diff --git a/eventbridge-scheduler-ai-agent-trigger/Architecture.png b/eventbridge-scheduler-ai-agent-trigger/Architecture.png
new file mode 100644
index 000000000..482111a97
Binary files /dev/null and b/eventbridge-scheduler-ai-agent-trigger/Architecture.png differ
diff --git a/eventbridge-scheduler-ai-agent-trigger/README.md b/eventbridge-scheduler-ai-agent-trigger/README.md
new file mode 100644
index 000000000..54e5e00ea
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/README.md
@@ -0,0 +1,89 @@
+# Amazon EventBridge Scheduler to Amazon Bedrock AI Agent
+
+This pattern demonstrates how to trigger an Amazon Bedrock AI Agent on a recurring schedule using Amazon EventBridge Scheduler. An orchestrator AWS Lambda function, invoked by the scheduler, sends a task payload to the Bedrock Agent, which processes the input, generates an execution summary, and persists the result to a Amazon DynamoDB table via an action group Lambda.
+
+Learn more about this pattern at Serverless Land Patterns: https://serverlessland.com/patterns/eventbridge-scheduler-ai-agent-trigger
+
+Important: this application uses various AWS services and there are costs associated with these services after the Free Tier usage - please see the [AWS Pricing page](https://aws.amazon.com/pricing/) for details. You are responsible for any AWS costs incurred. No warranty is implied in this example.
+
+## Requirements
+
+* [Create an AWS account](https://portal.aws.amazon.com/gp/aws/developer/registration/index.html) if you do not already have one and log in. The IAM user that you use must have sufficient permissions to make necessary AWS service calls and manage AWS resources.
+* [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) installed and configured
+* [Git Installed](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* [Terraform](https://www.terraform.io/downloads.html) >= 1.0 installed
+
+## Architecture
+
+![Architecture Diagram](Architecture.png)
+
+The pattern deploys the following resources:
+
+1. **Amazon EventBridge Scheduler** – Triggers the orchestrator Lambda on a recurring schedule (default: `rate(1 hour)`).
+2. **Orchestrator Lambda** (Python 3.14) – Receives the scheduler event and invokes the Bedrock Agent with a task payload.
+3. **Amazon Bedrock Agent** – Processes the task payload, generates an execution summary using a foundation model (default: Claude 3 Haiku), and calls the action group.
+4. **Action Group Lambda** (Python 3.14) – Persists execution records to DynamoDB.
+5. **Amazon DynamoDB Table** – Stores task execution records.
+6. **Amazon SQS Dead-Letter Queue** – Captures failed scheduler invocations after retries are exhausted.
+
+## Deployment Instructions
+
+1. Clone the repository:
+    ```
+    git clone https://github.com/aws-samples/serverless-patterns
+    ```
+1. Change directory to the pattern directory:
+    ```
+    cd serverless-patterns/eventbridge-scheduler-ai-agent-trigger
+    ```
+1. Initialize Terraform:
+    ```
+    terraform init
+    ```
+1. Deploy the infrastructure:
+    ```
+    terraform apply -auto-approve
+    ```
+    During the prompts, provide values for:
+    * `aws_region` – AWS region (e.g. `us-east-1`)
+    * `prefix` – Unique prefix for all resource names
+
+1. Note the outputs from the deployment. These contain the resource names and ARNs used for testing.
+
+## How it works
+
+1. EventBridge Scheduler fires on the configured schedule and invokes the orchestrator Lambda with a JSON payload containing `taskType`, `scheduleName`, and `scheduledTime`.
+2. The orchestrator Lambda calls `bedrock-agent-runtime:InvokeAgent` with the payload, targeting the agent alias.
+3. The Bedrock Agent parses the payload, generates an executive summary using the foundation model, and calls the `recordTaskExecution` action group.
+4. The action group Lambda writes the execution record (task ID, type, scheduled time, summary, and recorded timestamp) to the DynamoDB table.
+5. If the scheduler invocation fails after 3 retries, the event is sent to the SQS dead-letter queue.
