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Agentic AI Sample Projects Portfolio

Explore real-world Agentic AI project blueprints with code samples, system architecture, and deployment insights. Ideal for startups, enterprises, and academic use cases.



Project 1: Autonomous Research Assistant


Overview

A multi-agent system that conducts comprehensive research on any topic by automatically searching, analyzing, and synthesizing information from multiple sources.


Key Features



# Core Capabilities
- Automatic query decomposition and refinement
- Multi-source web scraping and API integration
- Fact verification across sources
- Automatic citation generation
- Research report creation with executive summaries

Technical Stack

  • Framework: LangChain + CrewAI

  • LLM: GPT-4 / Claude API

  • Tools: SerperAPI, ArXiv API, PubMed API, Wikipedia API

  • Memory: Pinecone Vector Database

  • Deployment: Docker + FastAPI



Sample Code Structure



class ResearchAgent:
    def __init__(self):
        self.planner = PlanningAgent()
        self.searcher = SearchAgent()
        self.analyzer = AnalysisAgent()
        self.writer = WritingAgent()
        self.critic = QualityControlAgent()
    
    async def conduct_research(self, topic: str, depth: str = "comprehensive"):
        # 1. Decompose research question
        research_plan = await self.planner.create_plan(topic)
        
        # 2. Gather information
        sources = await self.searcher.find_sources(research_plan)
        
        # 3. Analyze and synthesize
        insights = await self.analyzer.extract_insights(sources)
        
        # 4. Generate report
        report = await self.writer.create_report(insights)
        
        # 5. Quality check and refine
        final_report = await self.critic.review_and_improve(report)
        
        return final_report


Output Example

  • 📊 Structured research report with sections

  • 🔍 Source credibility scores

  • 📈 Data visualizations

  • 🎯 Key findings and recommendations

  • 📚 Full bibliography with citations




Project 2: Multi-Agent Customer Support System


Overview

An intelligent customer service system with specialized agents for different query types, automatic escalation, and sentiment-based routing.


Architecture


graph TD
    A[Customer Query] --> B[Router Agent]
    B --> C[Technical Support Agent]
    B --> D[Billing Agent]
    B --> E[Product Info Agent]
    C --> F[Escalation Agent]
    D --> F
    E --> F
    F --> G[Human Supervisor]


Implementation Highlights



class CustomerSupportSystem:
    def __init__(self):
        self.agents = {
            'router': RouterAgent(),
            'technical': TechnicalSupportAgent(),
            'billing': BillingAgent(),
            'product': ProductInfoAgent(),
            'escalation': EscalationAgent()
        }
        self.memory = ConversationMemory()
        self.sentiment_analyzer = SentimentAnalyzer()
    
    async def handle_query(self, query: str, customer_id: str):
        # Load customer history
        context = self.memory.get_context(customer_id)
        
        # Analyze sentiment
        sentiment = self.sentiment_analyzer.analyze(query)
        
        # Route to appropriate agent
        assigned_agent = self.agents['router'].route(query, sentiment)
        
        # Generate response
        response = await assigned_agent.respond(query, context)
        
        # Check if escalation needed
        if response.needs_escalation:
            response = await self.agents['escalation'].handle(
                query, response, context
            )
        
        # Save interaction
        self.memory.save_interaction(customer_id, query, response)
        
        return response


Features

  • 🎯 Intent classification and routing

  • 💬 Context-aware responses

  • 😊 Sentiment analysis and empathy

  • 📈 Automatic escalation logic

  • 🔄 Continuous learning from feedback

  • 📊 Performance analytics dashboard




Project 3: AI-Powered Project Manager


Overview

An autonomous agent that manages software development projects by creating tasks, assigning priorities, tracking progress, and coordinating team activities.


