AWS Serverless Deployment

Deploy agents to AWS Lambda for auto-scaling, serverless execution.

Architecture

Prerequisites

• AWS CLI configured
• AWS credentials with Lambda/API Gateway permissions
• Agent Kernel with AWS extras: pip install agentkernel[aws]

Deployment

1. Install Dependencies

pip install agentkernel[aws,openai]

2. Configure

Refer to Terraform modules for configuration details.

3. Deploy

terraform init && terraform deploy

Lambda Handler

Your agent code remains the same, just import the Lambda handler:

from agents import Agent as OpenAIAgent
from agentkernel.openai import OpenAIModule
from agentkernel.aws import Lambda

agent = OpenAIAgent(name="assistant", ...)
OpenAIModule([agent])

handler = Lambda.handler
## API Endpoints

After deployment:

POST https://{api-id}.execute-api.us-east-1.amazonaws.com/prod/chat

Body:

{
  "agent": "assistant",
  "message": "Hello!",
  "session_id": "user-123"
}

Cost Optimization

Lambda Configuration

Memory: 512 MB Timeout: 30

Refer to Terraform modules to update the configurations.

Cold Start Mitigation

• Use provisioned concurrency for critical endpoints
• Keep Lambda warm with scheduled pings
• Optimize package size

Fault Tolerance

AWS Lambda deployment is inherently fault-tolerant with fully managed infrastructure.

Serverless Resilience by Design

Lambda provides built-in fault tolerance without any configuration:

Key Features:

• Multi-AZ execution automatically
• No infrastructure to manage
• Automatic scaling to demand
• Built-in retry mechanisms
• AWS handles all failures

Multi-AZ Architecture

Automatic Distribution:

• Lambda functions run across all availability zones
• No configuration required
• Survives entire AZ failures
• Transparent to application code

Benefits:

• Zone-level isolation
• Geographic redundancy
• No single point of failure
• AWS-managed failover

Automatic Retry Logic

Lambda automatically retries failed invocations:

Synchronous Invocations (API Gateway):

1st attempt → Failure
↓
2nd attempt (immediate retry)
↓
3rd attempt (immediate retry)
↓
Error response to client

Error Types with Automatic Retry:

• Function errors (unhandled exceptions)
• Throttling errors (429)
• Service errors (5xx)
• Timeout errors

Scaling and Availability

Infinite Scaling:

• Automatically scales to handle any number of requests
• Each request can run in isolated execution environment
• No capacity planning needed
• No manual intervention required

Concurrency Management:

# Optional: Reserve capacity for critical functions
resource "aws_lambda_function" "agent" {
  reserved_concurrent_executions = 100
}

# Optional: Provisioned concurrency (eliminates cold starts)
resource "aws_lambda_provisioned_concurrency_config" "agent" {
  provisioned_concurrent_executions = 10
}

Benefits:

• Handle traffic spikes automatically
• No over-provisioning
• Pay only for actual usage
• No capacity limits (within AWS quotas)

State Persistence with DynamoDB

Serverless-native state management with maximum resilience:

export AK_SESSION__TYPE=dynamodb
export AK_SESSION__DYNAMODB__TABLE_NAME=agent-kernel-sessions

DynamoDB Fault Tolerance:

• Multi-AZ replication - Data replicated across 3 AZs automatically
• Point-in-time recovery (PITR) - Restore to any second in last 35 days

tip

For detailed DynamoDB session configuration and best practices, see the Session Management documentation.

• Continuous backups - Automatic and continuous
• 99.999% availability SLA - Five nines uptime
• Global tables (optional) - Multi-region replication

Recovery Time and Point Objectives

Recovery Time Objective (RTO):

• Function failure: < 1 second (automatic retry)
• AZ failure: 0 seconds (multi-AZ by default)
• Region failure: Requires multi-region setup

Recovery Point Objective (RPO):

• DynamoDB: Continuous (synchronous multi-AZ replication)
• Data loss: 0 (with proper DynamoDB configuration)

Fault Tolerance Benefits

Compared to Traditional Servers:

• ✅ No server failures (serverless)
• ✅ No patching required (managed by AWS)
• ✅ No capacity planning
• ✅ Automatic scaling
• ✅ Built-in redundancy

Compared to ECS:

• ✅ Zero infrastructure management
• ✅ Infinite scaling
• ✅ Pay only for usage
• ⚠️ Higher latency (cold starts)
• ⚠️ 15-minute execution limit

Learn more about fault tolerance →

Session Storage

For serverless deployments, use DynamoDB or ElastiCache Redis for session persistence:

DynamoDB (Recommended for Serverless)

export AK_SESSION__TYPE=dynamodb
export AK_SESSION__DYNAMODB__TABLE_NAME=agent-kernel-sessions
export AK_SESSION__DYNAMODB__TTL=3600  # 1 hour

Benefits:

• Serverless, fully managed
• Auto-scaling
• No cold starts
• Pay-per-use
• AWS-native integration

Requirements:

• DynamoDB table with partition key session_id (String) and sort key key (String)
• Lambda IAM role with DynamoDB permissions (dynamodb:GetItem, dynamodb:PutItem, dynamodb:UpdateItem, dynamodb:DescribeTable)

ElastiCache Redis

export AK_SESSION__TYPE=redis
export AK_SESSION__REDIS__URL=redis://elasticache-endpoint:6379

Benefits:

• High performance
• Shared cache across functions

Note: Redis requires VPC configuration for Lambda, which can impact cold start times.

Monitoring

CloudWatch metrics automatically available:

• Invocation count
• Duration
• Errors
• Concurrent executions

Best Practices

• Use DynamoDB for session storage (serverless-native)
• Alternatively, use Redis for session storage if already using ElastiCache
• Set appropriate timeout (30-60s for LLM calls)

Example Deployment

See examples/aws-serverless