+
+## Testing
+
+1. Replace `<prefix>` with the prefix chosen during deployment and invoke the orchestrator Lambda function manually:
+    ```
+    aws lambda invoke \
+      --function-name <prefix>-agent-orchestrator \
+      --payload '{"taskType":"scheduled-report","scheduleName":"manual-test","scheduledTime":"2026-03-13T10:00:00Z"}' \
+      --cli-binary-format raw-in-base64-out \
+      output.json
+    ```
+2. Check the DynamoDB table for the new execution record:
+    ```
+    aws dynamodb scan --table-name <prefix>-agent-task-executions
+    ```
+
+## Cleanup
+
+1. Destroy the stack:
+    ```
+    terraform destroy --auto-approve
+    ```
+1. Confirm all resources have been removed:
+    ```
+    terraform show
+    ```
+----
+Copyright 2026 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+SPDX-License-Identifier: MIT-0
diff --git a/eventbridge-scheduler-ai-agent-trigger/action_group.py b/eventbridge-scheduler-ai-agent-trigger/action_group.py
new file mode 100644
index 000000000..ad734974f
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/action_group.py
@@ -0,0 +1,145 @@
+import boto3
+import json
+import os
+import logging
+from datetime import datetime, timezone
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+dynamodb = boto3.resource("dynamodb")
+table = dynamodb.Table(os.environ["DYNAMODB_TABLE"])
+
+
+def lambda_handler(event, context):
+    """
+    Bedrock Agent Action Group Lambda.
+    Called by the agent to persist task execution records in DynamoDB.
+    """
+    logger.info("Action group event: %s", json.dumps(event))
+
+    api_path = event.get("apiPath", "")
+    action_group = event.get("actionGroup", "")
+    http_method = event.get("httpMethod", "")
+    params = _extract_parameters(event)
+
+    logger.info("API path: %s | params: %s", api_path, json.dumps(params))
+
+    if api_path == "/record-task-execution":
+        result = _record_task_execution(params)
+    elif api_path == "/get-last-execution":
+        result = _get_last_execution(params)
+    else:
+        result = {
+            "statusCode": 400,
+            "body": json.dumps({"error": f"Unknown API path: {api_path}"}),
+        }
+
+    return {
+        "messageVersion": "1.0",
+        "response": {
+            "actionGroup": action_group,
+            "apiPath": api_path,
+            "httpMethod": http_method,
+            "httpStatusCode": result["statusCode"],
+            "responseBody": {
+                "application/json": {"body": result["body"]}
+            },
+        },
+    }
+
+
+# ──────────────────────────────────────────
+# Action handlers
+# ──────────────────────────────────────────
+
+def _record_task_execution(params: dict) -> dict:
+    """Write an execution record to DynamoDB."""
+    now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    item = {
+        "TaskId": params.get("taskId", f"task-{now}"),
+        "TaskType": params.get("taskType", "unknown"),
+        "ScheduledTime": params.get("scheduledTime", now),
+        "ExecutionSummary": params.get("executionSummary", ""),
+        "Status": "COMPLETED",
+        "RecordedAt": now,
+    }
+
+    table.put_item(Item=item)
+    logger.info("Recorded task execution: %s", item["TaskId"])
+
+    return {
+        "statusCode": 200,
+        "body": json.dumps(
+            {
+                "message": f"Task execution {item['TaskId']} recorded successfully",
+                "taskId": item["TaskId"],
+                "recordedAt": now,
+            }
+        ),
+    }
+
+
+def _get_last_execution(params: dict) -> dict:
+    """Scan for the most recent execution (simple approach for demo)."""