Core Components




class ProjectManagerAgent:
    """
    Autonomous project management system with multiple specialized sub-agents
    """
    
    def __init__(self):
        self.task_planner = TaskPlanningAgent()
        self.resource_allocator = ResourceAllocationAgent()
        self.progress_tracker = ProgressTrackingAgent()
        self.risk_analyzer = RiskAnalysisAgent()
        self.communicator = TeamCommunicationAgent()
        
    async def manage_project(self, project_requirements: dict):
        # 1. Break down project into tasks
        tasks = await self.task_planner.decompose_project(project_requirements)
        
        # 2. Estimate effort and dependencies
        task_graph = await self.task_planner.create_dependency_graph(tasks)
        
        # 3. Allocate resources
        assignments = await self.resource_allocator.assign_tasks(
            task_graph, 
            self.get_team_availability()
        )
        
        # 4. Create timeline
        schedule = await self.create_gantt_chart(assignments)
        
        # 5. Monitor and adjust
        await self.continuous_monitoring(schedule)

Deliverables

  • 📋 Automated task breakdown (WBS)

  • 👥 Intelligent resource allocation

  • 📅 Dynamic Gantt charts

  • 🚨 Risk assessment reports

  • 📧 Automated status updates

  • 🔄 Sprint planning assistance




Project 4: Code Review & Refactoring Agent


Overview

An intelligent code analysis system that reviews code, suggests improvements, identifies bugs, and can automatically refactor code following best practices.


Sample Implementation




class CodeReviewAgent:
    def __init__(self):
        self.static_analyzer = StaticCodeAnalyzer()
        self.pattern_detector = PatternDetector()
        self.refactorer = RefactoringAgent()
        self.test_generator = TestGeneratorAgent()
        
    async def review_code(self, code: str, language: str):
        review_result = {
            'issues': [],
            'suggestions': [],
            'refactored_code': None,
            'test_cases': []
        }
        
        # 1. Static analysis
        issues = self.static_analyzer.find_issues(code, language)
        review_result['issues'] = issues
        
        # 2. Pattern detection (code smells, anti-patterns)
        patterns = self.pattern_detector.detect(code)
        
        # 3. Generate improvement suggestions
        suggestions = await self.generate_suggestions(patterns)
        review_result['suggestions'] = suggestions
        
        # 4. Auto-refactor if requested
        if self.should_refactor(issues, patterns):
            refactored = await self.refactorer.refactor(code, suggestions)
            review_result['refactored_code'] = refactored
        
        # 5. Generate test cases
        tests = await self.test_generator.create_tests(code)
        review_result['test_cases'] = tests
        
        return review_result

Features

  • 🐛 Bug detection and security vulnerability scanning

  • 🎨 Code style and formatting suggestions

  • ♻️ Automatic refactoring capabilities

  • 🧪 Test case generation

  • 📊 Code quality metrics

  • 💡 Best practice recommendations




Project 5: Financial Analysis & Trading Agent


Overview

An autonomous trading system that analyzes market data, news sentiment, and technical indicators to make informed trading decisions.


Architecture




class TradingAgent:
    def __init__(self, config: TradingConfig):
        self.market_analyzer = MarketAnalysisAgent()
        self.news_monitor = NewssentimentAgent()
        self.technical_analyst = TechnicalAnalysisAgent()
        self.risk_manager = RiskManagementAgent()
        self.executor = TradeExecutionAgent()
        self.portfolio_manager = PortfolioAgent()
        
    async def analyze_and_trade(self):
        while self.is_market_open():
            # 1. Gather market data
            market_data = await self.market_analyzer.get_current_data()
            
            # 2. Analyze news sentiment
            news_sentiment = await self.news_monitor.analyze_latest()
            
            # 3. Technical analysis
            signals = await self.technical_analyst.generate_signals(market_data)
            
            # 4. Risk assessment
            risk_score = self.risk_manager.evaluate_position(
                signals, 
                self.portfolio_manager.get_current_positions()
            )
            
            # 5. Make decision
            decision = await self.make_trading_decision(
                signals, news_sentiment, risk_score
            )
            
            # 6. Execute if appropriate
            if decision.should_trade:
                await self.executor.place_order(decision)

Capabilities

  • 📈 Real-time market data analysis

  • 📰 News sentiment analysis

  • 📊 Technical indicator calculation

  • ⚖️ Risk management and position sizing

  • 🔄 Automated order execution

  • 📑 Performance reporting





🔍 Project 6: Intelligent Document Processing Pipeline


Overview

An agent system that automatically processes various document types, extracts information, validates data, and populates databases or generates reports.


