docs: update documentation to reflect actual agent implementation status

- Update README.md with real agent implementation status (8/17 complete)
- Create CONTRIBUTING.md with comprehensive agent implementation guide
- Add AGENT_STATUS_2025.md with detailed status matrix for all agents
- Update CHANGELOG.md with v2.1.0 release notes
- Correct agent count from vague "11 more" to specific status
- Add clear implementation priority order for pending agents
- Document standard patterns and requirements for new agent development

The documentation now accurately reflects that 8 agents are fully operational
(47% complete) with 7 partially implemented and 1 missing.

CHANGELOG.md

@@ -1,5 +1,30 @@
 # 📋 Changelog - Cidadão.AI
 
+## 🚀 v2.1.0 - Agent System Completion & Documentation Update (2025-01-16)
+
+### ✨ Major Updates
+- **🤖 Multi-Agent System Status** - 8/17 agents fully operational (47% complete)
+- **📊 Test Coverage** - Achieved ~80% test coverage across the codebase
+- **📚 Documentation Overhaul** - Updated all docs to reflect real implementation status
+
+### 🤖 Agent Implementation Status
+- **✅ Fully Operational (8)**: Abaporu, Zumbi, Anita, Tiradentes, Nanã, Senna, Machado, Dandara
+- **⚠️ Partially Implemented (7)**: José Bonifácio, Carlos Drummond, Maria Quitéria, Oscar Niemeyer, Ceuci, Obaluaiê, Lampião
+- **❌ Missing (1)**: One agent mentioned in docs but not implemented
+
+### 📖 Documentation Updates
+- **CONTRIBUTING.md** - Created comprehensive guide for agent implementation
+- **AGENT_STATUS_2025.md** - Created detailed status matrix for all 17 agents
+- **README.md** - Updated to reflect actual agent implementation status
+- **NEXT_STEPS** - Updated with realistic progress and priorities
+
+### 🎯 Clarifications
+- Corrected agent count from "11 more" to specific status for each
+- Added clear implementation priority order
+- Documented standard patterns for new agent development
+
+---
+
 ## 🚀 v2.0.0 - Major Project Organization (2024-01-XX)
 
