Results Reporting System Documentation

Table of Contents

1. Overview
2. Architecture
3. Prerequisites
4. API Reference
5. Markdown Template and Guidelines
6. Agent Integration Guide
7. Verification System
8. Frontend Integration Guide
9. Testing Procedures
10. Troubleshooting Guide
11. End-to-End Examples
12. Configuration Options

Overview

The Results Reporting System is the third and final component in the Hephaestus trilogy (following Git Worktree Isolation and Validation Agent System). It provides a formal mechanism for agents to document their achievements, creating a permanent record of work completed with verification tracking.

Purpose

• Accountability: Creates an audit trail of agent work
• Quality Assurance: Enables verification of claimed achievements
• Knowledge Preservation: Documents solutions for future reference
• Trust Building: Provides evidence-based validation of results

Key Features

• Markdown-based result reporting
• Two result types: Task-level (AgentResult) and Workflow-level (WorkflowResult)
• Multiple results per task support
• Immutable result storage
• Verification status tracking
• Integration with validation system
• File size and format validation
• Path traversal protection

Result Types

Hephaestus supports two types of results:

1. Task-Level Results (AgentResult):

   • Created via POST /report_results endpoint
   • Associated with a specific task
   • Verification status: unverified, verified, or disputed
   • Multiple results can be submitted for the same task

2. Workflow-Level Results (WorkflowResult):

   • Created via POST /submit_result endpoint
   • Associated with an entire workflow
   • Status: pending_validation, validated, or rejected
   • Automatically triggers result validator agent when configured
   • Marks workflow completion when validated

Architecture

System Components

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│                 │     │                  │     │                 │
│  Agent Process  │────▶│   MCP Server     │────▶│    Database     │
│                 │     │                  │     │                 │
└─────────────────┘     └──────────────────┘     └─────────────────┘
        │                       │                         │
        │                       │                         │
        ▼                       ▼                         ▼
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Markdown File  │     │ Result Service   │     │ agent_results   │
│   (results.md)  │     │   Validation     │     │     table       │
└─────────────────┘     └──────────────────┘     └─────────────────┘

Database Schema

CREATE TABLE agent_results (
    id VARCHAR PRIMARY KEY,
    agent_id VARCHAR NOT NULL,
    task_id VARCHAR NOT NULL,
    markdown_content TEXT NOT NULL,
    markdown_file_path TEXT NOT NULL,
    result_type VARCHAR NOT NULL CHECK (
        result_type IN ('implementation', 'analysis', 'fix',
                       'design', 'test', 'documentation')
    ),
    summary TEXT NOT NULL,
    verification_status VARCHAR NOT NULL DEFAULT 'unverified' CHECK (
        verification_status IN ('unverified', 'verified', 'disputed')
    ),
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    verified_at TIMESTAMP,
    verified_by_validation_id VARCHAR,
    FOREIGN KEY (agent_id) REFERENCES agents(id),
    FOREIGN KEY (task_id) REFERENCES tasks(id),
    FOREIGN KEY (verified_by_validation_id) REFERENCES validation_reviews(id)
);

Data Flow

1. Agent Completes Work: Agent finishes task implementation
2. Create Markdown Report: Agent writes results to markdown file
3. Submit via MCP: Agent calls report_results endpoint
4. Validation: System validates file and ownership
5. Storage: Result stored in database
6. Task Update: Task marked as having results
7. Verification: Validator agent can verify results
8. Status Update: Verification status updated

Prerequisites

Required Systems

• Git Worktree Isolation System (must be implemented)
• Validation Agent System (must be implemented)
• SQLite database with proper schema
• Python 3.8+ with required dependencies
• MCP server running on port 8000

Dependencies

# Required Python packages
sqlalchemy>=1.4.0
fastapi>=0.68.0
pydantic>=1.8.0
httpx>=0.19.0
pytest>=6.2.0
pytest-asyncio>=0.15.0

API Reference

POST /report_results

Submit formal results for a completed task.