+    task_type = params.get("taskType", "scheduled-report")
+
+    response = table.scan(
+        FilterExpression="TaskType = :tt",
+        ExpressionAttributeValues={":tt": task_type},
+        Limit=10,
+    )
+
+    items = sorted(
+        response.get("Items", []),
+        key=lambda x: x.get("RecordedAt", ""),
+        reverse=True,
+    )
+
+    if items:
+        last = items[0]
+        return {
+            "statusCode": 200,
+            "body": json.dumps(
+                {
+                    "taskId": last["TaskId"],
+                    "taskType": last["TaskType"],
+                    "scheduledTime": last["ScheduledTime"],
+                    "executionSummary": last.get("ExecutionSummary", ""),
+                    "recordedAt": last["RecordedAt"],
+                }
+            ),
+        }
+
+    return {
+        "statusCode": 404,
+        "body": json.dumps(
+            {"message": f"No executions found for task type: {task_type}"}
+        ),
+    }
+
+
+# ──────────────────────────────────────────
+# Helpers
+# ──────────────────────────────────────────
+
+def _extract_parameters(event: dict) -> dict:
+    """Pull parameters from the Bedrock Agent request body and/or parameters list."""
+    params = {}
+
+    # From requestBody (POST actions)
+    properties = (
+        event.get("requestBody", {})
+        .get("content", {})
+        .get("application/json", {})
+        .get("properties", [])
+    )
+    for prop in properties:
+        params[prop["name"]] = prop.get("value", "")
+
+    # From top-level parameters (GET actions)
+    for param in event.get("parameters", []):
+        params[param["name"]] = param.get("value", "")
+
+    return params
\ No newline at end of file
diff --git a/eventbridge-scheduler-ai-agent-trigger/api_schema.json b/eventbridge-scheduler-ai-agent-trigger/api_schema.json
new file mode 100644
index 000000000..6eef8a289
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/api_schema.json
@@ -0,0 +1,106 @@
+{
+  "openapi": "3.0.0",
+  "info": {
+    "title": "Scheduled Task Execution API",
+    "version": "1.0.0",
+    "description": "Actions for recording and retrieving scheduled AI agent task executions"
+  },
+  "paths": {
+    "/record-task-execution": {
+      "post": {
+        "operationId": "recordTaskExecution",
+        "summary": "Record a scheduled task execution in the tracking database",
+        "description": "Persists a task execution record with task ID, type, timestamp, and an AI-generated summary to DynamoDB",
+        "requestBody": {
+          "required": true,
+          "content": {
+            "application/json": {
+              "schema": {
+                "type": "object",
+                "required": [
+                  "taskId",
+                  "taskType",
+                  "scheduledTime",
+                  "executionSummary"
+                ],
+                "properties": {
+                  "taskId": {
+                    "type": "string",
+                    "description": "Unique identifier for this task execution — combine scheduleName and scheduledTime"
+                  },
+                  "taskType": {
+                    "type": "string",
+                    "description": "The category of the scheduled task (e.g. scheduled-report)"
+                  },
+                  "scheduledTime": {
+                    "type": "string",
+                    "description": "ISO 8601 UTC timestamp when the task was scheduled to run"
+                  },
+                  "executionSummary": {
+                    "type": "string",
+                    "description": "AI-generated summary describing the task execution and its outcome"
+                  }
+                }
+              }
+            }
+          }
+        },
+        "responses": {
+          "200": {
+            "description": "Execution recorded successfully",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "properties": {
+                    "message": { "type": "string" },
+                    "taskId": { "type": "string" },
+                    "recordedAt": { "type": "string" }
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/get-last-execution": {
+      "get": {
+        "operationId": "getLastExecution",
+        "summary": "Get the most recent task execution for a given task type",
+        "description": "Retrieves the latest execution record from DynamoDB filtered by task type",
+        "parameters": [
+          {
+            "name": "taskType",
+            "in": "query",
+            "required": true,
+            "schema": { "type": "string" },
+            "description": "Task type to look up (e.g. scheduled-report)"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Last execution found",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "properties": {
+                    "taskId": { "type": "string" },
+                    "taskType": { "type": "string" },
+                    "scheduledTime": { "type": "string" },
+                    "executionSummary": { "type": "string" },
+                    "recordedAt": { "type": "string" }
+                  }
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "No executions found for the given task type"
+          }
+        }
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/eventbridge-scheduler-ai-agent-trigger/example-pattern.json b/eventbridge-scheduler-ai-agent-trigger/example-pattern.json
new file mode 100644
index 000000000..0278ecb49
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/example-pattern.json
@@ -0,0 +1,58 @@
+{
+  "title": "Trigger AI Agent with Amazon EventBridge Scheduler",
+  "description": "Create a EventBridge scheduler which invokes a Bedrock Agent upon triggering.",
+  "language": "Python",
+  "level": "300",
+  "framework": "Terraform",
+  "introBox": {
+    "headline": "How it works",
+    "text": [
+      "This pattern demonstrates how to trigger an Amazon Bedrock AI Agent on a recurring schedule using Amazon EventBridge Scheduler. An orchestrator AWS Lambda function, invoked by the scheduler, sends a task payload to the Bedrock Agent, which processes the input, generates an execution summary, and persists the result to a DynamoDB table via an action group Lambda. The pattern includes retry logic, a dead-letter queue for failed invocations, and least-privilege IAM policies scoped to the agent alias ARN."
