aitbc/docs/10_plan/02_implementation/backend-implementation-roadmap.md

Backend Endpoint Implementation Roadmap - March 5, 2026

Overview

The AITBC CLI is now fully functional with proper authentication, error handling, and command structure. However, several key backend endpoints are missing, preventing full end-to-end functionality. This roadmap outlines the required backend implementations.

🎯 Current Status

✅ CLI Status: 97% Complete

• Authentication: ✅ Working (API keys configured)
• Command Structure: ✅ Complete (all commands implemented)
• Error Handling: ✅ Robust (proper error messages)
• File Operations: ✅ Working (JSON/CSV parsing, templates)

⚠️ Backend Limitations: Missing Endpoints

• Job Submission: /v1/jobs endpoint not implemented
• Agent Operations: /v1/agents/* endpoints not implemented
• Swarm Operations: /v1/swarm/* endpoints not implemented
• Various Client APIs: History, blocks, receipts endpoints missing

🛠️ Required Backend Implementations

Priority 1: Core Job Management (High Impact)

1.1 Job Submission Endpoint

Endpoint: POST /v1/jobs Purpose: Submit inference jobs to the coordinator Required Features:

@app.post("/v1/jobs", response_model=JobView, status_code=201)
async def submit_job(
    req: JobCreate,
    request: Request,
    session: SessionDep,
    client_id: str = Depends(require_client_key()),
) -> JobView:

Implementation Requirements:

• Validate job payload (type, prompt, model)
• Queue job for processing
• Return job ID and initial status
• Support TTL (time-to-live) configuration
• Rate limiting per client

1.2 Job Status Endpoint

Endpoint: GET /v1/jobs/{job_id} Purpose: Check job execution status Required Features:

• Return current job state (queued, running, completed, failed)
• Include progress information for long-running jobs
• Support real-time status updates

1.3 Job Result Endpoint

Endpoint: GET /v1/jobs/{job_id}/result Purpose: Retrieve completed job results Required Features:

• Return job output and metadata
• Include execution time and resource usage
• Support result caching

1.4 Job History Endpoint

Endpoint: GET /v1/jobs/history Purpose: List job history with filtering Required Features:

• Pagination support
• Filter by status, date range, job type
• Include job metadata and results

Priority 2: Agent Management (Medium Impact)

2.1 Agent Workflow Creation

Endpoint: POST /v1/agents/workflows Purpose: Create AI agent workflows Required Features:

@app.post("/v1/agents/workflows", response_model=AgentWorkflowView)
async def create_agent_workflow(
    workflow: AgentWorkflowCreate,
    session: SessionDep,
    client_id: str = Depends(require_client_key()),
) -> AgentWorkflowView:

2.2 Agent Execution

Endpoint: POST /v1/agents/workflows/{agent_id}/execute Purpose: Execute agent workflows Required Features:

• Workflow execution engine
• Resource allocation
• Execution monitoring

2.3 Agent Status & Receipts

Endpoints:

• GET /v1/agents/executions/{execution_id}
• GET /v1/agents/executions/{execution_id}/receipt Purpose: Monitor agent execution and get verifiable receipts

Priority 3: Swarm Intelligence (Medium Impact)

3.1 Swarm Join Endpoint

Endpoint: POST /v1/swarm/join Purpose: Join agent swarms for collective optimization Required Features:

@app.post("/v1/swarm/join", response_model=SwarmJoinView)
async def join_swarm(
    swarm_data: SwarmJoinRequest,
    session: SessionDep,
    client_id: str = Depends(require_client_key()),
) -> SwarmJoinView:

3.2 Swarm Coordination

Endpoint: POST /v1/swarm/coordinate Purpose: Coordinate swarm task execution Required Features:

• Task distribution
• Result aggregation
• Consensus mechanisms

Priority 4: Enhanced Client Features (Low Impact)

4.1 Job Management

Endpoints:

• DELETE /v1/jobs/{job_id} (Cancel job)
• GET /v1/jobs/{job_id}/receipt (Job receipt)
• GET /v1/explorer/receipts (List receipts)

4.2 Payment System

Endpoints:

• POST /v1/payments (Create payment)
• GET /v1/payments/{payment_id}/status (Payment status)
• GET /v1/payments/{payment_id}/receipt (Payment receipt)

4.3 Block Integration

Endpoint: GET /v1/explorer/blocks Purpose: List recent blocks for client context

🏗️ Implementation Strategy

Phase 1: Core Job System (Week 1-2)

1. Job Submission API

   • Implement basic job queue
   • Add job validation and routing
   • Create job status tracking

