Platform Architecture

Understanding how Auteryn works under the hood. This guide covers the technical architecture, design decisions, and how components work together.

High-Level Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Auteryn Platform                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌────────────────┐      ┌──────────────────┐              │
│  │  Web Console   │      │   REST API       │              │
│  │  (Dashboard)   │◄────►│   (Public)       │              │
│  └────────────────┘      └──────────────────┘              │
│                                   │                          │
│                                   ▼                          │
│  ┌──────────────────────────────────────────────┐          │
│  │         Agent Orchestration Layer            │          │
│  │  - Agent lifecycle management                │          │
│  │  - Task scheduling & execution               │          │
│  │  - Event routing & webhooks                  │          │
│  └──────────────────────────────────────────────┘          │
│                    │              │                          │
│         ┌──────────┴──────┬───────┴──────┐                 │
│         ▼                 ▼              ▼                  │
│  ┌──────────┐      ┌──────────┐   ┌──────────┐            │
│  │ Sandbox  │      │ Sandbox  │   │ Sandbox  │            │
│  │ Agent A  │      │ Agent B  │   │ Agent C  │            │
│  │          │      │          │   │          │            │
│  │ - Files  │      │ - Files  │   │ - Files  │            │
│  │ - Shell  │      │ - Shell  │   │ - Shell  │            │
│  │ - Browser│      │ - Browser│   │ - Browser│            │
│  └──────────┘      └──────────┘   └──────────┘            │
│                                                              │
│  ┌──────────────────────────────────────────────┐          │
│  │         Knowledge Base Layer                 │          │
│  │  - Vector database (embeddings)              │          │
│  │  - Document storage                          │          │
│  │  - Semantic search                           │          │
│  └──────────────────────────────────────────────┘          │
│                                                              │
│  ┌──────────────────────────────────────────────┐          │
│  │         Integration Layer                    │          │
│  │  - OAuth management                          │          │
│  │  - API proxies                               │          │
│  │  - Webhook receivers                         │          │
│  └──────────────────────────────────────────────┘          │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Core Components

1. Agent Orchestration Layer

Responsibilities:

Agent lifecycle (create, start, stop, delete)
Task scheduling and execution
Event routing and webhooks
Resource allocation
Load balancing

Technology:

Python/FastAPI for API
Redis for task queues
PostgreSQL for metadata
Kubernetes for orchestration

2. Sandbox Environment

Responsibilities:

Isolated execution environment
Filesystem management
Terminal access
Browser automation
Snapshot management

Technology:

Docker containers
Persistent volumes
Chromium for browser
Linux (Ubuntu 22.04)

Isolation:

Network isolation (VPC)
Filesystem isolation (volumes)
Resource limits (CPU, memory, disk)
Security policies (AppArmor)

3. Knowledge Base Layer

Responsibilities:

Document ingestion and indexing
Semantic search
Source synchronization
Version management

Technology:

Vector database (Pinecone/Weaviate)
Embeddings (OpenAI/Cohere)
Document storage (S3)
Search API

Features:

Semantic search (not just keyword)
Multi-source aggregation
Real-time sync
Version history

4. Integration Layer

Responsibilities:

OAuth flow management
API credential storage
Webhook routing
Rate limiting
Error handling

Technology:

OAuth 2.0 / OpenID Connect
Encrypted credential storage
API gateway (Kong)
Webhook queue (Redis)

Data Flow

Agent Execution Flow

1. User sends message/trigger
   ↓
2. Orchestration layer receives request
   ↓
3. Agent instructions loaded
   ↓
4. Knowledge base queried (if needed)
   ↓
5. Sandbox executes task
   ↓
6. Integrations called (if needed)
   ↓
7. Response generated
   ↓
8. Snapshot created (auto)
   ↓
9. Response returned to user

Event-Driven Flow

1. External event occurs (GitHub PR, Jira ticket)
   ↓
2. Webhook received by integration layer
   ↓
3. Event routed to configured agent
   ↓
4. Agent processes event in sandbox
   ↓
5. Actions executed (comment, update, notify)
   ↓
6. Snapshot created
   ↓
7. Event marked complete

Scalability

Horizontal Scaling

Auteryn scales horizontally:

Sandboxes: Unlimited parallel execution
API: Auto-scaling based on load
Knowledge base: Distributed search
Integrations: Rate-limited per service

Performance Characteristics

Metric	Performance
Agent response time	< 2s (median)
Sandbox start time	< 5s (cold start)
Snapshot creation	< 1s (incremental)
Knowledge base search	< 500ms
API latency	< 100ms (p95)
Webhook processing	< 200ms

Load Handling

Tested at scale:

10,000+ concurrent agents
1M+ API requests/day
100K+ webhook events/day
99.9% uptime SLA