Request Headers

X-Agent-ID: <agent-id>
Content-Type: application/json

Request Body

{
    "task_id": "task-uuid",
    "markdown_file_path": "/path/to/results.md",
    "result_type": "implementation",
    "summary": "Brief summary of results"
}

Result Types

• implementation: Code or feature implementation
• analysis: Research or analysis results
• fix: Bug fix or issue resolution
• design: Design documents or architecture
• test: Test implementation or results
• documentation: Documentation creation or updates

Response (200 OK)

{
    "status": "stored",
    "result_id": "result-uuid",
    "task_id": "task-uuid",
    "agent_id": "agent-uuid",
    "verification_status": "unverified",
    "created_at": "2024-01-01T12:00:00Z"
}

Error Responses

400 Bad Request

{
    "detail": "File too large: 150.00KB exceeds maximum of 100KB"
}

404 Not Found

{
    "detail": "Markdown file not found: /path/to/missing.md"
}

403 Forbidden

{
    "detail": "Task task-123 is not assigned to agent agent-456"
}

Validation Rules

1. File Existence: File must exist and be readable
2. File Format: Must be markdown (.md extension)
3. File Size: Maximum 100KB
4. Path Security: No directory traversal allowed
5. Task Ownership: Agent must own the task
6. Multiple Results: Agents can submit multiple results per task
7. Immutability: Results cannot be modified after submission

POST /submit_result

Submit a workflow-level result (different from task-level results).

Request Headers

X-Agent-ID: <agent-id>
Content-Type: application/json

Request Body

{
    "markdown_file_path": "/path/to/result.md",
    "explanation": "Brief explanation of what was accomplished",
    "evidence": ["Evidence item 1", "Evidence item 2"]
}

Response (200 OK)

{
    "status": "submitted",
    "result_id": "result-uuid",
    "workflow_id": "workflow-uuid",
    "agent_id": "agent-uuid",
    "validation_triggered": true,
    "message": "Result submitted successfully and validation triggered",
    "created_at": "2024-01-01T12:00:00Z"
}

Note: This endpoint is for workflow-level results that mark completion of an entire workflow. It automatically derives the workflow_id from the agent's current task and may trigger result validation.

Markdown Template and Guidelines

Template Location

templates/result_report_template.md

Required Sections

1. Task Results Header

   • Clear title describing the task

2. Summary

   • 2-3 sentence overview of achievements

3. Detailed Achievements

   • Bullet list of completed items
   • Technical implementation details

4. Artifacts Created

   • Table of files created/modified
   • Code metrics if applicable

5. Validation Evidence

   • Test results
   • Manual verification steps
   • Performance metrics

6. Known Limitations

   • Current limitations
   • Workarounds available

7. Recommended Next Steps

   • Immediate actions needed
   • Future enhancements

Best Practices

# Good Example
## Summary
Successfully implemented JWT authentication with refresh tokens,
achieving 100% test coverage and sub-100ms response times.

# Bad Example
## Summary
Done with auth stuff.

Agent Integration Guide

Step 1: Complete Your Task

# Perform the actual work
result = implement_feature()
run_tests()
verify_output()

Step 2: Create Markdown Report

report_content = f"""# Task Results: {task_description}

## Summary
{summary_of_work}

## Detailed Achievements
{list_of_achievements}

## Artifacts Created
{table_of_files}

## Validation Evidence
{test_results}
"""

# Write to file
report_path = f"/tmp/results_{task_id}.md"
with open(report_path, 'w') as f:
    f.write(report_content)

Step 3: Submit Results via MCP

import httpx

async def report_results(task_id, report_path, agent_id):
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:8000/report_results",
            json={
                "task_id": task_id,
                "markdown_file_path": report_path,
                "result_type": "implementation",
                "summary": "Implemented feature successfully"
            },
            headers={"X-Agent-ID": agent_id}
        )

        if response.status_code == 200:
            result_data = response.json()
            print(f"Results stored: {result_data['result_id']}")
        else:
            print(f"Error: {response.text}")