+    ]
+  },
+  "gitHub": {
+    "template": {
+      "repoURL": "https://github.com/aws-samples/serverless-patterns/tree/main/eventbridge-scheduler-ai-agent-trigger",
+      "templateURL": "serverless-patterns/eventbridge-scheduler-ai-agent-trigger",
+      "projectFolder": "eventbridge-scheduler-ai-agent-trigger",
+      "templateFile": "main.tf"
+    }
+  },
+  "resources": {
+    "bullets": [
+      {
+        "text": "Invoke a Lambda function on a schedule",
+        "link": "https://docs.aws.amazon.com/lambda/latest/dg/with-eventbridge-scheduler.html"
+      },
+      {
+        "text": "Allow users to view information about and invoke an agent",
+        "link": "https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples-agent.html#security_iam_id-based-policy-examples-perform-actions-agent"
+      }
+    ]
+  },
+  "deploy": {
+    "text": [
+      "terraform init",
+      "terraform apply"
+    ]
+  },
+  "testing": {
+    "text": [
+      "See the GitHub repo for detailed testing instructions."
+    ]
+  },
+  "cleanup": {
+    "text": [
+      "terraform destroy",
+      "terraform show"
+    ]
+  },
+  "authors": [
+    {
+      "name": "Rajil Paloth",
+      "image": "https://i.ibb.co/r2TsqGf6/Passport-size.jpg",
+      "bio": "ProServe Delivery Consultant at AWS",
+      "linkedin": "paloth"
+    }
+  ]
+}
diff --git a/eventbridge-scheduler-ai-agent-trigger/main.tf b/eventbridge-scheduler-ai-agent-trigger/main.tf
new file mode 100644
index 000000000..731337070
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/main.tf
@@ -0,0 +1,533 @@
+terraform {
+  required_version = ">= 1.0"
+  required_providers {
+    aws = {
+      source  = "hashicorp/aws"
+      version = "~> 6.32.1"
+    }
+    archive = {
+      source  = "hashicorp/archive"
+      version = "~> 2.7"
+    }
+  }
+}
+
+provider "aws" {
+  region = var.aws_region
+}
+
+############################################################
+# Variables
+############################################################
+
+variable "aws_region" {
+  description = "AWS region for resources (e.g. us-east-1, us-west-2)"
+  type        = string
+
+  validation {
+    condition     = can(regex("^[a-z]{2}-[a-z]+-[0-9]+$", var.aws_region))
+    error_message = "Must be a valid AWS region (e.g. us-east-1, eu-west-2)."
+  }
+}
+
+variable "prefix" {
+  description = "Unique prefix for all resource names"
+  type        = string
+
+  validation {
+    condition     = can(regex("^[a-z0-9][a-z0-9\\-]{1,20}$", var.prefix))
+    error_message = "Prefix must be 2-21 lowercase alphanumeric characters or hyphens."
