feat(phase8): implement Report Agent for structured scientific report generation

- Introduced `ReportAgent` to generate comprehensive research reports from evidence and hypotheses.
- Added `ResearchReport` model with sections for executive summary, methodology, findings, limitations, and references.
- Updated MagenticOrchestrator to include ReportAgent as the final synthesis step in the workflow.
- Implemented report generation flow and markdown rendering for output.
- Added unit tests to ensure functionality of ReportAgent and report generation process.

docs/implementation/08_phase_report.md

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+# Phase 8 Implementation Spec: Report Agent
+
+**Goal**: Generate structured scientific reports with proper citations and methodology.
+**Philosophy**: "Research isn't complete until it's communicated clearly."
+**Prerequisite**: Phase 7 complete (Hypothesis Agent working)
+
+---
+
+## 1. Why Report Agent?
+
+Current limitation: **Synthesis is basic markdown, not a scientific report.**
+
+Current output:
+```
+## Drug Repurposing Analysis
+### Drug Candidates
+- Metformin
+### Key Findings
+- Some findings
+### Citations
+1. [Paper 1](url)
+```
+
+With Report Agent:
+```
+## Executive Summary
+One-paragraph summary for busy readers...
+
+## Research Question
+Clear statement of what was investigated...
+
+## Methodology
+- Sources searched: PubMed, DuckDuckGo
+- Date range: ...
+- Inclusion criteria: ...
+
+## Hypotheses Tested
+1. Metformin → AMPK → neuroprotection (Supported: 7 papers, Contradicted: 2)
+
+## Findings
+### Mechanistic Evidence
+...
+### Clinical Evidence
+...
+
+## Limitations
+- Only English language papers
+- Abstract-level analysis only
+
+## Conclusion
+...
+
+## References
+Properly formatted citations...
+```
+
+---
+
+## 2. Architecture
+
+### Phase 8 Addition
+```
+Evidence + Hypotheses + Assessment
+            ↓
+      Report Agent
+            ↓
+   Structured Scientific Report
+```
+
+### Report Generation Flow
+```
+1. JudgeAgent says "synthesize"
+2. Magentic Manager selects ReportAgent
+3. ReportAgent gathers:
+   - All evidence from shared context
+   - All hypotheses (supported/contradicted)
+   - Assessment scores
+4. ReportAgent generates structured report
+5. Final output to user
+```
+
+---
+
+## 3. Report Model
+
+### 3.1 Data Model (`src/utils/models.py`)
+
+```python
+class ReportSection(BaseModel):
+    """A section of the research report."""
+    title: str
+    content: str
+    citations: list[str] = Field(default_factory=list)
+
+
+class ResearchReport(BaseModel):
+    """Structured scientific report."""
+
+    title: str = Field(description="Report title")
+    executive_summary: str = Field(
+        description="One-paragraph summary for quick reading",
+        min_length=100,
+        max_length=500
+    )
+    research_question: str = Field(description="Clear statement of what was investigated")
+
+    methodology: ReportSection = Field(description="How the research was conducted")
+    hypotheses_tested: list[dict] = Field(
+        description="Hypotheses with supporting/contradicting evidence counts"
+    )
+
+    mechanistic_findings: ReportSection = Field(
+        description="Findings about drug mechanisms"
+    )
+    clinical_findings: ReportSection = Field(
+        description="Findings from clinical/preclinical studies"
+    )
+
+    drug_candidates: list[str] = Field(description="Identified drug candidates")
+    limitations: list[str] = Field(description="Study limitations")
+    conclusion: str = Field(description="Overall conclusion")
+
+    references: list[dict] = Field(
+        description="Formatted references with title, authors, source, URL"
+    )
+
+    # Metadata
+    sources_searched: list[str] = Field(default_factory=list)
+    total_papers_reviewed: int = 0
+    search_iterations: int = 0
+    confidence_score: float = Field(ge=0, le=1)
+
+    def to_markdown(self) -> str:
+        """Render report as markdown."""
