---
name: ak-cloud-deploy
description: >
  Deploy an Agent Kernel project to AWS, Azure, or GCP using Terraform modules, or to any
  Kubernetes cluster (on-prem, baremetal, EKS) using the official Helm chart.
  Supports serverless and containerized modes for all three clouds. AWS supports
  execution modes (rest_sync, rest_async, async, stream), queue-based scalable processing,
  custom API Gateway authorizers, EventBridge Scheduler for scheduled tasks, and SSM
  Parameter Store secret resolution (`ssm_enabled`).
  GCP supports Cloud Run serverless (scale-to-zero)
  and containerized (always-on) with Redis or Firestore session backends. Kubernetes runs
  the queue pipeline over NATS JetStream, Kafka, or SQS with KEDA autoscaling and an
  optional WebSocket gateway tier.
license: Apache-2.0
metadata:
  author: yaalalabs
  version: "0.9.3"
  category: user
---

# Deploy to Cloud

Use this skill to deploy your Agent Kernel project to AWS, Azure, or GCP.

## Instructions for the Agent

When the user wants cloud deployment, follow this workflow.

### Step 1: Identify the Project

Check for:
- `pyproject.toml` with `agentkernel` dependency
- agent entry file (`app.py`, `lambda.py`, or similar)
- `config.yaml`

If missing, suggest `ak-init` first.

### Step 2: Ask Deployment Questions

1. Target platform: AWS, Azure, GCP, or Kubernetes (on-prem / baremetal / EKS via the Helm chart; see the On-Prem / Kubernetes section)?
2. Runtime mode:
- Serverless
- Containerized
3. Execution pattern (AWS only):
- Synchronous HTTP (`rest_sync`, supports standard or queue/scalable mode; AWS serverless or containerized)
- Asynchronous REST (`rest_async`, queue/scalable mode; AWS serverless or containerized)
- WebSocket full-response (`async`, works with or without queue mode — `queue_mode = false` runs the agent inline, `queue_mode = true` enqueues to a separately-scalable Agent Runner) — AWS serverless or AWS containerized/ECS
- WebSocket token streaming (`stream`, works with or without queue mode, same as `async` above) — AWS serverless or AWS containerized/ECS; also available via SSE (`POST /api/v1/chat` with `execution.mode: stream`, no Terraform changes required) wherever the built-in FastAPI REST server runs: local/self-hosted, AWS ECS single-container REST, Azure Container Apps, GCP Cloud Run — not on AWS Lambda or Azure Functions, which use WebSocket instead
4. Scalability (AWS serverless only): standard or queue/scalable mode?
5. Session store: Redis, Valkey (AWS only), DynamoDB (AWS), Cosmos DB (Azure), Firestore (GCP)?
6. Security: custom authorizer required (AWS serverless only)?
7. Resource naming: a single `prefix` applied to every resource name (e.g. `myapp-dev-chat`).

### Step 3: Choose the Correct Terraform Module

Use official modules:
- AWS serverless: `yaalalabs/ak-serverless/aws`
- AWS containerized: `yaalalabs/ak-containerized/aws`
- Azure serverless: `yaalalabs/ak-serverless/azurerm`
- Azure containerized: `yaalalabs/ak-containerized/azurerm`
- GCP serverless: `yaalalabs/ak-serverless/google`
- GCP containerized: `yaalalabs/ak-containerized/google`

Use current module version (`0.9.3`) unless user requests another.

Kubernetes does not use Terraform: the Helm chart lives at `ak-deployment/ak-k8s/chart` in the
Agent Kernel repository and is published as an OCI artifact
(`oci://ghcr.io/yaalalabs/charts/agent-kernel`, versioned with each release). Install the
published chart, as in the Kubernetes section below, not a repository checkout.

All modules are provider-agnostic: they declare `required_providers` but do not configure them internally. Configure each provider (`aws`/`docker`, `azurerm`, or `google`/`google-beta`/`docker`) in the root module and pass it explicitly via the module's `providers = { ... }` argument, as shown in the examples below. Azure's containerized module builds and pushes its image via a nested submodule with its own internal `docker` provider, so no `docker` provider needs to be configured or passed by the caller there.

AWS-only features in this skill:
- `execution_mode`
- `queue_mode`
- `authorizer`

Azure examples use the provider module's standard top-level inputs (`function_name`, `package_path`, `container_port`, `publisher_email`) rather than AWS serverless execution modes.

GCP modules use Cloud Run for both serverless (scale-to-zero, `min_instance_count=0`) and containerized (always-on, `min_instance_count≥1`) modes. Both use `agentkernel.gcp.CloudRun` as the entry point.

### Step 4: Align App Dependencies and Session Config

When the user selects a session store, always update both app dependencies and `config.yaml`.

#### Redis sessions

- Dependency extras: include `redis`
- Typical dependency example:

```toml
dependencies = [
  "agentkernel[openai,api,redis]>=0.9.3"
]
```

- `config.yaml` session block:

```yaml
session:
  type: redis
  namespace: chat
  redis:
    host: ${REDIS_HOST}
    port: 6379
    db: 0
```

#### Valkey sessions (AWS)

[Valkey](https://valkey.io/) is the open-source, Linux Foundation-governed fork of Redis. It is
wire-compatible with Redis and available on AWS ElastiCache at a lower price point than the Redis
OSS engine. Agent Kernel treats it as a first-class session and response store backend on AWS.

- Dependency extras: include `valkey`
- Typical dependency example:

```toml
dependencies = [
  "agentkernel[openai,api,aws,valkey]>=0.9.3"
]
```

- `config.yaml` session block:

```yaml
session:
  type: valkey
  valkey:
    url: ${AK_SESSION__VALKEY__URL}  # valkeys:// for SSL
    ttl: 604800
    prefix: "ak:sessions:"
```

- Terraform provisions the cluster with `create_valkey_cluster = true` (serverless and
  containerized) and injects `AK_SESSION__VALKEY__URL` — `session.type: valkey` must still be set
  in `config.yaml` since the type itself is never injected.
- For queue-mode response storage, set `create_valkey_response_store = true` (serverless only) and
  `execution.response_store.type: valkey` in `config.yaml`.