Step 4: Update Task Status

# After reporting results, mark task as complete
await update_task_status(task_id, "done", summary, learnings)

Verification System

How Verification Works

1. Initial State: Results start as unverified
2. Validation Trigger: When task enters validation
3. Validator Review: Validator agent examines results
4. Evidence Check: Validator verifies claims against evidence
5. Status Update: Results marked as verified or disputed

Verification States

Status	Description	When Applied
unverified	Default state	On creation
verified	Claims validated	Validation passed
disputed	Claims questioned	Validation failed

Integration with Validation System

# In validation review handler
if validation_passed:
    # Update all results for the task to verified
    for result in task.results:
        ResultService.verify_result(
            result_id=result.id,
            validation_review_id=review.id,
            verified=True
        )

Frontend Integration Guide

Fetching Results

// Get results for a specific task
async function getTaskResults(taskId) {
    const response = await fetch(`/api/results?task_id=${taskId}`);
    const results = await response.json();
    return results;
}

Displaying Results

function ResultCard({ result }) {
    return (
        <div className="result-card">
            <div className="result-header">
                <h3>{result.summary}</h3>
                <VerificationBadge status={result.verification_status} />
            </div>
            <div className="result-content">
                <MarkdownRenderer content={result.markdown_content} />
            </div>
            <div className="result-metadata">
                <span>Type: {result.result_type}</span>
                <span>Created: {result.created_at}</span>
                {result.verified_at && (
                    <span>Verified: {result.verified_at}</span>
                )}
            </div>
        </div>
    );
}

Verification Badge Component

function VerificationBadge({ status }) {
    const badges = {
        unverified: { icon: '⏳', color: 'gray', text: 'Unverified' },
        verified: { icon: '✅', color: 'green', text: 'Verified' },
        disputed: { icon: '⚠️', color: 'red', text: 'Disputed' }
    };

    const badge = badges[status];

    return (
        <span className={`badge badge-${badge.color}`}>
            {badge.icon} {badge.text}
        </span>
    );
}

Testing Procedures

Unit Tests

Run unit tests for the result service:

pytest tests/test_result_service.py -v

Run unit tests for the MCP endpoint:

pytest tests/test_mcp_results_endpoint.py -v

Integration Tests

Run the full integration test:

python tests/mcp_integration/test_mcp_flow.py

Expected output:

✓ Server Health Check
✓ Create Task via MCP
✓ Get Tasks List
✓ Save Memory
✓ Update Task (Wrong Agent)
✓ Report Results
✓ Report Multiple Results
✓ Update Task (Correct Agent)
✓ Update Task (Missing Fields)
✓ Get Agent Status

All 10 tests passed!

Manual Testing

1. Start the MCP server:

python run_server.py

2. Create a test markdown file:

echo "# Test Results\n\nTest content" > /tmp/test_results.md

3. Submit results via curl:

curl -X POST http://localhost:8000/report_results \
  -H "Content-Type: application/json" \
  -H "X-Agent-ID: test-agent-001" \
  -d '{
    "task_id": "task-123",
    "markdown_file_path": "/tmp/test_results.md",
    "result_type": "implementation",
    "summary": "Test implementation"
  }'

Troubleshooting Guide

Common Issues and Solutions

Issue: "File not found" error

Cause: Markdown file doesn't exist at specified path Solution:

• Verify file exists: ls -la /path/to/file.md
• Check file permissions: chmod 644 /path/to/file.md
• Use absolute paths instead of relative

Issue: "File too large" error

Cause: Markdown file exceeds 100KB limit Solution:

• Check file size: du -h /path/to/file.md
• Split into multiple results if needed
• Compress images or move to external storage

Issue: "Not assigned to agent" error

Cause: Agent trying to report results for wrong task Solution:

• Verify task assignment in database
• Check agent ID in request header
• Ensure task wasn't reassigned

Issue: Results not being verified

Cause: Validation system not running or misconfigured Solution:

• Check validation agent is spawned
• Verify validation_reviews table has entries
• Check worktree manager is initialized