Implementation




class DocumentProcessingPipeline:
    def __init__(self):
        self.classifier = DocumentClassifier()
        self.ocr_agent = OCRAgent()
        self.extractor = InformationExtractor()
        self.validator = DataValidator()
        self.transformer = DataTransformer()
        self.storage = StorageAgent()
        
    async def process_document(self, document_path: str):
        # 1. Classify document type
        doc_type = await self.classifier.classify(document_path)
        
        # 2. Extract text (OCR if needed)
        raw_text = await self.ocr_agent.extract_text(document_path)
        
        # 3. Extract structured information
        extracted_data = await self.extractor.extract(
            raw_text, 
            doc_type,
            self.get_extraction_schema(doc_type)
        )
        
        # 4. Validate extracted data
        validated_data = await self.validator.validate(
            extracted_data,
            self.get_validation_rules(doc_type)
        )
        
        # 5. Transform to target format
        transformed_data = self.transformer.transform(
            validated_data,
            target_format='database'
        )
        
        # 6. Store or output
        result = await self.storage.save(transformed_data)
        
        return {
            'status': 'success',
            'document_type': doc_type,
            'extracted_fields': len(extracted_data),
            'storage_location': result.location
        }

Features

  • 📄 Multi-format support (PDF, Images, Word, Excel)

  • 🔤 OCR with error correction

  • 🎯 Named entity recognition

  • ✅ Data validation and quality checks

  • 🔄 Format transformation

  • 💾 Automatic database population




🎮 Project 7: Game-Playing AI Agent (Chess/Go/Poker)


Overview

An advanced game-playing agent using Monte Carlo Tree Search, neural networks, and reinforcement learning.


Core Algorithm




class GamePlayingAgent:
    def __init__(self, game_type: str):
        self.game = GameEnvironment(game_type)
        self.mcts = MonteCarloTreeSearch()
        self.evaluator = NeuralNetworkEvaluator()
        self.opening_book = OpeningDatabase()
        self.endgame_tables = EndgameTablebase()
        
    async def make_move(self, game_state: GameState):
        # 1. Check opening book
        if self.is_opening(game_state):
            return self.opening_book.get_move(game_state)
        
        # 2. Check endgame database
        if self.is_endgame(game_state):
            return self.endgame_tables.get_best_move(game_state)
        
        # 3. Use MCTS with neural network evaluation
        root = MCTSNode(game_state)
        
        for _ in range(self.num_simulations):
            node = root
            
            # Selection
            while node.is_fully_expanded() and not node.is_terminal():
                node = node.select_child(self.uct_constant)
            
            # Expansion
            if not node.is_terminal():
                node = node.expand()
            
            # Evaluation
            value = await self.evaluator.evaluate(node.state)
            
            # Backpropagation
            node.backpropagate(value)
        
        return root.get_best_move()




🏥 Project 8: Medical Diagnosis Assistant


Overview

An AI system that assists in medical diagnosis by analyzing symptoms, medical history, and test results.


Implementation




class MedicalDiagnosisAgent:
    def __init__(self):
        self.symptom_analyzer = SymptomAnalyzer()
        self.medical_kb = MedicalKnowledgeBase()
        self.differential_diagnosis = DifferentialDiagnosisEngine()
        self.test_recommender = TestRecommendationAgent()
        self.risk_assessor = RiskAssessmentAgent()
        
    async def diagnose(self, patient_data: dict):
        # 1. Analyze symptoms
        symptoms = self.symptom_analyzer.parse(patient_data['symptoms'])
        
        # 2. Generate differential diagnosis
        possible_conditions = await self.differential_diagnosis.generate(
            symptoms,
            patient_data['history'],
            patient_data['demographics']
        )
        
        # 3. Rank by probability
        ranked_conditions = self.rank_conditions(possible_conditions)
        
        # 4. Recommend tests
        recommended_tests = await self.test_recommender.suggest(
            ranked_conditions,
            patient_data
        )
        
        # 5. Risk assessment
        risk_factors = self.risk_assessor.evaluate(patient_data)
        
        return {
            'differential_diagnosis': ranked_conditions[:5],
            'recommended_tests': recommended_tests,
            'risk_factors': risk_factors,
            'disclaimer': 'For educational purposes only. Consult healthcare provider.'
        }



🚗 Project 9: Autonomous Vehicle Decision System


Overview

A multi-agent system for autonomous vehicle decision-making, including perception, planning, and control.