+  }
+}
+
+variable "bedrock_model_id" {
+  description = "Bedrock foundation model ID for the agent"
+  type        = string
+  default     = "anthropic.claude-3-haiku-20240307-v1:0"
+}
+
+variable "schedule_expression" {
+  description = "EventBridge Scheduler expression (e.g. rate(1 hour), cron(0 9 * * ? *))"
+  type        = string
+  default     = "rate(1 hour)"
+}
+
+variable "log_retention_days" {
+  description = "CloudWatch log retention in days (0 = never expire)"
+  type        = number
+  default     = 14
+}
+
+############################################################
+# Data Sources
+############################################################
+
+data "aws_caller_identity" "current" {}
+data "aws_region" "current" {}
+
+############################################################
+# 1. DYNAMODB TABLE (agent writes execution records here)
+############################################################
+
+resource "aws_dynamodb_table" "task_executions" {
+  name         = "${var.prefix}-agent-task-executions"
+  billing_mode = "PAY_PER_REQUEST"
+  hash_key     = "TaskId"
+
+  attribute {
+    name = "TaskId"
+    type = "S"
+  }
+
+  tags = {
+    Project = "${var.prefix}-ai-agent-scheduler"
+  }
+}
+
+############################################################
+# 2. ACTION GROUP LAMBDA (Bedrock Agent calls this to
+#    read/write DynamoDB)
+############################################################
+
+resource "aws_iam_role" "action_group_role" {
+  name = "${var.prefix}-action-group-lambda-role"
+
+  assume_role_policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Action = "sts:AssumeRole"
+      Effect = "Allow"
+      Principal = { Service = "lambda.amazonaws.com" }
+    }]
+  })
+}
+
+resource "aws_iam_role_policy_attachment" "action_group_basic" {
+  role       = aws_iam_role.action_group_role.name
+  policy_arn = "arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
+}
+
+resource "aws_iam_role_policy" "action_group_dynamodb" {
+  name = "${var.prefix}-action-group-dynamodb"
+  role = aws_iam_role.action_group_role.id
+
+  policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Effect = "Allow"
+      Action = [
+        "dynamodb:PutItem",
+        "dynamodb:GetItem",
+        "dynamodb:UpdateItem",
+        "dynamodb:Query",
+        "dynamodb:Scan"
+      ]
+      Resource = [
+        aws_dynamodb_table.task_executions.arn,
+        "${aws_dynamodb_table.task_executions.arn}/index/*"
+      ]
+    }]
+  })
+}
+
+resource "aws_cloudwatch_log_group" "action_group_logs" {
+  name              = "/aws/lambda/${var.prefix}-agent-action-group"
+  retention_in_days = var.log_retention_days
+}
+
+data "archive_file" "action_group" {
+  type        = "zip"
+  output_path = "${path.module}/action_group.zip"
+
+  source {
+    filename = "action_group.py"
+    content  = file("${path.module}/action_group.py")
+  }
+}
+
+resource "aws_lambda_function" "action_group" {
+  function_name    = "${var.prefix}-agent-action-group"
+  role             = aws_iam_role.action_group_role.arn
+  handler          = "action_group.lambda_handler"
+  runtime          = "python3.14"
+  timeout          = 30
+  memory_size      = 128
+  filename         = data.archive_file.action_group.output_path
+  source_code_hash = data.archive_file.action_group.output_base64sha256
+
+  environment {
+    variables = {
+      DYNAMODB_TABLE = aws_dynamodb_table.task_executions.name
+      PREFIX         = var.prefix
+    }
+  }
+
+  depends_on = [
+    aws_cloudwatch_log_group.action_group_logs,
+    aws_iam_role_policy_attachment.action_group_basic,
+    aws_iam_role_policy.action_group_dynamodb,
+  ]
+}
+
+############################################################
+# 3. BEDROCK AGENT (AI agent with action group)
+############################################################
+
+# --- Agent IAM Role ---
+
+resource "aws_iam_role" "bedrock_agent_role" {
+  name = "${var.prefix}-bedrock-agent-role"
+
+  assume_role_policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Action = "sts:AssumeRole"
+      Effect = "Allow"
+      Principal = { Service = "bedrock.amazonaws.com" }
+      Condition = {
+        StringEquals = {
+          "aws:SourceAccount" = data.aws_caller_identity.current.account_id
+        }
+      }
+    }]
+  })
+}
+