 ### ✨ New Features

CONTRIBUTING.md

@@ -0,0 +1,278 @@
+# Contributing to Cidadão.AI
+
+Thank you for your interest in contributing to Cidadão.AI! This guide will help you understand how to contribute effectively to our multi-agent transparency analysis system.
+
+## 🤖 Agent Implementation Guide
+
+### Current Status
+- **8/17 agents fully implemented** (47%)
+- **7 agents partially implemented** (need completion)
+- **1 agent missing** (needs creation)
+
+### How to Implement a New Agent
+
+#### 1. **Choose an Agent to Implement**
+
+Priority order for pending agents:
+1. **José Bonifácio** (`bonifacio.py`) - Structure ready, implement logic
+2. **Carlos Drummond** (`drummond.py`) - Design complete, implement channels
+3. **Maria Quitéria** (`maria_quiteria.py`) - Security auditor
+4. **Oscar Niemeyer** (`niemeyer.py`) - Data visualization
+5. Others: Ceuci, Obaluaiê, Lampião
+
+#### 2. **Follow the Standard Pattern**
+
+All agents must inherit from `ReflectiveAgent`:
+
+```python
+from typing import List, Dict, Any
+from src.agents.deodoro import ReflectiveAgent, AgentMessage, AgentResponse
+
+class YourAgent(ReflectiveAgent):
+    """
+    Brief description of the agent's purpose.
+    
+    This agent specializes in [specific domain].
+    """
+    
+    def __init__(self):
+        super().__init__(
+            agent_id="your_agent_id",
+            name="Full Agent Name", 
+            description="Detailed description of capabilities",
+            capabilities=[
+                "capability_1",
+                "capability_2",
+                # List all specific capabilities
+            ]
+        )
+        
+    async def process(self, message: AgentMessage) -> AgentResponse:
+        """
+        Main processing logic for the agent.
+        
+        Args:
+            message: The agent message to process
+            
+        Returns:
+            AgentResponse with results
+        """
+        context = message.context
+        content = message.content
+        
+        # Your implementation logic here
+        results = await self._analyze_data(content)
+        
+        # Use reflection if quality is low
+        if results.get("confidence", 0) < 0.8:
+            return await self.reflect_and_retry(message, results)
+            
+        return AgentResponse(
+            agent_id=self.agent_id,
+            message_id=message.message_id,
+            content=results,
+            metadata={
+                "processing_time": time.time() - start_time,
+                "confidence": results.get("confidence")
+            }
+        )
+```
+
+#### 3. **Required Methods**
+
+Each agent must implement:
+- `__init__()`: Initialize with unique ID and capabilities
+- `process()`: Main async processing method
+- Helper methods for specific analyses
+
+#### 4. **Agent Capabilities Examples**
+
+Reference our fully implemented agents:
+
+**Anomaly Detection (Zumbi)**:
+- Statistical analysis (Z-score)
+- Spectral analysis (FFT)
+- Pattern recognition
+- Duplicate detection
+
+**Analysis (Anita)**:
+- Trend analysis
+- Behavioral patterns
+- Efficiency metrics
+- Seasonal patterns
+
+**Reporting (Tiradentes)**:
+- Multi-format generation
+- Audience adaptation
+- Risk prioritization
+- Multilingual support
+
+#### 5. **Testing Requirements**
+
+Create comprehensive tests in `tests/unit/test_agents/`:
+
+```python
+import pytest
+from src.agents.your_agent import YourAgent
+
+class TestYourAgent:
+    @pytest.fixture
+    def agent(self):
+        return YourAgent()
+        
+    @pytest.fixture
+    def sample_message(self):
+        return AgentMessage(
+            content={"data": "test"},
+            sender="test",
+            context=AgentContext(investigation_id="test-123")
+        )
+        
+    async def test_process_valid_data(self, agent, sample_message):
+        response = await agent.process(sample_message)
+        assert response.status == "success"
+        assert "results" in response.content
+        
+    async def test_handles_invalid_data(self, agent):
+        # Test error handling
+        pass
+```
+
+#### 6. **Documentation Requirements**
+
+- Add docstrings to all methods
+- Update `docs/AGENT_STATUS_2025.md` with implementation status
+- Include usage examples in docstrings
+- Document any external dependencies
+
+### Code Style Guidelines
+
+1. **Python Style**:
+   - Follow PEP 8
+   - Use type hints
+   - Black formatter (88 char line length)
+   - isort for imports
+
+2. **Async Best Practices**:
+   - Use `async`/`await` for all I/O operations
+   - Proper exception handling with `try`/`except`
+   - Timeout handling for external calls
+
+3. **Security**:
+   - Validate all inputs
+   - No hardcoded secrets
+   - Use `settings` from `src.core.config`
+
+### Commit Message Format
+
+Follow [Conventional Commits](https://www.conventionalcommits.org/):
+
+```
+feat(agents): implement José Bonifácio policy analysis agent
+
+- Add policy evaluation metrics
+- Implement SROI calculations
+- Add comprehensive test coverage
+```
+
+Types:
+- `feat`: New feature
+- `fix`: Bug fix
+- `test`: Tests only
+- `docs`: Documentation only
+- `refactor`: Code restructuring
+- `style`: Formatting changes
+- `chore`: Maintenance
+
+### Testing
+
+Run tests before submitting:
+
+```bash
+# All tests
+make test
+
+# Specific agent tests
+pytest tests/unit/test_agents/test_your_agent.py -v
+
+# With coverage
+make test-coverage
+```
+
+Minimum requirements:
+- 80% test coverage for new code
+- All tests passing
+- No linting errors
+
+### Pull Request Process
+
+1. Fork the repository
+2. Create a feature branch: `git checkout -b feat/implement-agent-name`
+3. Make your changes following the guidelines
+4. Run all checks: `make check`
+5. Commit with clear messages
+6. Push to your fork
+7. Create a Pull Request with:
+   - Clear description of changes
+   - Reference to any related issues
+   - Test results screenshot
+   - Documentation updates
+
+### Getting Help
+
+- Check existing agent implementations for examples
+- Review `src/agents/deodoro.py` for base classes
+- Ask questions in GitHub Issues
+- Tag maintainers for complex questions
+
+## 🚀 Other Contributions
+
+### Bug Fixes
+1. Create an issue describing the bug
+2. Reference the issue in your PR
+3. Include tests that verify the fix
+
+### Documentation
+- Fix typos and clarify explanations
+- Add examples and use cases
+- Translate documentation
+
+### Performance Improvements
+- Profile before optimizing
+- Benchmark improvements
+- Document performance gains
+
+## 📋 Development Setup
+
+```bash
+# Clone the repo
+git clone https://github.com/your-fork/cidadao.ai-backend
+cd cidadao.ai-backend
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # Linux/Mac
+
+# Install dev dependencies
+make install-dev
+
+# Run tests
+make test
+```
+
+## 🤝 Code of Conduct
+
+- Be respectful and inclusive
+- Welcome newcomers
+- Focus on constructive feedback
+- Celebrate Brazilian culture and diversity
+
+## 📄 License
+
+By contributing, you agree that your contributions will be licensed under the MIT License.
+
+---
+
+**Questions?** Open an issue with the `question` label.
+
+**Ready to contribute?** Check our [good first issue](https://github.com/anderson-ufrj/cidadao.ai-backend/labels/good%20first%20issue) labels!

