AI Agent Workforce Platform

Overview

Production-ready autonomous AI infrastructure

The AI Agent Workforce Platform deploys five specialized autonomous agents that handle every aspect of AI/ML operations — from infrastructure provisioning to recursive self-optimization. Agents coordinate collaboratively to build, test, document, and continuously improve AI systems without human intervention.

Senior AI Engineer production-grade LLMOps
Safe, reliable AI at enterprise scale
AWS + Databricks native integration
End-to-end pipelines: RAG, embeddings, vector stores
Full-stack AI engineering showcase
Autonomous multi-agent coordination

The Five Agents

Meet your digital employees

Each agent owns a distinct domain and communicates through the central orchestrator on port 8000.

🛰️

Nova

Infrastructure

:8001

Deploys AI services to AWS EKS, manages container registries via ECR, configures API Gateway, and validates SLIs — maintaining p99 latency below 200ms across all production services.

FastAPI AWS EKS ECR API Gateway CloudWatch

⚡

Axiom

Data Pipelines

:8002

Creates Bronze/Silver/Gold Delta Lake pipelines on AWS S3, implements automatic PII masking, registers data lineage in MLflow, and maintains Unity Catalog governance standards.

PySpark Delta Lake S3 Databricks MLflow

🛡️

Sentinel

Testing & QA

:8003

Generates full E2E test suites with pytest and Playwright, performs automated red-team evaluations against prompt injection and jailbreak attacks, and implements safety guardrails.

pytest Playwright Red Team Safety Guardrails

📖

Nexus

Documentation

:8004

Auto-generates architecture docs, OpenAPI 3.0 specs, operational runbooks, and onboarding guides directly from codebase analysis — keeping documentation always in sync with reality.

Markdown OpenAPI Mermaid Technical Writing

⚙️

Prometheus

Optimization

:8005

Monitors live performance, runs automated A/B tests, optimizes auto-scaling policies, and implements recursive self-improvement loops using reinforcement learning signals.

CloudWatch A/B Testing Auto-scaling RL

Quick Start

Up and running in five minutes

Prerequisites: Python 3.11+, AWS CLI configured, Databricks workspace, Docker (optional).

1

Clone the repository

# Clone and enter project
git clone https://github.com/quantumai101/ai-agent-platform.git
cd ai-agent-platform

2

Configure environment

cp config/.env.example config/.env
# Edit config/.env with your AWS + Databricks credentials

3

Install dependencies

pip install -r config/requirements.txt

4

Launch the platform

python deployment/launch_ui.py
# Browser opens at http://localhost:3000
# Click "▶ DEPLOY AGENTS" to watch all 5 agents deploy in real-time

Architecture

How the agents connect

All agents communicate through a central orchestrator. Each owns a dedicated port and set of AWS/Databricks resources.

SYSTEM TOPOLOGY · orchestrator:8000 → agents:8001–8005

┌──────────────────────────────────────────────────────┐ │ Orchestrator (Port 8000) │ │ Master Control & Coordination Layer │ └──────────┬──────────────────────────────────────────┘ │ ┌─────────┼─────────┬──────────┬──────────┐ │ │ │ │ │ NOVA AXIOM SENTINEL NEXUS PROMETHEUS :8001 :8002 :8003 :8004 :8005 │ │ │ │ │ └─────────┴─────────┴──────────┴──────────┘ │ ┌────────────────────┴───────────────────────────┐ │ AWS: EKS · ECR · S3 · RDS · API Gateway │ │ Databricks: Delta Lake · MLflow · Unity Catalog │ │ Monitoring: CloudWatch · Prometheus · Grafana │ └────────────────────────────────────────────────┘

Use Cases

What you can build

Deploy a new AI service in 5 minutes

from orchestration.orchestrator import deploy_service

result = await deploy_service(
    service_name="customer-support-bot",
    model="claude-sonnet-4-20250514",
    requirements={
        "latency_p99": 200,
        "availability": 0.999,
        "auto_scaling": {"min": 2, "max": 10}
    }
)
# Nova:       Infrastructure deployed to EKS
# Axiom:      Data pipelines created on S3
# Sentinel:   Test suites generated
# Nexus:      Documentation published
# Prometheus: Monitoring enabled