#### DynamoDB sessions (AWS)

- Dependency extras: include `aws`
- Typical dependency example:

```toml
dependencies = [
  "agentkernel[openai,api,aws]>=0.9.3"
]
```

- `config.yaml` session block:

```yaml
session:
  type: dynamodb
  dynamodb:
    table_name: ${DYNAMODB_MEMORY_TABLE}
    region: ${AWS_REGION}
```

#### Cosmos DB sessions (Azure)

- Dependency extras: include `azure`
- Typical dependency example:

```toml
dependencies = [
  "agentkernel[openai,api,azure]>=0.9.3"
]
```

- `config.yaml` session block:

```yaml
session:
  type: cosmosdb
  cosmosdb:
    endpoint: ${AZURE_COSMOS_ENDPOINT}
    database_name: ${AZURE_COSMOS_DATABASE}
    container_name: ${AZURE_COSMOS_CONTAINER}
```

#### Firestore sessions (GCP)

- Dependency extras: include `gcp`
- Typical dependency example:

```toml
dependencies = [
  "agentkernel[openai,api,gcp]>=0.9.3"
]
```

- `config.yaml` session block:

```yaml
session:
  type: firestore
  firestore:
    collection_name: ${AK_SESSION__FIRESTORE__COLLECTION_NAME}
    project_id: ${PROJECT_ID}
    ttl: 604800
```

- The Terraform module injects `AK_SESSION__TYPE=firestore` and `AK_SESSION__FIRESTORE__COLLECTION_NAME` automatically when Firestore is enabled.
- A TTL policy must be set on the Firestore collection pointing to the `expiry_time` field for automatic document expiry.

If the Terraform module creates the backing store (for example `create_redis_cluster = true`, `create_valkey_cluster = true`, or `create_dynamodb_memory_table = true`), make sure output values are wired to the app environment variables used by `config.yaml`.

### Deploying Conversation Thread Storage

Conversation threads (`ak-add-capabilities`) deploy the same way sessions do: the app mounts
`AgentThreadRequestHandler` (which is what enables the feature; the self-hosted REST API is the only
surface that records threads — queue-mode runners and the other deployment adapters do not) and
declares `thread.type` in `config.yaml`, and Terraform provisions the backend, injecting only the
connection detail. Terraform never sets `AK_THREAD__TYPE` itself.

- **AWS (serverless + containerized)**: `create_dynamodb_thread_table = true` provisions a DynamoDB
  table (partition `session_id`, sort `sk`, TTL on `expiry_time`) and injects
  `AK_THREAD__DYNAMODB__TABLE_NAME`.
- **GCP (serverless + containerized)**: `create_firestore_thread_collection = true` requires
  `create_firestore_database = true`, reuses that database, and injects
  `AK_THREAD__FIRESTORE__COLLECTION_NAME`, `__PROJECT_ID`, and `__DATABASE_ID`. No new resource is
  provisioned — the collection is created on first write.
- **Redis / Valkey**: no dedicated Terraform flag — reuse whatever `create_redis_cluster` /
  `create_valkey_cluster` already provisions and declare `thread: {type: redis}` (or `valkey`) with
  the cluster URL in `config.yaml`.

Setting the Terraform flag *without* declaring `thread.type` in `config.yaml` silently leaves
threads on the non-durable in-memory backend (any `AK_THREAD__*` var materialises `AKConfig.thread`,
but `type` still defaults to `in_memory`) — always pair the flag with the matching `thread.type`.

### Deploying Scheduled Tasks

Scheduled tasks (`ak-add-capabilities`) deploy on the same two-step split: the app declares
`schedule.provider.type` and `schedule.store.type` in `config.yaml`, and Terraform provisions the
backends, injecting only the coordinates. Terraform never sets `AK_SCHEDULE__PROVIDER__TYPE` or
`AK_SCHEDULE__STORE__TYPE`.

**Scheduling requires queue mode** — occurrences are delivered into the input queue, so
`queue_mode = true` is mandatory on AWS. `enable_scheduling` without it is rejected.

- **AWS (serverless + containerized)**: `enable_scheduling = true` provisions an EventBridge Scheduler
  schedule group and the execution role Scheduler assumes to deliver triggers to the input queue,
  grants both the request/REST role and the agent-runner role `scheduler:CreateSchedule|UpdateSchedule|DeleteSchedule|GetSchedule`
  plus `iam:PassRole` on that role, and injects
  `AK_SCHEDULE__PROVIDER__EVENTBRIDGE__GROUP_NAME`, `__ROLE_ARN` and `__QUEUE_ARN`. It also flips the
  input queue to content-based deduplication, because Scheduler cannot set a
  `MessageDeduplicationId` (application senders are unaffected — their explicit id takes precedence).
- **AWS (serverless + containerized)**: `create_dynamodb_schedule_table = true` provisions a DynamoDB
  table (partition `task_id`, no sort key, no GSI, TTL on `expiry_time`) and injects
  `AK_SCHEDULE__STORE__DYNAMODB__TABLE_NAME`.
- **Redis / Valkey task store**: no dedicated Terraform flag — reuse whatever `create_redis_cluster` /
  `create_valkey_cluster` already provisions and declare `schedule: {store: {type: redis}}` (or
  `valkey`) with the cluster URL in `config.yaml`.
- **Azure / GCP**: no scheduling provider ships for these clouds yet. The `local` provider only works
  in a single always-on process with the `in_memory` transport and store, which no cloud deployment
  mode gives you.

```hcl
queue_mode                     = true
enable_scheduling              = true
create_dynamodb_schedule_table = true
```

```yaml
schedule:
  provider:
    type: eventbridge
  store:
    type: dynamodb
```

Setting the Terraform flags *without* declaring both types in `config.yaml` silently leaves scheduling
on the in-process `local` provider and `in_memory` store (any `AK_SCHEDULE__*` var materialises
`AKConfig.schedule`, but the types still default), while the provisioned group and table sit unused —
always pair the flags with the matching types. The reverse is safe: declaring the types without the
flags fails at startup with an `AKConfigError` on the missing `group_name` / `role_arn` / `queue_arn`.

The blocks above enable deferring and the agent tools. The `/api/v1/schedules` management routes are
**not** mounted from config — on ECS queue mode, pass the handler to the IO container's entrypoint,
where it is served alongside the queue-producing chat route:

```python
# app_rest_service.py
from agentkernel.aws import ECSIOHandler
from agentkernel.schedule import ScheduleRESTRequestHandler

if __name__ == "__main__":
    # add authoriser=... to scope listings to the caller; without one the routes are open
    ECSIOHandler.run(handlers=[ScheduleRESTRequestHandler()])
```

Remember to list the schedule paths in `gateway_endpoints` — the gateway only proxies paths it is
told about.

New outputs on both AWS stacks (null unless the matching flag is set): `schedule_group_name`,
`schedule_group_arn`, `scheduler_execution_role_arn`, `schedule_table_name`, `schedule_table_arn`.