README.md

@@ -81,15 +81,22 @@ Our comprehensive test suite ensures reliability and security:
 
 ### 🤖 **Multi-Agent System**
 
-17 specialized AI agents with Brazilian cultural identities:
-
-- **🎯 Abaporu** (Master): Investigation orchestrator
-- **🔍 Zumbi dos Palmares** (Investigator): Anomaly detection
-- **📊 Anita Garibaldi** (Analyst): Pattern analysis
-- **📝 Tiradentes** (Reporter): Natural language reports
-- **🧠 Nanã** (Memory): Knowledge management
-- **🏎️ Ayrton Senna** (Router): Intelligent routing
-- And 11 more specialized agents...
+**Status**: 8 agents fully operational, 7 partially implemented, 16/17 total
+
+#### ✅ **Fully Operational Agents**:
+- **🎯 Abaporu** (Master): Investigation orchestrator and coordinator
+- **🔍 Zumbi dos Palmares** (Investigator): Advanced anomaly detection with FFT
+- **📊 Anita Garibaldi** (Analyst): Pattern analysis and trend detection
+- **📝 Tiradentes** (Reporter): Multi-format adaptive report generation
+- **🧠 Nanã** (Memory): Episodic, semantic and conversational memory
+- **🏎️ Ayrton Senna** (Router): Semantic routing with intent detection
+- **📚 Machado de Assis** (Textual): Document analysis with NER and compliance
+- **⚖️ Dandara** (Social Justice): Equity analysis with social coefficients
+
+#### ⚠️ **In Development** (7 agents):
+- José Bonifácio (Policy Analyst), Carlos Drummond (Communication)
+- Maria Quitéria (Security), Oscar Niemeyer (Visualization)
+- Ceuci (ETL), Obaluaiê (Health), Lampião (Regional)
 
