# SWARMCARE EXECUTION - Instance instance_02 - Phase 2

## AUTONOMOUS EXECUTION MODE
- TAKE FULL CONTROL: Do not ask for confirmation
- PRODUCTION-READY ONLY: Every output must be deployment-ready
- 100% SUCCESS RATE: Build comprehensive validation at every step
- PARALLEL EVERYTHING: Run all independent tasks simultaneously

## PHASE: SWARMCARE Agents

### Phase Overview
- **Phase ID:** 2
- **Story Points:** 94
- **Priority:** P0
- **Dependencies:** 0

### User Stories to Implement

#### User Story 3.1: Agent Framework Setup
**As a** System
**I want** a multi-agent framework
**So that** agents can be defined, deployed, and coordinated

**Acceptance Criteria:**
- [ ] AutoGen or CrewAI integrated
- [ ] Agent definition system from agents.yaml
- [ ] Agent lifecycle management (create, activate, deactivate)
- [ ] Inter-agent communication protocol
- [ ] Agent state persistence
- [ ] Agent monitoring dashboard

**Implementation Tasks:**
1. Choose framework (AutoGen vs CrewAI) - recommend AutoGen for flexibility
2. Install and configure AutoGen
3. Create agent definition loader (reads agents.yaml)
4. Implement agent factory pattern
5. Create agent registry (track active agents)
6. Implement inter-agent messaging (RabbitMQ or Redis pub/sub)
7. Create agent state management (PostgreSQL)
8. Build agent monitoring dashboard (React)
9. Test with 2 simple agents

**Story Points:** 21
**Priority:** P0


#### User Story 3.2: Medical Knowledge Extractor Agent
**As a** System
**I want** an agent that extracts medical knowledge from EHR data
**So that** clinical information can be structured

**Acceptance Criteria:**
- [ ] Agent implements medical_knowledge_extractor from agents.yaml
- [ ] FHIR data parsing
- [ ] SNOMED CT, LOINC, RxNorm code extraction
- [ ] Clinical relationship identification
- [ ] Integration with RAG Heat knowledge graph
- [ ] Output: structured clinical profile

**Implementation Tasks:**
1. Define agent using AutoGen
2. Implement FHIR parser
3. Create medical terminology tools (SNOMED lookup, LOINC lookup, RxNorm lookup)
4. Implement knowledge graph parser
5. Create agent prompt template
6. Integrate with Neo4j for ontology queries
7. Test with sample EHR data
8. Validate output structure

**Story Points:** 13
**Priority:** P0


#### User Story 3.3: Patient Case Synthesizer Agent
**As a** System
**I want** an agent that creates clinical case presentations
**So that** medical data can be transformed into educational content

**Acceptance Criteria:**
- [ ] Agent implements patient_case_synthesizer from agents.yaml
- [ ] Takes clinical data from Knowledge Extractor
- [ ] Generates structured case presentation
- [ ] Includes: chief complaint, history, exam, assessment, plan
- [ ] Educational format suitable for teaching
- [ ] Output: formatted case presentation

**Implementation Tasks:**
1. Define agent using AutoGen
2. Create clinical case template
3. Implement prompt engineering for case synthesis
4. Add medical guidelines tools
5. Integrate disease knowledge base
6. Test with sample patient data
7. Validate clinical accuracy with doctor
8. Refine based on feedback

**Story Points:** 13
**Priority:** P0


#### User Story 3.4: Medical Conversation Writer Agent
**As a** System
**I want** an agent that creates realistic medical dialogues
**So that** educational conversations can be generated

**Acceptance Criteria:**
- [ ] Agent implements medical_conversation_writer from agents.yaml
- [ ] Generates doctor-patient dialogues
- [ ] Generates doctor-doctor consultations
- [ ] Natural conversation flow
- [ ] Medically accurate
- [ ] Output: structured dialogue script

**Implementation Tasks:**
1. Define agent using AutoGen
2. Load conversation templates
3. Implement dialogue generation prompts
4. Add conversation structure (opening, discussion, closing)
5. Integrate with case synthesizer output
6. Test with multiple scenarios
7. Validate for naturalness and accuracy
8. Refine prompts based on testing

**Story Points:** 13
**Priority:** P0


#### User Story 3.5: Compliance Validator Agent
**As a** System
**I want** an agent that validates HIPAA compliance
**So that** all generated content is legally compliant

**Acceptance Criteria:**
- [ ] Agent implements compliance_validator from agents.yaml
- [ ] HIPAA privacy rule checking
- [ ] Patient de-identification verification
- [ ] Medical disclaimer generation
- [ ] Clinical accuracy validation
- [ ] Output: compliance report + disclaimers

**Implementation Tasks:**
1. Define agent using AutoGen
2. Implement HIPAA compliance checker tools
3. Create privacy anonymization tool
4. Implement medical disclaimer generator
5. Add clinical accuracy validator
6. Test with generated content
7. Integrate with lawyer advisor for validation
8. Create compliance report template

**Story Points:** 13
**Priority:** P0


#### User Story 3.6: Podcast Script Generator Agent
**As a** System
**I want** an agent that creates podcast scripts
**So that** educational podcasts can be generated

**Acceptance Criteria:**
- [ ] Agent implements podcast_script_generator from agents.yaml
- [ ] Uses System Prompt from Documents folder
- [ ] Generates patient education scripts
- [ ] Generates professional education scripts
- [ ] Natural dialogue format
- [ ] Includes timing cues
- [ ] Output: complete podcast script

**Implementation Tasks:**
1. Define agent using AutoGen
2. Load system prompt from Documents/System Prompt for Script Generation.txt
3. Implement podcast structure tool
4. Create narrative flow generator
5. Add audio pacing considerations
6. Test with clinical cases
7. Validate script quality
8. Add timing and production notes

**Story Points:** 13
**Priority:** P1


#### User Story 3.7: Quality Assurance Reviewer Agent
**As a** System
**I want** an agent that reviews all content for quality
**So that** only high-quality content is published

**Acceptance Criteria:**
- [ ] Agent implements quality_assurance_reviewer from agents.yaml
- [ ] Clinical accuracy review
- [ ] Educational value assessment
- [ ] Professional standards check
- [ ] Content quality scoring
- [ ] Output: quality assurance report

**Implementation Tasks:**
1. Define agent using AutoGen
2. Implement clinical guideline checker
3. Create medical fact validator
4. Add content quality scorer
5. Test with generated content
6. Set quality thresholds
7. Integrate with doctor advisor for validation
8. Create QA report template

**Story Points:** 8
**Priority:** P1


### Success Criteria
- All user stories implemented
- All tests passing (>80% coverage)
- Code reviewed and optimized
- Documentation complete
- No critical security vulnerabilities

### Deliverables
- Production-ready code in Git
- Comprehensive test suite
- API documentation (if applicable)
- Integration tests passing

### Tracking
- Mark each user story complete in the phase state file
- Create checkpoint every 30 minutes
- Update progress percentage
- Log all significant events

### Next Steps After Completion
1. Run all tests
2. Generate documentation
3. Create checkpoint
4. Mark phase as COMPLETED
5. Notify integration coordinator

BEGIN EXECUTION NOW.