### Deploying Secret Resolution (AWS SSM)

Secret resolution (`ak-add-capabilities`) follows the same split: the app declares
`secret.provider.type: aws_ssm` in `config.yaml` and resolves keys with
`SecretManager.current().get("OPENAI_API_KEY")`; Terraform grants read access and injects only the
deployment scope. Terraform never sets `AK_SECRET__PROVIDER__TYPE`.

- **AWS (serverless + containerized)**: `ssm_enabled = true` grants only the tier that runs the agent
  (the agent runner when `queue_mode = true`; otherwise the serverless request handler / containerized
  REST service) `ssm:GetParameter` on `arn:aws:ssm:<region>:<account>:parameter/ak/<prefix>/*` and
  injects `AK_SECRET__PREFIX = <prefix>` (the module's own `prefix`). The response handler and
  WebSocket connection handler never get SSM access, so resolve secrets in agent-runner code only.
- **Terraform does not create the parameters** — create each one yourself as a `SecureString`
  (AWS-managed `alias/aws/ssm` key) at `/ak/<prefix>/<key lowercased>`, e.g.
  `aws ssm put-parameter --name /ak/<prefix>/openai_api_key --type SecureString --value sk-...`.
- Drop the key from `environment_variables`: a set, non-empty environment variable always wins over
  SSM, so leaving `OPENAI_API_KEY` there bypasses the store.
- The app needs the `aws` extra (`agentkernel[aws]`) for the `aws_ssm` provider.

```hcl
ssm_enabled = true
```

```yaml
secret:
  provider:
    type: aws_ssm
  # prefix is injected by Terraform as AK_SECRET__PREFIX
```

```python
from agentkernel.secret import SecretManager
from agents import set_default_openai_key

set_default_openai_key(SecretManager.current().get("OPENAI_API_KEY"))
```

Declaring `aws_ssm` without `ssm_enabled` (and without setting `secret.prefix` yourself) fails at
startup with an `AKConfigError` on the missing prefix; with a prefix but no IAM grant, lookups raise
`SecretError` (AccessDenied). Setting `ssm_enabled` without declaring the provider leaves resolution on
the `env` provider. See `examples/aws-serverless/openai` and
`examples/aws-containerized/openai-dynamodb-scalable`.

## AWS Serverless (Lambda + API Gateway)

### Agent Code Pattern

Use Lambda handler entrypoint:

```python
from agentkernel.aws import Lambda
from agentkernel.openai import OpenAIModule

# Register agents with your framework module
OpenAIModule([...])

handler = Lambda.handler
```

### A) Basic Synchronous REST (`rest_sync`)

Use this for straightforward request/response APIs.

This is the single-Lambda pattern: use `request_handler` plus any `gateway_endpoints`. Do not add `agent_runner` or `response_handler` unless you are using queue mode.

```hcl
module "serverless_agents" {
  source  = "yaalalabs/ak-serverless/aws"
  version = "0.9.3"

  prefix               = var.prefix
  product_display_name = "AK Serverless"
  region               = var.region

  execution_mode = "rest_sync"

  request_handler = {
    function_name        = "chat-handler"
    function_description = "AK request handler"
    handler_path         = "lambda.handler"
    package_type         = "Image"
    package_path         = "../dist"
    timeout              = 45
    memory_size          = 256
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  gateway_endpoints = [
    { path = "app", method = "GET" },
    { path = "app_info", method = "POST" }
  ]
}
```

### B) Scalable Queue Mode (`rest_sync` or `rest_async`)

Use this for high throughput and long-running requests.

This is the multi-artifact pattern used by the scalable example: a request handler zip, an agent-runner image, and a response-handler zip.

Each Lambda can use one of three `package_type` values:


| `package_type` | Artifact source                    | Required field(s)                                    | What Terraform does                                                                                                                                                                                       |
| -------------- | ---------------------------------- | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `LocalZip`     | Local ZIP file or source directory | `package_path`                                       | Uses the Lambda module's local packaging support. No shared source bucket is managed by this module.                                                                                                      |
| `S3Zip`        | ZIP artifact stored in S3          | **Either** `package_path` **or** `lambda_package_s3` | If `package_path` is provided, this module creates/uses a shared source bucket, uploads the ZIP, and deploys from S3. If `lambda_package_s3` (`{ bucket, key, version_id? }`) is provided, Terraform uses the existing S3 object directly; set `version_id` (from a versioned bucket) so re-uploading changed code redeploys the function. |
| `Image`        | Container image in ECR             | **Either** `ecr_image_uri` **or** `package_path`     | If `ecr_image_uri` is provided, Terraform uses the existing image. If `package_path` is provided, Terraform builds and pushes an image to ECR and deploys it.                                             |


`package_path` and `lambda_package_s3`/`ecr_image_uri` are mutually exclusive — set only one.

**Development / local build** (build artifacts locally before running `terraform apply`):

```hcl
module "serverless_agents" {
  source  = "yaalalabs/ak-serverless/aws"
  version = "0.9.3"

  prefix             = var.prefix
  region             = var.region
  product_display_name = "AK Scalable REST"

  queue_mode     = true
  execution_mode = "rest_sync" # can also be "rest_async"

  create_dynamodb_memory_table   = true
  create_dynamodb_response_store = true

  # Uncomment with `schedule: {provider: {type: eventbridge}, store: {type: dynamodb}}` in config.yaml
  # enable_scheduling              = true
  # create_dynamodb_schedule_table = true

  request_handler = {
    function_name        = "request-handler"
    function_description = "Receives REST requests"
    handler_path         = "lambda_request_handler.handler"
    package_type         = "LocalZip"
    package_path         = "../dist_request_handler.zip"
    timeout              = 45
    memory_size          = 256
    environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
    }
  }

  agent_runner = {
    function_name        = "agent-runner"
    function_description = "Processes queued requests"
    handler_path         = "lambda_agent_runner.handler"
    package_type         = "Image"
    package_path         = "../dist_agent_runner"
    timeout              = 45
    memory_size          = 512
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  response_handler = {
    function_name        = "response-handler"
    function_description = "Handles async response completion"
    handler_path         = "lambda_response_handler.handler"
    package_type         = "LocalZip"
    package_path         = "../dist_response_handler.zip"
    timeout              = 45
    memory_size          = 256
  }

  queue_config = {
    input_queue_visibility_timeout         = 60
    input_queue_max_receive_count          = 3
    input_queue_create_dlq                 = false
    input_queue_message_retention_seconds  = 300
    output_queue_visibility_timeout        = 60
    output_queue_max_receive_count         = 3
    output_queue_create_dlq                = false
    output_queue_message_retention_seconds = 300
    batch_size                             = 10
    maximum_batching_window_in_seconds     = 0
  }
}
```