Debug Logging

Enable debug logging for troubleshooting:

import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("src.services.result_service")

Database Queries for Debugging

-- Check results for a task
SELECT * FROM agent_results WHERE task_id = 'task-123';

-- Check verification status
SELECT ar.*, vr.feedback
FROM agent_results ar
LEFT JOIN validation_reviews vr ON ar.verified_by_validation_id = vr.id
WHERE ar.task_id = 'task-123';

-- Count results by status
SELECT verification_status, COUNT(*)
FROM agent_results
GROUP BY verification_status;

End-to-End Examples

Example 1: Simple Implementation Task

# Agent completes implementation
async def complete_implementation_task(task_id, agent_id):
    # 1. Do the work
    implement_feature()
    run_tests()

    # 2. Create report
    report = """# Task Results: Implement User Authentication

## Summary
Implemented JWT-based authentication with refresh tokens.

## Detailed Achievements
- [x] JWT token generation
- [x] Token validation middleware
- [x] Refresh token mechanism
- [x] 15 unit tests passing

## Artifacts Created
| File Path | Type | Description |
|-----------|------|-------------|
| src/auth.py | Python | Authentication logic |
| tests/test_auth.py | Python | Unit tests |

## Validation Evidence

Ran 15 tests in 0.5s OK

"""

    # 3. Write to file
    report_path = f"/tmp/results_{task_id}.md"
    with open(report_path, 'w') as f:
        f.write(report)

    # 4. Submit results
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:8000/report_results",
            json={
                "task_id": task_id,
                "markdown_file_path": report_path,
                "result_type": "implementation",
                "summary": "Implemented JWT authentication"
            },
            headers={"X-Agent-ID": agent_id}
        )

        result_id = response.json()["result_id"]
        print(f"Results reported: {result_id}")

    # 5. Mark task complete
    await update_task_status(task_id, "done")

Example 2: Multiple Results for Analysis Task

async def report_analysis_results(task_id, agent_id):
    # First result: Initial analysis
    analysis_report = """# Task Results: Performance Analysis

## Summary
Analyzed application performance and identified bottlenecks.

## Detailed Achievements
- Profiled application under load
- Identified N+1 query problems
- Found memory leaks in cache layer
"""

    with open("/tmp/analysis.md", 'w') as f:
        f.write(analysis_report)

    await submit_result(task_id, "/tmp/analysis.md",
                       "analysis", "Performance analysis complete")

    # Second result: Optimization recommendations
    recommendations = """# Additional Results: Optimization Plan

## Summary
Detailed plan for addressing performance issues.

## Recommendations
1. Implement query batching
2. Add Redis caching layer
3. Optimize database indices
"""

    with open("/tmp/recommendations.md", 'w') as f:
        f.write(recommendations)

    await submit_result(task_id, "/tmp/recommendations.md",
                       "design", "Optimization plan created")

Example 3: Fix with Validation

async def fix_bug_with_validation(task_id, agent_id):
    # 1. Fix the bug
    apply_fix()

    # 2. Create detailed report with evidence
    report = """# Task Results: Fix Memory Leak in Cache Manager

## Summary
Fixed memory leak by implementing proper cleanup in cache eviction.

## Detailed Achievements
- Identified leak using memory profiler
- Implemented cleanup callbacks
- Added unit tests for cleanup
- Verified fix under load

## Validation Evidence
### Before Fix

Memory usage: 2.5GB after 1 hour Growth rate: 50MB/minute

### After Fix

Memory usage: 500MB after 1 hour Growth rate: 0MB/minute (stable)

## Test Results

pytest tests/test_cache.py::test_cleanup ===== 5 passed in 0.3s =====

"""

    # 3. Save and submit
    with open("/tmp/fix_results.md", 'w') as f:
        f.write(report)

    result = await submit_result(task_id, "/tmp/fix_results.md",
                                "fix", "Memory leak fixed")