Architecture




class AutonomousVehicleAgent:
    def __init__(self):
        self.perception = PerceptionAgent()
        self.localization = LocalizationAgent()
        self.path_planner = PathPlanningAgent()
        self.behavior_planner = BehaviorPlanningAgent()
        self.controller = VehicleControlAgent()
        self.safety_monitor = SafetyMonitorAgent()
        
    async def drive(self, sensor_data: SensorData):
        # 1. Perceive environment
        scene = await self.perception.process(sensor_data)
        
        # 2. Localize vehicle
        position = self.localization.update(sensor_data.gps, scene)
        
        # 3. Plan behavior (lane change, stop, etc.)
        behavior = await self.behavior_planner.decide(scene, position)
        
        # 4. Plan path
        trajectory = await self.path_planner.plan(
            position,
            behavior.target,
            scene.obstacles
        )
        
        # 5. Generate control commands
        controls = self.controller.compute(trajectory, position)
        
        # 6. Safety check
        safe_controls = self.safety_monitor.verify(controls, scene)
        
        return safe_controls



📝 Project 10: Content Creation & SEO Optimization Agent


Overview

An AI system that generates high-quality content and optimizes it for search engines.


Implementation



class ContentCreationAgent:
    def __init__(self):
        self.topic_researcher = TopicResearchAgent()
        self.content_planner = ContentPlanningAgent()
        self.writer = WritingAgent()
        self.seo_optimizer = SEOOptimizationAgent()
        self.image_generator = ImageGenerationAgent()
        self.fact_checker = FactCheckingAgent()
        
    async def create_article(self, topic: str, keywords: list):
        # 1. Research topic
        research = await self.topic_researcher.research(topic)
        
        # 2. Plan content structure
        outline = await self.content_planner.create_outline(
            topic,
            research,
            target_word_count=2000
        )
        
        # 3. Generate content
        draft = await self.writer.write(outline, research)
        
        # 4. SEO optimization
        optimized = await self.seo_optimizer.optimize(
            draft,
            keywords,
            {
                'keyword_density': 0.02,
                'readability_score': 60,
                'meta_description': True,
                'headers': True
            }
        )
        
        # 5. Fact checking
        verified = await self.fact_checker.verify(optimized)
        
        # 6. Generate images
        images = await self.image_generator.create_images(
            optimized,
            style='blog_post'
        )
        
        return {
            'content': verified,
            'images': images,
            'seo_score': self.seo_optimizer.score(verified),
            'readability': self.calculate_readability(verified)
        }





How Codersarts Can Help You Build These Projects


Our Expertise Includes:

  • Complete Implementation - Full code with documentation

  • Custom Modifications - Adapt projects to your requirements

  • Deployment Support - Help with hosting and scaling

  • Testing & Debugging - Comprehensive test suites

  • Performance Optimization - Make agents faster and efficient

  • Integration Assistance - Connect with existing systems



Technology Stack We Cover:

  • Frameworks: LangChain, AutoGPT, CrewAI, AutoGen, BabyAGI

  • LLMs: OpenAI GPT-4, Claude, Gemini, Llama, Mistral

  • Vector DBs: Pinecone, Weaviate, Chroma, Qdrant

  • Deployment: Docker, Kubernetes, AWS, GCP, Azure

  • APIs: REST, GraphQL, WebSocket, gRPC



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These sample projects demonstrate the breadth and depth of Agentic AI applications. Each can be customized for academic assignments or production deployments. Contact Codersarts for implementation support!

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