**Production: external artifact sources (S3 / ECR)**

Build and push artifacts in CI/CD, then point Terraform at them so `terraform apply` contains no local build step:

```hcl
  request_handler = {
    function_name    = "request-handler"
    handler_path     = "lambda_request_handler.handler"
    package_type     = "S3Zip"
    lambda_package_s3 = {
      bucket     = "my-lambda-packages-bucket"
      key        = "dist_request_handler.zip"
      version_id = "<object-version-id>" # from your versioned bucket → redeploys work (#548)
    }
    timeout     = 45
    memory_size = 256
    environment_variables = { OPENAI_API_KEY = var.openai_api_key }
  }

  agent_runner = {
    function_name = "agent-runner"
    handler_path  = "lambda_agent_runner.handler"
    package_type  = "Image"
    ecr_image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/agent-runner:latest"
    timeout       = 45
    memory_size   = 512
    environment_variables = { OPENAI_API_KEY = var.openai_api_key }
  }

  response_handler = {
    function_name    = "response-handler"
    handler_path     = "lambda_response_handler.handler"
    package_type     = "S3Zip"
    lambda_package_s3 = {
      bucket     = "my-lambda-packages-bucket"
      key        = "dist_response_handler.zip"
      version_id = "<object-version-id>" # from your versioned bucket → redeploys work (#548)
    }
    timeout     = 45
    memory_size = 256
  }
```

See [examples/aws-serverless/scalable-openai](https://github.com/yaalalabs/agent-kernel/tree/develop/examples/aws-serverless/scalable-openai) for a complete working example of this pattern.

Terraform only replaces a Lambda's code when `s3_bucket`, `s3_key`, `s3_object_version`, or `source_code_hash` changes. If you re-upload to the **same** S3 key in an **unversioned** bucket, none of those change and the function keeps running the old code. To make `S3Zip` redeploys reliable when pointing at your own artifact (`lambda_package_s3`): enable versioning on the bucket holding the ZIPs, then pass the uploaded object's version through `lambda_package_s3.version_id`.

**Queue mode `config.yaml`** (bundled into every Lambda package — `execution.mode`, queue URLs, table names, and `max_receive_count` are all injected automatically by Terraform as environment variables; only set values that are NOT injected):

```yaml
# For rest_sync or rest_async
execution:
  response_store:
    type: dynamodb  # or redis / valkey — not injected, must be set here
    retry_count: 5
    delay: 5
session:
  type: dynamodb  # or redis / valkey — not injected, must be set here
```

- `rest_sync`: request handler sends to queue, polls the response store until the result is available, then returns it synchronously.
- `rest_async`: request handler returns `{status: "ACCEPTED", request_id}` immediately; the client polls `GET /api/{version}/{endpoint}` with `{"request_id": "<id>"}` in the JSON body.
- SQS visibility timeout for each queue **must be >= the corresponding Lambda timeout**.

**Required `pyproject.toml` extras for queue mode**:

```toml
dependencies = [
  "agentkernel[openai,api,aws]>=0.9.3"  # include 'redis' if using Redis, or 'valkey' if using Valkey session/response store
]
```

### C) WebSocket Async (`async`)

Use this for realtime bidirectional interactions.

This follows the current websocket example shape: the request handler stays on the module's top-level Lambda inputs, then queue workers and WebSocket handlers are added via nested blocks.

```hcl
module "serverless_agents" {
  source  = "yaalalabs/ak-serverless/aws"
  version = "0.9.3"

  prefix               = var.prefix
  region               = var.region
  product_display_name = "AK WebSocket Serverless Example"

  queue_mode     = true
  execution_mode = "async"

  create_redis_cluster           = true
  create_dynamodb_response_store = true

  request_handler = {
    function_name        = "request-handler"
    function_description = "Receives WebSocket requests"
    handler_path         = "lambda_request_handler.handler"
    package_type         = "LocalZip"
    package_path         = "../dist_request_handler.zip"
    timeout              = 45
    memory_size          = 256
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  agent_runner = {
    function_name        = "agent-runner"
    function_description = "Processes chat tasks"
    handler_path         = "lambda_agent_runner.handler"
    package_type         = "Image"
    package_path         = "../dist_agent_runner"
    timeout              = 45
    memory_size          = 512
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  response_handler = {
    function_name        = "response-handler"
    function_description = "Sends responses to connections"
    handler_path         = "lambda_response_handler.handler"
    package_type         = "LocalZip"
    package_path         = "../dist_response_handler.zip"
    timeout              = 45
    memory_size          = 256
  }

  ws_connection_handler = {
    function_name        = "ws-connection-handler"
    function_description = "Handles $connect/$disconnect"
    handler_path         = "lambda_ws_connection_handler.handler"
    package_path         = "../dist_ws_connection_handler.zip"
    timeout              = 45
    memory_size          = 256
  }

  ws_routes = [
    { route = "app" },
    { route = "app_info" }
  ]
}
```

**WebSocket `lambda_ws_connection_handler.py`** — implement `AuthValidator` to validate the bearer token on `$connect`:

```python
import jwt
import os
from agentkernel.auth import AuthValidator, ValidationResult
from agentkernel.aws import WebsocketConnectionHandler


class MyAuthValidator(AuthValidator):
    def validate(self, token: str) -> ValidationResult:
        try:
            payload = jwt.decode(
                token,
                os.environ["JWT_SECRET"],
                algorithms=["HS256"],
                issuer=os.environ["JWT_ISSUER"],
                audience=os.environ["JWT_AUDIENCE"],
            )
            user_id = payload.get("userId", "")
            if user_id:
                return ValidationResult(is_valid=True, claims={"userId": user_id})
            return ValidationResult(is_valid=False, error_msg="userId claim missing")
        except Exception as e:
            return ValidationResult(is_valid=False, error_msg=str(e))


handler = WebsocketConnectionHandler.set_auth_validator(MyAuthValidator()).handler
```

**WebSocket `lambda_request_handler.py`** — routes are registered by **route name** (no leading slash, no HTTP method) because the WebSocket API uses `$request.body.route` for dispatch:

```python
import json
from agentkernel.aws import Lambda


@Lambda.register("app")               # WebSocket route name, no slash/method
def app_handler(event, context):
    return {"response": "Hello from app route"}


@Lambda.register("app_info")
def app_info_handler(event, context):
    payload = json.loads(event.get("body") or "{}")
    return {"response": "Hello from app_info route"}


handler = Lambda.handler
```

Contrast with REST mode where routes use a leading slash and HTTP method:
```python
@Lambda.register("/app", method="GET")    # REST mode: path + method
```

**WebSocket `config.yaml`** (bundled into every Lambda package — `execution.mode`, `websocket_api.chat_route`, queue URLs, and connection table name are all injected automatically by Terraform; only set values that are NOT injected):

```yaml
session:
  type: redis                   # or valkey / dynamodb — not injected, must be set here
  redis:
    prefix: "ak:myapp:"
```

- Authentication is **mandatory** for WebSocket mode. `AuthValidator.validate()` must return `claims["userId"]`; connections without a valid token are rejected at `$connect`.
- The `authorizer` Terraform block is not supported in WebSocket mode — leave it unset.
- Only `LocalZip` is supported for `ws_connection_handler.package_path`.
- `ws_routes` custom routes must have a matching `@Lambda.register("route_name")` entry. `ws_chat_route` is the built-in AI chat route — it is handled internally by the framework and does **not** need a `@Lambda.register` entry.