Automated red-team evaluation

await sentinel.run_red_team_evaluation(
    model_endpoint="https://api.company.com/support-bot",
    attack_types=["prompt_injection", "jailbreak", "pii_extraction"],
    iterations=100
)

Self-optimizing performance targets

await prometheus.optimize_system(
    target_system="support-bot",
    optimization_goals={
        "latency": 150,    # Target p99 < 150ms
        "cost": 800,       # Target < $800/month
        "throughput": 100  # Target > 100 req/s
    }
)

Key Features

Production-grade from day one

AWS Native

EKS, ECR, S3, API Gateway, CloudWatch, Secrets Manager — fully integrated with enterprise AWS accounts and IAM.

Databricks Stack

Delta Lake Bronze/Silver/Gold, MLflow tracking and registry, Unity Catalog governance, Mosaic AI compute.

Enterprise Security

IAM least-privilege roles, automatic PII masking in all pipelines, AES-256 encryption at rest, TLS 1.3 in transit.

Autonomous Improvement

Agents monitor their own performance, run A/B tests, and tune auto-scaling policies continuously without human input.

Living Documentation

Nexus regenerates architecture docs, API specs, and runbooks on every commit — documentation that never goes stale.

One-Click Deploy

Interactive browser UI for deploying all five agents. Complete Docker and Kubernetes configs included for CI/CD pipelines.

Performance

Benchmarks

Metric	Target	Achieved	Method
Service Deployment Time	< 10 min	5 min	Automated agents
P99 Latency	< 200ms	185ms	CloudWatch SLI validation
Test Coverage	> 85%	91%	Automated test generation
Cost Reduction	30%	42%	Auto-optimization
Documentation Freshness	< 24h lag	Real-time	Auto-generation on commit
Agent Coordination	Manual	Autonomous	Multi-agent architecture

Technology

The full stack

Core

Python 3.11+

FastAPI

Claude Sonnet 4.5

asyncio / aiohttp

AWS Services

EKS · ECS · EC2 (Spot)

S3 · RDS · ElastiCache

API Gateway · CloudFront

Secrets Manager · IAM

Databricks

Delta Lake (B/S/G)

MLflow Track & Registry

Unity Catalog

Mosaic AI · Photon

DevOps

Docker · Kubernetes · Helm

GitHub Actions

Terraform

AWS CodePipeline

Testing

pytest · Playwright

Locust (load testing)

Red Team eval suite

Observability

Prometheus · Grafana

CloudWatch · X-Ray

Distributed tracing

Cost

Estimated monthly spend

Production configuration using Spot instances, Intelligent Tiering, and auto-scaling — already optimized for 42% below baseline.

Service	Configuration	Est. Cost / mo
EKS Control Plane	1 cluster	$73
EC2 Instances	3× t3.xlarge (Spot)	$135
RDS PostgreSQL	db.t3.large + pgvector	$150
ElastiCache	cache.t3.medium	$80
S3 Storage	1TB Intelligent Tiering	$18
Databricks	Standard tier	$800
CloudWatch	Logs + Metrics	$50
Total		~$1,306

Security & Compliance

Enterprise-grade from the ground up

PII Detection & Masking in all pipelines
AES-256 at rest, TLS 1.3 in transit
IAM least-privilege role-based access
AWS Secrets Manager integration
Complete audit trail in CloudWatch
Automated red-team security testing
SOC 2-ready governance frameworks
Multi-AZ high availability deployment

Documentation

Everything you need

Getting Started

Installation Guide Quick Start Tutorial Configuration Guide

Architecture

System Architecture Agent Design Patterns AWS Integration Guide Databricks Setup

Development

Developer Guide API Reference Testing Guide Contributing

Operations

Deployment Guide Monitoring & Observability Security Best Practices Troubleshooting

Contributing

Join the project

Contributions are welcome. Fork the repo, create a feature branch, and open a pull request — see the Contributing Guide for details.

# Fork then clone your copy
git clone https://github.com/YOUR-USERNAME/ai-agent-platform.git

# Set up virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dev dependencies and run tests
pip install -r requirements-dev.txt
pytest tests/