 ### 🔒 **Security Features**
 

docs/AGENT_STATUS_2025.md

@@ -0,0 +1,151 @@
+# 🤖 Status dos Agentes - Cidadão.AI Backend
+
+**Última Atualização**: Janeiro 2025  
+**Total de Agentes**: 17  
+**Status**: 8 totalmente funcionais, 9 parcialmente implementados
+
+## 📊 Matriz de Status dos Agentes
+
+| Agente | Arquivo | Status | Capacidades | Observações |
+|--------|---------|--------|-------------|-------------|
+| **Abaporu** | `abaporu.py` | ✅ Completo | Orquestração, Planejamento, Coordenação | Master Agent totalmente operacional |
+| **Zumbi dos Palmares** | `zumbi.py` | ✅ Completo | Detecção de anomalias, FFT, Análise estatística | Investigador principal |
+| **Anita Garibaldi** | `anita.py` | ✅ Completo | Análise de padrões, Tendências, Comportamento | Analista de dados |
+| **Tiradentes** | `tiradentes.py` | ✅ Completo | Geração de relatórios multi-formato | Reporter adaptativo |
+| **Ayrton Senna** | `ayrton_senna.py` | ✅ Completo | Roteamento semântico inteligente | Router de queries |
+| **Nanã** | `nana.py` | ✅ Completo | Memória episódica/semântica/conversacional | Gestão de memória |
+| **Machado de Assis** | `machado.py` | ✅ Completo | Análise textual, NER, Conformidade legal | Processamento de documentos |
+| **Dandara** | `dandara.py` | ✅ Completo | Análise de equidade, Coeficientes sociais | Justiça social |
+| **José Bonifácio** | `bonifacio.py` | ⚠️ Parcial | Framework para avaliação de políticas | Estrutura completa, lógica placeholder |
+| **Carlos Drummond** | `drummond.py` | ⚠️ Parcial | Comunicação multicanal | Estrutura OK, canais não implementados |
+| **Maria Quitéria** | `maria_quiteria.py` | ⚠️ Parcial | Auditoria de segurança | Estrutura básica apenas |
+| **Oscar Niemeyer** | `niemeyer.py` | ⚠️ Parcial | Visualização de dados | Estrutura básica apenas |
+| **Ceuci** | `ceuci.py` | ⚠️ Parcial | ETL e processamento | Estrutura básica apenas |
+| **Obaluaiê** | `obaluaie.py` | ⚠️ Parcial | Monitoramento de saúde | Estrutura básica apenas |
+| **Lampião** | `lampiao.py` | ⚠️ Parcial | Análise regional | Estrutura básica apenas |
+| **Deodoro** | `deodoro.py` | 🏗️ Base | Classes base do sistema | Não é um agente, é infraestrutura |
+| **[Faltando]** | - | ❌ Não existe | - | 1 agente mencionado nos docs não tem arquivo |
+
+## ✅ Agentes Totalmente Funcionais (8)
+
+### 1. **Abaporu (Master Agent)**
+- **Papel**: Orquestrador central
+- **Funcionalidades**:
+  - Planejamento estratégico de investigações
+  - Coordenação multi-agente
+  - Auto-reflexão e melhoria contínua
+  - Síntese de resultados
+
+### 2. **Zumbi dos Palmares (Investigator)**
+- **Papel**: Detective de anomalias
+- **Funcionalidades**:
+  - Detecção estatística (Z-score > 2.5)
+  - Análise espectral (FFT)
+  - Concentração de fornecedores
+  - Detecção de duplicatas
+
+### 3. **Anita Garibaldi (Analyst)**
+- **Papel**: Analista de padrões
+- **Funcionalidades**:
+  - Análise de tendências
+  - Comportamento organizacional
+  - Padrões sazonais
+  - Métricas de eficiência
+
+### 4. **Tiradentes (Reporter)**
+- **Papel**: Gerador de relatórios
+- **Funcionalidades**:
+  - Multi-formato (MD, HTML, PDF, JSON)
+  - Adaptação por audiência
+  - Suporte multilíngue
+  - Priorização de riscos
+
+### 5. **Ayrton Senna (Router)**
+- **Papel**: Roteador semântico
+- **Funcionalidades**:
+  - Roteamento por regras
+  - Similaridade semântica
+  - Detecção de intenção
+  - Estratégias de fallback
+
+### 6. **Nanã (Memory)**
+- **Papel**: Guardião da memória
+- **Funcionalidades**:
+  - Memória episódica
+  - Memória semântica
+  - Memória conversacional
+  - Busca vetorial
+
+### 7. **Machado de Assis (Textual)**
+- **Papel**: Analista textual
+- **Funcionalidades**:
+  - Processamento de documentos
+  - NER (Named Entity Recognition)
+  - Detecção de cláusulas suspeitas
+  - Análise de conformidade
+
+### 8. **Dandara (Social Justice)**
+- **Papel**: Guardiã da equidade
+- **Funcionalidades**:
+  - Coeficiente Gini
+  - Índices de Atkinson, Theil, Palma
+  - Detecção de violações
+  - Análise de inclusão
+
+## ⚠️ Agentes Parcialmente Implementados (7)
+
+### Necessitam Implementação Completa:
+1. **José Bonifácio** - Estrutura pronta, lógica placeholder
+2. **Carlos Drummond** - Design completo, canais não implementados
+3. **Maria Quitéria** - Apenas estrutura básica
+4. **Oscar Niemeyer** - Apenas estrutura básica
+5. **Ceuci** - Apenas estrutura básica
+6. **Obaluaiê** - Apenas estrutura básica
+7. **Lampião** - Apenas estrutura básica
+
+## ❌ Agentes Faltantes (1)
+
+Segundo a documentação original, deveria haver 17 agentes, mas só encontramos 16 arquivos (15 agentes + deodoro.py que é infraestrutura).
+
+## 🎯 Próximos Passos
+
+1. **Prioridade Alta**:
+   - Completar implementação de José Bonifácio (já tem estrutura)
+   - Finalizar Carlos Drummond (implementar canais de comunicação)
+   
+2. **Prioridade Média**:
+   - Implementar Maria Quitéria (segurança é crítica)
+   - Implementar Oscar Niemeyer (visualizações são importantes)
+   
+3. **Prioridade Baixa**:
+   - Completar Ceuci, Obaluaiê e Lampião
+   - Identificar e implementar o 17º agente faltante
+
+## 📈 Métricas de Progresso
+
+- **Agentes Completos**: 8/17 (47%)
+- **Agentes com Estrutura**: 15/17 (88%)
+- **Cobertura de Testes**: ~80% nos agentes implementados
+- **Documentação**: 100% nos agentes completos
+
+## 🔧 Padrão de Implementação
+
+Todos os agentes seguem o mesmo padrão:
+```python
+class NomeAgent(ReflectiveAgent):
+    def __init__(self):
+        super().__init__(
+            agent_id="nome",
+            name="Nome Completo",
+            description="Descrição",
+            capabilities=[...]
+        )
+    
+    async def process(self, message: AgentMessage) -> AgentResponse:
+        # Lógica principal do agente
+        pass
+```
+
+---
+
+**Nota**: Este documento reflete o estado REAL do código, não as aspirações da documentação original.