**Required `pyproject.toml` extras for WebSocket mode**:

```toml
dependencies = [
  "agentkernel[openai,api,aws,redis,auth]>=0.9.3"
]
```

### D) WebSocket Token Streaming (`stream`)

Use this when the client should receive each stream event as soon as it is produced, instead of waiting for the full response.

Same Terraform shape as WebSocket Async (`request_handler`, `agent_runner`, `response_handler`, `ws_connection_handler`, `ws_routes`) — only `execution_mode` changes:

```hcl
module "serverless_agents" {
  source  = "yaalalabs/ak-serverless/aws"
  version = "0.9.3"

  prefix               = var.prefix
  region               = var.region
  product_display_name = "AK Streaming WebSocket Example"

  queue_mode     = true
  execution_mode = "stream"

  create_redis_cluster           = true
  # create_redis_response_store, create_valkey_response_store, and create_dynamodb_response_store must stay false/unset:
  # WebSocket modes (async/stream) push responses over the connection and Terraform
  # validation fails if a response store is enabled for them.

  request_handler       = { ... } # same shape as async mode
  agent_runner          = { ... } # runs ServerlessStreamAgentRunner, streams chunks to output queue
  response_handler      = { ... } # broadcasts each chunk as a STREAM_CHUNK message
  ws_connection_handler = { ... }
  ws_routes             = [ { route = "app" }, { route = "app_info" } ]
}
```

**`config.yaml`** — the only required setting beyond WebSocket async mode is the execution mode itself:

```yaml
execution:
  mode: stream
```

This makes `ServerlessAgentRunner.handle()` dispatch to `ServerlessStreamAgentRunner` (queue mode) — no code change needed in the agent runner Lambda beyond the standard `Lambda.handler` entrypoint.

**Message format** — clients receive a sequence of `STREAM_CHUNK` messages instead of one `CHAT_RESPONSE`:

```json
{"type": "STREAM_CHUNK", "delta": "Hello", "done": false, "session_id": "user-1"}
{"type": "STREAM_CHUNK", "delta": " world", "done": false, "session_id": "user-1"}
{"type": "STREAM_CHUNK", "delta": "!", "done": true, "session_id": "user-1"}
```

On an unrecoverable error, the final chunk carries `error` instead of `delta`, with `done: true`.

- Queue disabled (`queue_mode = false`): the request handler Lambda streams tokens directly to the WebSocket client without SQS.
- `create_redis_response_store` / `create_valkey_response_store` / `create_dynamodb_response_store` must be `false` for `stream` (same constraint as `async`) — Terraform validation enforces this.
- See [examples/aws-serverless/streaming-openai](https://github.com/yaalalabs/agent-kernel/tree/develop/examples/aws-serverless/streaming-openai) for a complete working example.

**Containerized / direct streaming (no Terraform WebSocket setup)**: wherever the built-in FastAPI REST server runs — local/self-hosted, AWS ECS single-container REST, Azure Container Apps, GCP Cloud Run — SSE event streaming can be enabled by setting `execution.mode: stream` in `config.yaml`. `POST /api/v1/chat` and `/api/v1/chat-multipart` then return `text/event-stream` responses instead of JSON — no queue or WebSocket infrastructure is required for this mode. Not available on AWS Lambda or Azure Functions (serverless), which use WebSocket for streaming instead.

### E) API Gateway Custom Authorizer (AWS)

If the user needs token verification, include `authorizer` block:

```hcl
authorizer = {
  description           = "API Gateway Lambda Authorizer"
  function_name         = "gateway-authorizer"
  handler_path          = "lambda_auth.handler"
  package_path          = "../dist_auth.zip"
  package_type          = "LocalZip"
  result_ttl_in_seconds = 0
  environment_variables = {
    SOME_OTHER_KEY = "Some Other Value"
  }
}
```

## AWS Containerized (ECS/Fargate)

### A) Basic Mode (single container, direct execution)

```python
from agentkernel.api import RESTAPI
from agentkernel.openai import OpenAIModule

OpenAIModule([...])

if __name__ == "__main__":
    RESTAPI.run()
```

```hcl
module "containerized_agents" {
  source  = "yaalalabs/ak-containerized/aws"
  version = "0.9.3"

  prefix               = var.prefix
  region               = var.region
  product_display_name = "AK ECS Deployment"

  rest_service = {
    package_path  = "../dist"
    container_port = 8000
    desired_count  = 2
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  create_dynamodb_memory_table = true
}
```

### B) Scalable Queue Mode (two containers)

Use this for high-throughput or long-running agents. Two separate ECS services share SQS queues:

- **IO container** (`ECSIOHandler`) — Thread 1: FastAPI REST API; Thread 2: output queue consumer → DynamoDB / WebSocket. Thread 2 is actually `queues.output.no_of_consumers` (default 5) parallel polling threads.
- **Agent Runner container** (`ECSAgentRunner`) — runs `queues.input.no_of_consumers` (default 5) parallel threads, each polling the input queue, executing the agent, and putting the result on the output queue.

**`app_rest_service.py`** (IO container entrypoint — NO agent definitions here):

```python
from agentkernel.aws import ECSIOHandler

runner = ECSIOHandler.run

if __name__ == "__main__":
    runner()
```

Pass `auth_validator=MyAuthValidator()` to secure the REST routes: `ECSIOHandler.run` binds it
onto every REST route via `AWSRestAPI.add_auth_handlers` (the same Bearer-token mechanism as
`RESTAPI.add_auth_handlers` in Basic Mode); omit it to leave the REST routes unauthenticated.