+        sections = [
+            f"# {self.title}\n",
+            f"## Executive Summary\n{self.executive_summary}\n",
+            f"## Research Question\n{self.research_question}\n",
+            f"## Methodology\n{self.methodology.content}\n",
+        ]
+
+        # Hypotheses
+        sections.append("## Hypotheses Tested\n")
+        for h in self.hypotheses_tested:
+            status = "✅ Supported" if h.get("supported", 0) > h.get("contradicted", 0) else "⚠️ Mixed"
+            sections.append(
+                f"- **{h['mechanism']}** ({status}): "
+                f"{h.get('supported', 0)} supporting, {h.get('contradicted', 0)} contradicting\n"
+            )
+
+        # Findings
+        sections.append(f"## Mechanistic Findings\n{self.mechanistic_findings.content}\n")
+        sections.append(f"## Clinical Findings\n{self.clinical_findings.content}\n")
+
+        # Drug candidates
+        sections.append("## Drug Candidates\n")
+        for drug in self.drug_candidates:
+            sections.append(f"- **{drug}**\n")
+
+        # Limitations
+        sections.append("## Limitations\n")
+        for lim in self.limitations:
+            sections.append(f"- {lim}\n")
+
+        # Conclusion
+        sections.append(f"## Conclusion\n{self.conclusion}\n")
+
+        # References
+        sections.append("## References\n")
+        for i, ref in enumerate(self.references, 1):
+            sections.append(
+                f"{i}. {ref.get('authors', 'Unknown')}. "
+                f"*{ref.get('title', 'Untitled')}*. "
+                f"{ref.get('source', '')} ({ref.get('date', '')}). "
+                f"[Link]({ref.get('url', '#')})\n"
+            )
+
+        # Metadata footer
+        sections.append("\n---\n")
+        sections.append(
+            f"*Report generated from {self.total_papers_reviewed} papers "
+            f"across {self.search_iterations} search iterations. "
+            f"Confidence: {self.confidence_score:.0%}*"
+        )
+
+        return "\n".join(sections)
+```
+
+---
+
+## 4. Implementation
+
+### 4.1 Report Prompts (`src/prompts/report.py`)
+
+```python
+"""Prompts for Report Agent."""
+
+SYSTEM_PROMPT = """You are a scientific writer specializing in drug repurposing research reports.
+
+Your role is to synthesize evidence and hypotheses into a clear, structured report.
+
+A good report:
+1. Has a clear EXECUTIVE SUMMARY (one paragraph, key takeaways)
+2. States the RESEARCH QUESTION clearly
+3. Describes METHODOLOGY (what was searched, how)
+4. Evaluates HYPOTHESES with evidence counts
+5. Separates MECHANISTIC and CLINICAL findings
+6. Lists specific DRUG CANDIDATES
+7. Acknowledges LIMITATIONS honestly
+8. Provides a balanced CONCLUSION
+9. Includes properly formatted REFERENCES
+
+Write in scientific but accessible language. Be specific about evidence strength."""
+
+
+def format_report_prompt(
+    query: str,
+    evidence: list,
+    hypotheses: list,
+    assessment: dict,
+    metadata: dict
+) -> str:
+    """Format prompt for report generation."""
+
+    evidence_summary = "\n".join([
+        f"- [{e.citation.title}]({e.citation.url}): {e.content[:200]}..."
+        for e in evidence[:15]
+    ])
+
+    hypotheses_summary = "\n".join([
+        f"- {h.drug} → {h.target} → {h.pathway} → {h.effect} (Confidence: {h.confidence:.0%})"
+        for h in hypotheses
+    ])
+
+    return f"""Generate a structured research report for the following query.
+
+## Original Query
+{query}
+
+## Evidence Collected ({len(evidence)} papers)
+{evidence_summary}
+
+## Hypotheses Generated
+{hypotheses_summary}
+
+## Assessment Scores
+- Mechanism Score: {assessment.get('mechanism_score', 'N/A')}/10
+- Clinical Evidence Score: {assessment.get('clinical_score', 'N/A')}/10
+- Overall Confidence: {assessment.get('confidence', 0):.0%}
+
+## Metadata
+- Sources Searched: {', '.join(metadata.get('sources', []))}
+- Search Iterations: {metadata.get('iterations', 0)}
+
+Generate a complete ResearchReport with all sections filled in."""
+```
+
+### 4.2 Report Agent (`src/agents/report_agent.py`)
+
+```python
+"""Report agent for generating structured research reports."""
+from collections.abc import AsyncIterable
+from typing import Any
+
+from agent_framework import (
+    AgentRunResponse,
+    AgentRunResponseUpdate,
+    AgentThread,
+    BaseAgent,
+    ChatMessage,
+    Role,
+)
+from pydantic_ai import Agent
+
+from src.prompts.report import SYSTEM_PROMPT, format_report_prompt
+from src.utils.config import settings
+from src.utils.models import Evidence, MechanismHypothesis, ResearchReport
+
+
+class ReportAgent(BaseAgent):
+    """Generates structured scientific reports from evidence and hypotheses."""
+
+    def __init__(
+        self,
+        evidence_store: dict[str, list[Evidence]],
+    ) -> None:
+        super().__init__(
+            name="ReportAgent",
+            description="Generates structured scientific research reports with citations",
+        )
+        self._evidence_store = evidence_store
+        self._agent = Agent(
+            model=settings.llm_provider,
+            output_type=ResearchReport,
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    async def run(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AgentRunResponse:
+        """Generate research report."""