+resource "aws_iam_role_policy" "bedrock_agent_model" {
+  name = "${var.prefix}-bedrock-invoke-model"
+  role = aws_iam_role.bedrock_agent_role.id
+
+  policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Effect   = "Allow"
+      Action   = "bedrock:InvokeModel"
+      Resource = "arn:aws:bedrock:${var.aws_region}::foundation-model/${var.bedrock_model_id}"
+    }]
+  })
+}
+
+# --- Agent ---
+
+resource "aws_bedrockagent_agent" "task_agent" {
+  agent_name                  = "${var.prefix}-scheduled-task-agent"
+  agent_resource_role_arn     = aws_iam_role.bedrock_agent_role.arn
+  foundation_model            = var.bedrock_model_id
+  idle_session_ttl_in_seconds = 600
+  prepare_agent               = false # prepared after action group is attached
+
+  instruction = <<-EOT
+    You are a Scheduled Task Execution Agent. You run on a schedule via
+    Amazon EventBridge Scheduler. Each time you are invoked you must:
+
+    1. PARSE the task payload provided to you (JSON with taskType, scheduleName,
+       and scheduledTime).
+    2. GENERATE a brief, one-paragraph executive summary describing the task
+       execution — include the task type, timestamp, and a note that the
+       execution completed successfully.
+    3. CALL the recordTaskExecution action to persist the execution record.
+       Use the values from the payload for taskId (use scheduleName + timestamp
+       combined), taskType, scheduledTime, and pass your generated summary as
+       the executionSummary.
+    4. CONFIRM success by returning a short completion message.
+
+    Always use the exact values from the payload. Never fabricate timestamps
+    or identifiers.
+  EOT
+}
+
+# --- Action Group ---
+
+resource "aws_bedrockagent_agent_action_group" "task_actions" {
+  action_group_name          = "${var.prefix}-task-execution-actions"
+  agent_id                   = aws_bedrockagent_agent.task_agent.agent_id
+  agent_version              = "DRAFT"
+  skip_resource_in_use_check = true
+
+  action_group_executor {
+    lambda = aws_lambda_function.action_group.arn
+  }
+
+  api_schema {
+    payload = file("${path.module}/api_schema.json")
+  }
+}
+
+# --- Allow Bedrock to invoke Action Group Lambda ---
+
+resource "aws_lambda_permission" "allow_bedrock" {
+  statement_id  = "AllowBedrockAgentInvoke"
+  action        = "lambda:InvokeFunction"
+  function_name = aws_lambda_function.action_group.function_name
+  principal     = "bedrock.amazonaws.com"
+  source_arn    = aws_bedrockagent_agent.task_agent.agent_arn
+}
+
+# --- Prepare Agent (must happen after action group is attached) ---
+
+resource "terraform_data" "prepare_agent" {
+  depends_on = [
+    aws_bedrockagent_agent_action_group.task_actions,
+    aws_lambda_permission.allow_bedrock,
+  ]
+
+  triggers_replace = [
+    aws_bedrockagent_agent.task_agent.agent_id,
+    aws_bedrockagent_agent_action_group.task_actions.action_group_name,
+  ]
+
+  provisioner "local-exec" {
+    command = "aws bedrock-agent prepare-agent --agent-id ${aws_bedrockagent_agent.task_agent.agent_id} --region ${var.aws_region} && sleep 15"
+  }
+}
+
+# --- Agent Alias (points to the prepared version) ---
+
+resource "aws_bedrockagent_agent_alias" "live" {
+  agent_alias_name = "${var.prefix}-live"
+  agent_id         = aws_bedrockagent_agent.task_agent.agent_id
+  description      = "Live alias for scheduled task agent"
+
+  depends_on = [terraform_data.prepare_agent]
+}
+
+############################################################
+# 4. ORCHESTRATOR LAMBDA (Scheduler invokes this →
+#    it invokes the Bedrock Agent)
+############################################################
+
+resource "aws_iam_role" "orchestrator_role" {
+  name = "${var.prefix}-orchestrator-lambda-role"
+
+  assume_role_policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Action = "sts:AssumeRole"
+      Effect = "Allow"
+      Principal = { Service = "lambda.amazonaws.com" }
+    }]
+  })
+}
+
+resource "aws_iam_role_policy_attachment" "orchestrator_basic" {
+  role       = aws_iam_role.orchestrator_role.name
+  policy_arn = "arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
+}
+
+# ── InvokeAgent requires permission on the agent ALIAS ARN ──
+# ARN format: arn:aws:bedrock:{region}:{account}:agent-alias/{agent-id}/{alias-id}
+# The alias resource exposes this as agent_alias_arn.