Security Architecture

Defense in Depth

Multiple security layers:

Network Layer - VPC isolation, firewall rules
Application Layer - Authentication, authorization
Data Layer - Encryption at rest and in transit
Sandbox Layer - Container isolation, resource limits

Encryption

All data encrypted:

At rest: AES-256 encryption
In transit: TLS 1.3
Credentials: Separate encryption key per customer
Backups: Encrypted snapshots

Compliance

SOC 2 Type II - Annual audit
GDPR - EU data residency available
CCPA - California privacy compliance
HIPAA - Available for Enterprise (BAA required)

Learn more about security →

Reliability & Availability

High Availability

Infrastructure:

Multi-region deployment
Automatic failover
Load balancing
Health checks

SLA: 99.9% uptime

Downtime allowed: 43 minutes/month
Actual uptime: 99.95% (last 12 months)

Disaster Recovery

Backup Strategy:

Automatic snapshots every 5 minutes
Cross-region replication
Point-in-time recovery
30-day retention (configurable)

Recovery Time:

RTO (Recovery Time Objective): < 1 hour
RPO (Recovery Point Objective): < 5 minutes

Monitoring

What we monitor:

API response times
Sandbox health
Integration status
Error rates
Resource usage

Alerting:

PagerDuty for critical issues
Slack for warnings
Email for updates

Technology Stack

Backend

API: Python 3.11, FastAPI
Database: PostgreSQL 15
Cache: Redis 7
Queue: Redis + Celery
Search: Elasticsearch 8

Infrastructure

Compute: Kubernetes (EKS)
Storage: S3, EBS
Network: VPC, CloudFront
Monitoring: Prometheus, Grafana
Logging: ELK stack

Frontend

Console: React 18, TypeScript
Docs: Astro, Starlight
Widget: Vanilla JS (no dependencies)

API Design

RESTful Principles

All APIs follow REST conventions:

Resources: /agents, /sandboxes, /knowledge-bases
Methods: GET, POST, PUT, DELETE
Status codes: 200, 201, 400, 401, 404, 500
Pagination: Cursor-based
Versioning: /v1/, /v2/

Rate Limiting

Protect against abuse:

Plan	Rate Limit	Burst
Free	60 req/min	100
Starter	300 req/min	500
Pro	1,000 req/min	2,000
Enterprise	Custom	Custom

Webhooks

Event-driven architecture:

{
  "event": "agent.task.completed",
  "timestamp": "2026-04-02T10:15:23Z",
  "agent_id": "agent_123",
  "task_id": "task_456",
  "status": "success",
  "data": { ... }
}

API documentation →

Performance Optimization

Caching Strategy

Multi-level caching:

Browser cache - Static assets (24 hours)
CDN cache - Global edge caching
API cache - Redis (5 minutes)
Database cache - Query results

Async Processing

Long-running tasks are async:

# Submit task
task = agent.run_async("long_task.py")

# Check status
status = task.status()  # "pending", "running", "completed"

# Get result when ready
result = task.result()  # Blocks until complete

Parallel Execution

Run multiple tasks simultaneously:

# Run 10 tasks in parallel
tasks = [
    agent.run_async(f"task_{i}.py")
    for i in range(10)
]

# Wait for all to complete
results = [task.result() for task in tasks]

Extensibility

Plugin System

Extend Auteryn with plugins:

# Custom tool plugin
class MyTool(AuterynTool):
    def execute(self, params):
        # Your logic here
        return result

# Register plugin
agent.register_tool(MyTool())

Custom Skills

Create reusable skills:

name: "My Custom Skill"
instructions: |
  Step-by-step instructions
tools_required:
  - github
  - slack

Webhooks

Integrate with external systems:

# Receive webhook
@agent.webhook("/my-webhook")
def handle_webhook(data):
    # Process webhook
    return {"status": "processed"}

Roadmap

Coming Soon

Multi-agent orchestration - Agents working together
GPU support - For ML workloads (Enterprise)
Custom regions - Deploy in your preferred region
VPC peering - Connect to your private network
Audit logs API - Programmatic access to logs

Under Consideration

On-premise deployment - Self-hosted option
Air-gapped environments - For high-security needs
Custom LLM models - Bring your own model
Edge deployment - Run agents closer to users

View changelog →

Resources

Questions?

What cloud provider do you use? AWS (primary), with multi-cloud support coming.
Can I deploy on-premise? Enterprise customers can request on-premise deployment.
How do you ensure uptime? Multi-region deployment, automatic failover, 24/7 monitoring.
What about data residency? EU and US regions available. Custom regions for Enterprise.
Can I audit the infrastructure? Enterprise customers get access to SOC 2 reports and can request audits.

View all FAQs →