+        query = self._extract_query(messages)
+
+        # Gather all context
+        evidence = self._evidence_store.get("current", [])
+        hypotheses = self._evidence_store.get("hypotheses", [])
+        assessment = self._evidence_store.get("last_assessment", {})
+
+        if not evidence:
+            return AgentRunResponse(
+                messages=[ChatMessage(
+                    role=Role.ASSISTANT,
+                    text="Cannot generate report: No evidence collected."
+                )],
+                response_id="report-no-evidence",
+            )
+
+        # Build metadata
+        metadata = {
+            "sources": list(set(e.citation.source for e in evidence)),
+            "iterations": self._evidence_store.get("iteration_count", 0),
+        }
+
+        # Generate report
+        prompt = format_report_prompt(
+            query=query,
+            evidence=evidence,
+            hypotheses=hypotheses,
+            assessment=assessment,
+            metadata=metadata
+        )
+
+        result = await self._agent.run(prompt)
+        report = result.output
+
+        # Store report
+        self._evidence_store["final_report"] = report
+
+        # Return markdown version
+        return AgentRunResponse(
+            messages=[ChatMessage(role=Role.ASSISTANT, text=report.to_markdown())],
+            response_id="report-complete",
+            additional_properties={"report": report.model_dump()},
+        )
+
+    def _extract_query(self, messages) -> str:
+        """Extract query from messages."""
+        if isinstance(messages, str):
+            return messages
+        elif isinstance(messages, ChatMessage):
+            return messages.text or ""
+        elif isinstance(messages, list):
+            for msg in reversed(messages):
+                if isinstance(msg, ChatMessage) and msg.role == Role.USER:
+                    return msg.text or ""
+                elif isinstance(msg, str):
+                    return msg
+        return ""
+
+    async def run_stream(
+        self,
+        messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
+        *,
+        thread: AgentThread | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[AgentRunResponseUpdate]:
+        """Streaming wrapper."""
+        result = await self.run(messages, thread=thread, **kwargs)
+        yield AgentRunResponseUpdate(
+            messages=result.messages,
+            response_id=result.response_id
+        )
+```
+
+### 4.3 Update MagenticOrchestrator
+
+Add ReportAgent as the final synthesis step:
+
+```python
+# In MagenticOrchestrator.__init__
+self._report_agent = ReportAgent(self._evidence_store)
+
+# In workflow building
+workflow = (
+    MagenticBuilder()
+    .participants(
+        searcher=search_agent,
+        hypothesizer=hypothesis_agent,
+        judge=judge_agent,
+        reporter=self._report_agent,  # NEW
+    )
+    .with_standard_manager(...)
+    .build()
+)
+
+# Update task instruction
+task = f"""Research drug repurposing opportunities for: {query}
+
+Workflow:
+1. SearchAgent: Find evidence from PubMed and web
+2. HypothesisAgent: Generate mechanistic hypotheses
+3. SearchAgent: Targeted search based on hypotheses
+4. JudgeAgent: Evaluate evidence sufficiency
+5. If sufficient → ReportAgent: Generate structured research report
+6. If not sufficient → Repeat from step 1 with refined queries
+
+The final output should be a complete research report with:
+- Executive summary
+- Methodology
+- Hypotheses tested
+- Mechanistic and clinical findings
+- Drug candidates
+- Limitations
+- Conclusion with references
+"""
+```
+
+---
+
+## 5. Directory Structure After Phase 8
+
+```
+src/
+├── agents/
+│   ├── search_agent.py
+│   ├── judge_agent.py
+│   ├── hypothesis_agent.py
+│   └── report_agent.py         # NEW
+├── prompts/
+│   ├── judge.py
+│   ├── hypothesis.py
+│   └── report.py               # NEW
+├── services/
+│   └── embeddings.py
+└── utils/
+    └── models.py               # Updated with report models
+```
+
+---
+
+## 6. Tests
+
+### 6.1 Unit Tests (`tests/unit/agents/test_report_agent.py`)
+
+```python
+"""Unit tests for ReportAgent."""