**`app_agent_runner.py`** (Agent Runner container entrypoint):

```python
from agentkernel.aws import ECSAgentRunner
from agentkernel.openai import OpenAIModule

OpenAIModule([...])  # register agents here only

handler = ECSAgentRunner.run

if __name__ == "__main__":
    handler()
```

**`config.yaml`** (same file included in both images — queue URLs and table names are injected by Terraform; `batch_size` is Terraform-only, never set here):

```yaml
execution:
  queues:
    type: sqs              # mandatory in a declared queues block: it is what selects the transport
    input:
      no_of_consumers: 5   # parallel input-poll threads in the Agent Runner container (default 5)
    output:
      no_of_consumers: 5   # parallel output-poll threads in the IO container (default 5)
  response_store:
    type: dynamodb
    retry_count: 30
    delay: 2
session:
  type: dynamodb  # or redis / valkey (create_redis_cluster / create_valkey_cluster)
```

**Terraform:**

```hcl
module "containerized_agents" {
  source  = "yaalalabs/ak-containerized/aws"
  version = "0.9.3"

  prefix        = var.prefix
  region        = var.region

  rest_service = {
    package_path  = "../dist-rest-service"
    cpu           = 512
    memory        = 1024
    desired_count = 2
    command       = ["python", "app_rest_service.py"]
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  queue_mode = true
  execution_mode   = "rest_sync"   # or "rest_async"

  # Uncomment with `schedule: {provider: {type: eventbridge}, store: {type: dynamodb}}` in config.yaml
  # enable_scheduling              = true
  # create_dynamodb_schedule_table = true

  queue_config = {
    input_queue_visibility_timeout  = 120
    output_queue_visibility_timeout = 60
    input_queue_create_dlq          = true
    output_queue_create_dlq         = true
  }

  # Agent Runner container — separate image with agent definitions
  agent_runner = {
    cpu           = 1024
    memory        = 2048
    desired_count = 1
    package_path  = "../dist-agent-runner"
    command       = ["python", "app_agent_runner.py"]
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  # Optional: auto-scale Agent Runner based on Input Queue depth
  scaling_config = {
    enabled            = true
    min_count          = 1
    max_count          = 10
    backlog_target     = 5
    scale_in_cooldown  = 180
    scale_out_cooldown = 60
  }

  create_dynamodb_memory_table = true
}
```

**Required `pyproject.toml` extras:**

```toml
dependencies = [
  "agentkernel[openai,api,aws]>=0.9.3"
]
```

**Key rules:**
- Agent definitions (`OpenAIModule([...])`) go in `app_agent_runner.py` only — never in `app_rest_service.py`.
- `ECSIOHandler` starts two threads via `ThreadRunner`; if the output-consumer pool crashes, sibling consumer threads finish their in-flight message first (graceful drain via a shared `shutdown_event`), then the container exits (`os._exit(1)`) so ECS can restart it. The REST API thread doesn't participate in the drain — it's just terminated at that point.
- `ECSAgentRunner` and `ECSOutputConsumer` both extend `ECSSQSConsumer` (itself a `RawQueueConsumer`, the same base `LambdaSQSConsumer` extends); extend either class to customise message processing.

### C) WebSocket Mode (`async` / `stream`)

A WebSocket API Gateway proxies frames to the ECS REST service via a VPC Link V1 + internal NLB
in front of the existing ALB. Supports both **direct** (`queue_mode = false`, one ECS service,
agent runs inline) and **queue** (`queue_mode = true`, same two-container split as section B,
with the REST/IO service enqueueing chat frames and its output-queue consumer pushing replies
back over the socket) variants. `execution_mode = "async"` delivers the full reply as one
`CHAT_RESPONSE` push; `execution_mode = "stream"` delivers it token-by-token as a sequence of
`STREAM_CHUNK` pushes (terminated by a chunk with `"done": true`) — in queue mode
`ECSAgentRunner.run()` dispatches to `ECSStreamAgentRunner`, which fans out one Output Queue
message per chunk instead of one for the full reply; in direct mode the chat route runs
`ChatService.process_stream_chat_async()` and broadcasts each chunk inline. See
`examples/aws-containerized/openai-stream` (direct mode) or
`examples/aws-containerized/openai-stream-queue-mode` (queue mode) for full streaming examples.

**`app.py`** (direct/single-container variant; register custom routes before calling `run()`):

```python
from agentkernel.aws import AWSWebsocketAPI
from agentkernel.auth import AuthValidator, ValidationContext, ValidationResult
from agentkernel.openai import OpenAIModule
from typing import Optional

OpenAIModule([...])

class CustomAuthValidator(AuthValidator):
    def validate(self, token: str, context: Optional[ValidationContext] = None) -> ValidationResult:
        # userId claim is mandatory — it's how a connection maps to a user for reply routing
        ...
        return ValidationResult(is_valid=True, claims={"userId": "userId-claim-value"})

@AWSWebsocketAPI.register("status")  # bare route name, no leading slash, no HTTP verb
async def status(ctx: dict) -> dict:
    # ctx = {"message": <raw JSON body dict>, "user_id": ...} — no BaseRequest schema imposed
    return {"status": "OK", "user_id": ctx["user_id"]}  # dict return -> broadcast as SYSTEM_RESPONSE; None -> no broadcast

if __name__ == "__main__":
    # Direct mode calls AWSWebsocketAPI directly — NOT ECSIOHandler, which always starts a
    # second thread polling an output queue that doesn't exist in direct (non-queue) mode.
    AWSWebsocketAPI.set_auth_handler(auth_validator=CustomAuthValidator()).run()
```

Queue mode is different: it splits into `app_rest_service.py` (`ECSIOHandler.run(auth_validator=...)`
— correct here, since `ECSIOHandler` also starts the output-queue consumer thread — custom routes
registered here, no agent definitions) and `app_agent_runner.py` (`ECSAgentRunner.run`,
`OpenAIModule([...])` here only) — identical shape to section B.

