, compliance_check)

            time.sleep(self.compliance_check_interval)
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For Coverage or Performance Gaps

Analyze dashboard metrics and user feedback, then upgrade agent skills or add specialized nodes incrementally, not en masse. Each new addition should prove its value through KPIs before full deployment.

class PerformanceOptimizer:
    def __init__(self):
        self.metrics_analyzer = MetricsAnalyzer()
        self.feedback_processor = FeedbackProcessor()
        self.improvement_engine = ImprovementEngine()

    def identify_performance_gaps(self):
        # Analyze quantitative metrics
        performance_data = self.metrics_analyzer.get_performance_summary()
        gaps = []

        for metric, value in performance_data.items():
            if value < self.get_threshold(metric):
                gaps.append(PerformanceGap(metric, value, self.get_threshold(metric)))

        # Analyze qualitative feedback
        user_feedback = self.feedback_processor.analyze_recent_feedback()
        gaps.extend(self.feedback_processor.identify_capability_gaps(user_feedback))

        return sorted(gaps, key=lambda x: x.business_impact, reverse=True)

    def implement_targeted_improvements(self, performance_gaps):
        for gap in performance_gaps[:3]:  # Focus on top 3 gaps
            improvement = self.improvement_engine.design_improvement(gap)

            # A/B test the improvement
            test_result = self.run_ab_test(improvement)

            if test_result.shows_improvement():
                self.deploy_improvement(improvement)
                self.monitor_improvement_impact(improvement)
            else:
                self.log_failed_improvement_attempt(improvement, test_result)
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Conclusion

Well-designed Agentic AI solutions are anchored by clear intent, strong modular boundaries, real-time observability, and phased expansion under active human guidance. The code examples above illustrate how these principles translate into practical implementation patterns that you can adapt for your specific use cases. Adhering to these principles avoids costly missteps and speeds the path from breakthrough to sustainable business impact.

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