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from src.agents.report_agent import ReportAgent
+from src.utils.models import (
+    Citation, Evidence, MechanismHypothesis,
+    ResearchReport, ReportSection
+)
+
+
+@pytest.fixture
+def sample_evidence():
+    return [
+        Evidence(
+            content="Metformin activates AMPK...",
+            citation=Citation(
+                source="pubmed",
+                title="Metformin mechanisms",
+                url="https://pubmed.ncbi.nlm.nih.gov/12345/",
+                date="2023",
+                authors=["Smith J", "Jones A"]
+            )
+        )
+    ]
+
+
+@pytest.fixture
+def sample_hypotheses():
+    return [
+        MechanismHypothesis(
+            drug="Metformin",
+            target="AMPK",
+            pathway="mTOR inhibition",
+            effect="Neuroprotection",
+            confidence=0.8,
+            search_suggestions=[]
+        )
+    ]
+
+
+@pytest.fixture
+def mock_report():
+    return ResearchReport(
+        title="Drug Repurposing Analysis: Metformin for Alzheimer's",
+        executive_summary="This report analyzes metformin as a potential...",
+        research_question="Can metformin be repurposed for Alzheimer's disease?",
+        methodology=ReportSection(
+            title="Methodology",
+            content="Searched PubMed and web sources..."
+        ),
+        hypotheses_tested=[
+            {"mechanism": "Metformin → AMPK → neuroprotection", "supported": 5, "contradicted": 1}
+        ],
+        mechanistic_findings=ReportSection(
+            title="Mechanistic Findings",
+            content="Evidence suggests AMPK activation..."
+        ),
+        clinical_findings=ReportSection(
+            title="Clinical Findings",
+            content="Limited clinical data available..."
+        ),
+        drug_candidates=["Metformin"],
+        limitations=["Abstract-level analysis only"],
+        conclusion="Metformin shows promise...",
+        references=[],
+        sources_searched=["pubmed", "web"],
+        total_papers_reviewed=10,
+        search_iterations=3,
+        confidence_score=0.75
+    )
+
+
+@pytest.mark.asyncio
+async def test_report_agent_generates_report(
+    sample_evidence, sample_hypotheses, mock_report
+):
+    """ReportAgent should generate structured report."""
+    store = {
+        "current": sample_evidence,
+        "hypotheses": sample_hypotheses,
+        "last_assessment": {"mechanism_score": 8, "clinical_score": 6}
+    }
+
+    with patch("src.agents.report_agent.Agent") as MockAgent:
+        mock_result = MagicMock()
+        mock_result.output = mock_report
+        MockAgent.return_value.run = AsyncMock(return_value=mock_result)
+
+        agent = ReportAgent(store)
+        response = await agent.run("metformin alzheimer")
+
+        assert "Executive Summary" in response.messages[0].text
+        assert "Methodology" in response.messages[0].text
+        assert "References" in response.messages[0].text
+
+
+@pytest.mark.asyncio
+async def test_report_agent_no_evidence():
+    """ReportAgent should handle empty evidence gracefully."""
+    store = {"current": [], "hypotheses": []}
+    agent = ReportAgent(store)
+
+    response = await agent.run("test query")
+
+    assert "Cannot generate report" in response.messages[0].text
+```
+
+---
+
+## 7. Definition of Done
+
+Phase 8 is **COMPLETE** when:
+
+1. `ResearchReport` model implemented with all sections
+2. `ReportAgent` generates structured reports
+3. Reports include proper citations and methodology
+4. Magentic workflow uses ReportAgent for final synthesis
+5. Report renders as clean markdown
+6. All unit tests pass
+
+---
+
+## 8. Value Delivered
+
+| Before (Phase 7) | After (Phase 8) |
+|------------------|-----------------|
+| Basic synthesis | Structured scientific report |
+| Simple bullet points | Executive summary + methodology |
+| List of citations | Formatted references |
+| No methodology | Clear research process |
+| No limitations | Honest limitations section |
+
+**Sample output comparison:**
+
+Before:
+```
+## Analysis
+- Metformin might help
+- Found 5 papers
+[Link 1] [Link 2]
+```
+
+After:
+```
+# Drug Repurposing Analysis: Metformin for Alzheimer's Disease
+
+## Executive Summary
+Analysis of 15 papers suggests metformin may provide neuroprotection
+through AMPK activation. Mechanistic evidence is strong (8/10),
+while clinical evidence is moderate (6/10)...
+
+## Methodology
+Systematic search of PubMed and web sources using queries...
+
+## Hypotheses Tested
+- ✅ Metformin → AMPK → neuroprotection (7 supporting, 2 contradicting)
+
+## References
+1. Smith J, Jones A. *Metformin mechanisms*. Nature (2023). [Link](...)
+```
+
+---
+
+## 9. Complete Magentic Architecture (Phases 5-8)
+
+```
+User Query
+    ↓
+Gradio UI
+    ↓
+Magentic Manager (LLM Coordinator)
+    ├── SearchAgent ←→ PubMed + Web + VectorDB
+    ├── HypothesisAgent ←→ Mechanistic Reasoning
+    ├── JudgeAgent ←→ Evidence Assessment
+    └── ReportAgent ←→ Final Synthesis
+    ↓
+Structured Research Report
+```
+
+**This matches Mario's diagram** with the practical agents that add real value for drug repurposing research.