**Terraform:**

```hcl
module "containerized_agents" {
  source  = "yaalalabs/ak-containerized/aws"
  version = "0.9.3"

  providers = { aws = aws, docker = docker }

  prefix        = var.prefix
  region        = var.region
  vpc_id        = var.vpc_id
  private_subnet_ids = var.private_subnet_ids

  rest_service = {
    package_path   = "../dist"
    container_port = 8000
    environment_variables = {
      OPENAI_API_KEY = var.openai_api_key
    }
  }

  queue_mode     = false          # or true for the two-container queue variant (see section B)
  execution_mode = "async"        # "stream" delivers the reply as STREAM_CHUNK messages instead
  ws_chat_route  = "chat"         # optional, defaults to "chat"
  ws_routes = [                   # every @AWSWebsocketAPI.register(...) route must be listed here too
    { route = "status" },
  ]

  create_dynamodb_memory_table = true
}
```

**Key rules:**
- Auth is **mandatory**: `AuthValidator.validate()` must resolve a `userId` claim, or the framework
  raises at `$connect` construction time. There is no API Gateway authorizer for WebSocket mode.
- Every custom route needs both a Python `@AWSWebsocketAPI.register("name")` decorator **and** a
  matching Terraform `ws_routes = [{ route = "name" }]` entry — Python cannot create the API
  Gateway route/integration, so both sides must declare it. `ws_chat_route` is Terraform-only —
  it just names the API Gateway route key that forwards to the container's hardcoded `/ws/chat`
  endpoint; the container never reads it from config, and renaming it needs no Python change.
- The DynamoDB response store is simply never created in WebSocket mode (no `response_store`
  input to set) — replies are always pushed over the connection instead. `gateway_endpoints`
  specifically has a Terraform validation rule rejecting it in `async`/`stream` modes.
- Client wire format: `{"route": "chat", "body": {...}}` — the WebSocket API's
  `route_selection_expression` is `$request.body.route`.

## Azure Serverless (Functions + APIM)

### Agent Code Pattern

```python
from agentkernel.azure import AzureFunctions
from agentkernel.openai import OpenAIModule

OpenAIModule([...])

handler = AzureFunctions.handler
```

### Terraform Example

```hcl
module "serverless_agents" {
  source  = "yaalalabs/ak-serverless/azurerm"
  version = "0.9.3"

  prefix               = var.prefix
  region               = var.region
  resource_group_name  = var.resource_group_name
  publisher_email      = var.publisher_email
  product_display_name = "AK Azure Functions Example"

  function_name        = "openai-agents"
  function_description = "Agent Kernel OpenAI Sample Azure Function"
  module_type          = "python"
  package_path         = "../dist.zip"
  create_redis_cluster = true

  create_cosmosdb_cluster = false

  environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
  }

  gateway_endpoints = [
    {
      function_name = "AgentFunction"
      path          = "/chat"
      method        = "POST"
    },
    {
      function_name = "CustomFunction"
      path          = "/custom"
      method        = "POST"
    }
  ]
}
```

## Azure Containerized (Container Apps + APIM)

### Terraform Example

```hcl
module "containerized_agents" {
  source  = "yaalalabs/ak-containerized/azurerm"
  version = "0.9.3"

  prefix               = var.prefix
  region               = var.region
  resource_group_name  = var.resource_group_name
  publisher_email      = var.publisher_email
  package_path         = "../dist"
  product_display_name = "AK Azure Container Apps"

  container_port = 8000

  create_cosmosdb_cluster = true

  environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
  }
}
```

## GCP Serverless (Cloud Run — scale-to-zero)

### Agent Code Pattern

```python
from agentkernel.gcp import CloudRun
from agentkernel.openai import OpenAIModule

OpenAIModule([...])

@CloudRun.register("/app", method="GET")
def app_handler() -> dict:
    return {"status": "ok"}

def main() -> None:
    CloudRun.run()
```

### Terraform Example (with Redis sessions)

```hcl
module "serverless_agent" {
  source  = "yaalalabs/ak-serverless/google"
  version = "0.9.3"

  providers = { google = google, google-beta = google-beta, docker = docker }

  project_id           = var.project_id
  region               = var.region
  prefix               = var.prefix
  product_display_name = "AK GCP Serverless"

  package_path = "${path.module}/../dist"

  create_redis_cluster = true

  environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
  }

  gateway_endpoints = [
    { path = "app", method = "GET", overwrite_path = "/app" },
    { path = "app_info", method = "POST", overwrite_path = "/app_info" }
  ]
}
```

### Terraform Example (with Firestore sessions)

```hcl
module "serverless_agent" {
  source  = "yaalalabs/ak-serverless/google"
  version = "0.9.3"

  providers = { google = google, google-beta = google-beta, docker = docker }

  project_id           = var.project_id
  region               = var.region
  prefix               = var.prefix
  product_display_name = "AK GCP Serverless Firestore"

  package_path = "${path.module}/../dist"

  create_firestore_db = true

  environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
  }
}
```

The module injects `AK_SESSION__TYPE=firestore` and `AK_SESSION__FIRESTORE__COLLECTION_NAME` automatically when `create_firestore_db = true`.

**Required `pyproject.toml` extras:**

```toml
dependencies = [
  "agentkernel[openai,api,gcp]>=0.9.3"      # for Firestore sessions
  # or: "agentkernel[openai,api,redis]>=0.9.3"  # for Redis sessions
]
```

## GCP Containerized (Cloud Run — always-on)

### Agent Code Pattern

Same as GCP Serverless — use `agentkernel.gcp.CloudRun`:

```python
from agentkernel.gcp import CloudRun
from agentkernel.openai import OpenAIModule

OpenAIModule([...])

def main() -> None:
    CloudRun.run()
```

### Terraform Example

```hcl
module "containerized_agent" {
  source  = "yaalalabs/ak-containerized/google"
  version = "0.9.3"

  providers = { google = google, google-beta = google-beta, docker = docker }

  project_id           = var.project_id
  region               = var.region
  prefix               = var.prefix
  product_display_name = "AK GCP Containerized"

  package_path       = "${path.module}/../dist"
  min_instance_count = 1   # always-on — no cold starts
  container_port     = 8000

  create_redis_cluster = true

  environment_variables = {
    OPENAI_API_KEY = var.openai_api_key
  }
}
```

**Key difference from GCP Serverless:** `min_instance_count = 1` keeps at least one instance running at all times, eliminating cold starts.

## On-Prem / Kubernetes (Helm Chart)

Deploys the queue pipeline to any Kubernetes cluster: an `io-handler` Deployment (REST API +
Response Handler), an `agent-runner` Deployment (the consumers executing the agents), and an
optional `ws-gateway` Deployment for WebSocket modes. Backing services (Valkey, NATS) are
condition-gated dependencies of the chart; flavors (dev, baremetal, EKS) are values files.

Reference: the chart at `ak-deployment/ak-k8s/` and the end-to-end example at
`examples/k8s/openai-queue-mode/` in the Agent Kernel repository.