    # 4. Mark task done (triggers validation if enabled)
    await update_task_status(task_id, "done")

    # 5. Result will be verified during validation
    print(f"Result {result['result_id']} awaiting verification")

Configuration Options

Current Implementation

The Results Reporting System uses hard-coded configuration values implemented in the validation layer:

File Size Limit:

• Maximum file size: 100 KB (hard-coded in src/services/validation_helpers.py)
• Enforced by validate_file_size() function

File Format:

• Allowed format: Markdown (.md) only (hard-coded in src/services/validation_helpers.py)
• Enforced by validate_markdown_format() function

Security Validation:

• Path traversal protection enabled (checks for ".." in paths)
• Task ownership verification required
• Results are immutable after creation

Database Indexing

Database indices are automatically managed by SQLAlchemy through foreign key relationships defined in the AgentResult model:

# src/core/database.py - AgentResult model
class AgentResult(Base):
    __tablename__ = "agent_results"

    id = Column(String, primary_key=True)
    agent_id = Column(String, ForeignKey("agents.id"), nullable=False)
    task_id = Column(String, ForeignKey("tasks.id"), nullable=False)
    # ... other fields

SQLAlchemy automatically creates indices for:

• Primary key (id)
• Foreign keys (agent_id, task_id, verified_by_validation_id)

Future Configuration Enhancements (Planned)

The following configuration options are planned for future releases:

# Planned environment variables (not yet implemented)
HEPHAESTUS_MAX_RESULT_SIZE_KB=100
HEPHAESTUS_RESULTS_DIR=/var/hephaestus/results
HEPHAESTUS_ARCHIVE_RESULTS=true
HEPHAESTUS_ARCHIVE_RETENTION_DAYS=30

# Planned configuration class (not yet implemented)
class ResultsConfig:
    max_file_size_kb: int = 100
    allowed_file_extensions: List[str] = [".md"]
    results_directory: str = "/tmp"
    enable_archival: bool = False
    archive_retention_days: int = 30
    enable_duplicate_detection: bool = True
    duplicate_threshold: float = 0.95

Performance Considerations

Optimization Tips

1. Batch Result Queries: Fetch all results for a task in one query
2. Cache Markdown Content: Store rendered HTML to avoid re-parsing
3. Index Frequently Queried Fields: Add database indices as needed
4. Compress Old Results: Archive and compress results after 30 days
5. Limit Result Size: Enforce 100KB limit to prevent storage issues

Monitoring Metrics

# Key metrics to track
metrics = {
    "results_per_task": "Average number of results per task",
    "result_size_avg": "Average result file size",
    "verification_rate": "Percentage of verified results",
    "submission_latency": "Time to store result",
    "retrieval_latency": "Time to fetch results",
}

Security Considerations

Path Traversal Protection

def validate_file_path(file_path: str) -> None:
    """Prevent directory traversal attacks."""
    if ".." in file_path:
        raise ValueError("Directory traversal detected")

    # Resolve to absolute path
    abs_path = os.path.abspath(file_path)

    # Ensure within allowed directories
    if not abs_path.startswith(ALLOWED_DIRS):
        raise ValueError("File outside allowed directories")

Input Sanitization

• Validate all file paths
• Check file extensions
• Limit file sizes
• Sanitize markdown content
• Validate task ownership

Access Control

• Agents can only report results for their tasks
• Results are immutable after creation
• Verification limited to validator agents
• Read access controlled by task visibility

Support and Resources

Documentation

• Git Worktree Isolation System
• Validation Agent System
• MCP Server Documentation (see main codebase)

Code Examples

• Integration Tests
• Unit Tests
• Template

Troubleshooting

• Check logs in the console output or agent tmux sessions
• Enable debug mode with LOG_LEVEL=DEBUG
• Query database directly to inspect results:

  SELECT * FROM agent_results WHERE task_id = 'your-task-id';

• Use the frontend dashboard to view results and validation status

---

Document Version: 1.0.0 Last Updated: January 2024 System Version: Hephaestus Results Reporting System v1.0.0