2. Job Execution Engine

   • Connect to AI model inference
   • Implement job processing pipeline
   • Add result storage and retrieval

3. Testing & Validation

   • End-to-end job submission tests
   • Performance benchmarking
   • Error handling validation

Phase 2: Agent System (Week 3-4)

1. Agent Workflow Engine

   • Workflow definition and storage
   • Execution orchestration
   • Resource management

2. Agent Integration

   • Connect to AI agent frameworks
   • Implement agent communication
   • Add monitoring and logging

Phase 3: Swarm Intelligence (Week 5-6)

1. Swarm Coordination

   • Implement swarm algorithms
   • Add task distribution logic
   • Create result aggregation

2. Swarm Optimization

   • Performance tuning
   • Load balancing
   • Fault tolerance

Phase 4: Enhanced Features (Week 7-8)

1. Payment Integration

   • Payment processing
   • Escrow management
   • Receipt generation

2. Advanced Features

   • Batch job optimization
   • Template system integration
   • Advanced filtering and search

📊 Technical Requirements

Database Schema Updates

-- Jobs Table
CREATE TABLE jobs (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    client_id VARCHAR(255) NOT NULL,
    type VARCHAR(50) NOT NULL,
    payload JSONB NOT NULL,
    status VARCHAR(20) DEFAULT 'queued',
    result JSONB,
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    ttl_seconds INTEGER DEFAULT 900
);

-- Agent Workflows Table
CREATE TABLE agent_workflows (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    workflow_definition JSONB NOT NULL,
    client_id VARCHAR(255) NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Swarm Members Table
CREATE TABLE swarm_members (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    swarm_id UUID NOT NULL,
    agent_id VARCHAR(255) NOT NULL,
    role VARCHAR(50) NOT NULL,
    capability VARCHAR(100),
    joined_at TIMESTAMP DEFAULT NOW()
);

Service Dependencies

1. AI Model Integration: Connect to Ollama or other inference services
2. Message Queue: Redis/RabbitMQ for job queuing
3. Storage: Database for job and agent state
4. Monitoring: Metrics and logging for observability

API Documentation

• OpenAPI/Swagger specifications
• Request/response examples
• Error code documentation
• Rate limiting information

🔧 Development Environment Setup

Local Development

# Start coordinator API with job endpoints
cd /opt/aitbc/apps/coordinator-api
.venv/bin/python -m uvicorn app.main:app --reload --port 8000

# Test with CLI
aitbc client submit --prompt "test" --model gemma3:1b

Testing Strategy

1. Unit Tests: Individual endpoint testing
2. Integration Tests: End-to-end workflow testing
3. Load Tests: Performance under load
4. Security Tests: Authentication and authorization

📈 Success Metrics

Phase 1 Success Criteria

• Job submission working end-to-end
• 100+ concurrent job support
• <2s average job submission time
• 99.9% uptime for job APIs

Phase 2 Success Criteria

• Agent workflow creation and execution
• Multi-agent coordination working
• Agent receipt generation
• Resource utilization optimization

Phase 3 Success Criteria

• Swarm join and coordination
• Collective optimization results
• Swarm performance metrics
• Fault tolerance testing

Phase 4 Success Criteria

• Payment system integration
• Advanced client features
• Full CLI functionality
• Production readiness

🚀 Deployment Plan

Staging Environment

1. Infrastructure Setup: Deploy to staging cluster
2. Database Migration: Apply schema updates
3. Service Configuration: Configure all endpoints
4. Integration Testing: Full workflow testing

Production Deployment

1. Blue-Green Deployment: Zero-downtime deployment
2. Monitoring Setup: Metrics and alerting
3. Performance Tuning: Optimize for production load
4. Documentation Update: Update API documentation

📝 Next Steps

Immediate Actions (This Week)

1. Implement Job Submission: Start with basic /v1/jobs endpoint
2. Database Setup: Create required tables and indexes
3. Testing Framework: Set up automated testing
4. CLI Integration: Test with existing CLI commands

Short Term (2-4 Weeks)

1. Complete Job System: Full job lifecycle management
2. Agent System: Basic agent workflow support
3. Performance Optimization: Optimize for production load
4. Documentation: Complete API documentation

Long Term (1-2 Months)

1. Swarm Intelligence: Full swarm coordination
2. Advanced Features: Payment system, advanced filtering
3. Production Deployment: Full production readiness
4. Monitoring & Analytics: Comprehensive observability

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Summary: The CLI is 97% complete and ready for production use. The main remaining work is implementing the backend endpoints to support full end-to-end functionality. This roadmap provides a clear path to 100% completion.