**Entry files** (one image per component; the chart's `command` defaults match these names):

```python
# app_io_handler.py: the io-handler image
from agentkernel.pipeline import IOHandler

def main():
    IOHandler.run()

if __name__ == "__main__":
    main()
```

```python
# app_agent_runner.py: the agent-runner image (registers the agent modules)
from agentkernel.openai import OpenAIModule
from agentkernel.pipeline import AgentRunner
from agents import Agent

# ... define agents ...
OpenAIModule([...])

def main():
    AgentRunner.run()

if __name__ == "__main__":
    main()
```

```python
# app_ws_gateway.py: only for async/stream modes
from agentkernel.pipeline import WebSocketGateway

def main():
    WebSocketGateway.run(auth_validator=YourAuthValidator())  # claims must include 'userId'

if __name__ == "__main__":
    main()
```

**Config split:** the image's `config.yaml` declares WHAT runs (`execution.mode`, agents,
logging); the chart injects WHERE it runs (broker, response store, session store) as `AK_*`
env vars that override matching `config.yaml` fields. Point `execution.queues.type` at
`nats` (recommended on-prem), `kafka`, or `sqs`, and use a shared response/session store
(`valkey`) on any multi-pod topology.

**Images:** `python:3.12-slim` base, dependencies staged with pip's `--target` layout, one
Dockerfile per component (mirror `examples/k8s/openai-queue-mode/deploy/package.sh`, which
also cross-installs Linux wheels so builds work from macOS).

**Install:**

```bash
helm pull oci://ghcr.io/yaalalabs/charts/agent-kernel --version 0.9.3 --untar   # unpacks the flavor values files
helm install ak oci://ghcr.io/yaalalabs/charts/agent-kernel --version 0.9.3 \
  -f agent-kernel/values-dev.yaml \
  --set ioHandler.image.repository=<io image> \
  --set agentRunner.image.repository=<runner image> --set image.tag=<tag> \
  --set 'extraEnv[0].name=OPENAI_API_KEY' \
  --set 'extraEnv[0].valueFrom.secretKeyRef.name=openai' \
  --set 'extraEnv[0].valueFrom.secretKeyRef.key=api-key'
```

The chart version tracks the Agent Kernel release (the pin above is the current one); the
flavor values files ship inside the chart, so no repository checkout is needed.
`ak-deployment/ak-k8s/chart` in the repository is for developing the chart itself.

- `values-dev.yaml`: micro-clusters (k3d/kind/microk8s/k3s), single replicas, auto-provisioned
  JetStream, port-forward entry; also documents the single-process profile (one pod,
  `in_memory` transport, zero backing services).
- `values-baremetal.yaml`: Envoy Gateway + MetalLB + cert-manager, NACK-managed JetStream
  objects (`autoProvision: false`), OpenEBS hostpath storage.
- `values-eks.yaml`: AWS Load Balancer Controller gateway classes, EBS gp3, Pod Identity;
  `sqs`, `kafka`, and `nats` all valid.
- WebSocket modes: `--set execution.mode=stream --set wsGateway.enabled=true` plus a
  `wsGateway.auth.token`; the gateway needs a shared session store for its connection table.
- Autoscaling: `keda.enabled=true` scales the agent-runner on queue depth (KEDA prerequisite).

**Verify:**

```bash
kubectl port-forward service/ak-agent-kernel-io 8000:80
curl -s -X POST http://localhost:8000/api/v1/chat \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "Hello", "session_id": "s1", "agent": "<agent>"}'
```

**Teardown:** `helm uninstall ak` (cluster prerequisites like Gateway API CRDs, KEDA, NACK,
and Strimzi are installed per cluster, never by the chart, so they remain).

## Config and Packaging Notes

### Packaging

For AWS async/scalable deployments, build and package separately:
- **request handler** — `LocalZip`, `S3Zip`, or `Image`
- **agent runner** — `LocalZip`, `S3Zip`, or `Image` (typically `Image` for heavier dependencies)
- **response handler** — `LocalZip`, `S3Zip`, or `Image`
- **ws-connection-handler** — `LocalZip` only (WebSocket mode; no `package_type` field, only `package_path`)

Azure serverless uses a single zip `package_path` for the Functions deployment.
Azure containerized uses `package_path` as the Docker build context for Container Apps.

### config.yaml per mode

Each Lambda package must include a `config.yaml`. Most runtime values (`execution.mode`, queue URLs, table names, `max_receive_count`, `websocket_api.chat_route`) are **injected automatically by Terraform as environment variables** and do not need to be set in `config.yaml`. Only the values in the table below must be configured manually:

| Config key | Required in | Notes |
|------------|------------|-------|
| `execution.response_store.type` | request-handler, response-handler (queue modes) | `redis`, `valkey`, or `dynamodb` — not injected |
| `execution.response_store.retry_count` | request-handler (`rest_sync`) | how many times to poll for response |
| `execution.response_store.delay` | request-handler (`rest_sync`) | seconds between polls |
| `session.type` | agent-runner | `redis`, `valkey`, or `dynamodb` — not injected |
| `session.redis.prefix` | agent-runner (Redis sessions) | key namespace prefix |
| `session.valkey.prefix` | agent-runner (Valkey sessions) | key namespace prefix |

### Timeouts and visibility

- SQS `input_queue_visibility_timeout` must be **>= agent runner Lambda timeout**.
- SQS `output_queue_visibility_timeout` must be **>= response handler Lambda timeout**.
- FIFO queues are used by default; `message_group_id` maps to session ID.

## Prerequisites Checklist

### AWS

- AWS CLI configured (`aws configure`)
- Terraform >= 1.9.5
- S3 bucket for remote state (optional but recommended)
- DynamoDB table for state lock (optional but recommended)
- Container registry access (if `package_type = "Image"`)

### Azure

- Azure CLI logged in (`az login`)
- Terraform >= 1.9.5
- Resource group and subscription permissions
- Storage for remote Terraform state (optional but recommended)

### GCP

- GCP CLI configured (`gcloud auth application-default login`)
- Terraform >= 1.9.5
- Docker installed (for local image building and push to Artifact Registry)
- An existing GCP project with appropriate permissions
- Required APIs enabled: Cloud Run, API Gateway, Artifact Registry, VPC Access

## Deploy

```bash
cd deploy
terraform init
terraform plan
terraform apply
```

Kubernetes deployments use `helm install` / `helm upgrade` instead; see the
On-Prem / Kubernetes section above.

## Teardown

```bash
cd deploy
terraform destroy
```

Kubernetes: `helm uninstall <release>`.

## What to Do Next

- Add capabilities (`ak-add-capabilities`) before production rollout.
- Add tests (`ak-test`) against deployed endpoints.
- Add integrations (`ak-add-integration`) for Slack/WhatsApp/Telegram/Teams.
- Iterate on tools and agents (`ak-build`) and re-run `terraform apply` (or `helm upgrade`).