+# We also grant access to all aliases of this agent so the role
+# continues to work if a new alias is created later.
+
+resource "aws_iam_role_policy" "orchestrator_bedrock" {
+  name = "${var.prefix}-orchestrator-invoke-agent"
+  role = aws_iam_role.orchestrator_role.id
+
+  policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Sid    = "InvokeBedrockAgentAlias"
+      Effect = "Allow"
+      Action = "bedrock:InvokeAgent"
+      Resource = [
+        # Specific alias created by this stack
+        aws_bedrockagent_agent_alias.live.agent_alias_arn,
+        # Wildcard for any future alias of the same agent
+        "arn:aws:bedrock:${var.aws_region}:${data.aws_caller_identity.current.account_id}:agent-alias/${aws_bedrockagent_agent.task_agent.agent_id}/*"
+      ]
+    }]
+  })
+}
+
+resource "aws_cloudwatch_log_group" "orchestrator_logs" {
+  name              = "/aws/lambda/${var.prefix}-agent-orchestrator"
+  retention_in_days = var.log_retention_days
+}
+
+data "archive_file" "orchestrator" {
+  type        = "zip"
+  output_path = "${path.module}/orchestrator.zip"
+
+  source {
+    filename = "orchestrator.py"
+    content  = file("${path.module}/orchestrator.py")
+  }
+}
+
+resource "aws_lambda_function" "orchestrator" {
+  function_name    = "${var.prefix}-agent-orchestrator"
+  role             = aws_iam_role.orchestrator_role.arn
+  handler          = "orchestrator.lambda_handler"
+  runtime          = "python3.14"
+  timeout          = 120
+  memory_size      = 256
+  filename         = data.archive_file.orchestrator.output_path
+  source_code_hash = data.archive_file.orchestrator.output_base64sha256
+
+  environment {
+    variables = {
+      BEDROCK_AGENT_ID       = aws_bedrockagent_agent.task_agent.agent_id
+      BEDROCK_AGENT_ALIAS_ID = aws_bedrockagent_agent_alias.live.agent_alias_id
+      PREFIX                 = var.prefix
+    }
+  }
+
+  depends_on = [
+    aws_cloudwatch_log_group.orchestrator_logs,
+    aws_iam_role_policy_attachment.orchestrator_basic,
+    aws_iam_role_policy.orchestrator_bedrock,
+  ]
+}
+
+############################################################
+# 5. SQS DEAD-LETTER QUEUE (captures failed scheduler
+#    invocations after retries are exhausted)
+############################################################
+
+resource "aws_sqs_queue" "scheduler_dlq" {
+  name                      = "${var.prefix}-scheduler-dlq"
+  message_retention_seconds = 1209600 # 14 days
+}
+
+############################################################
+# 6. EVENTBRIDGE SCHEDULER (triggers the orchestrator
+#    on a recurring schedule)
+############################################################
+
+resource "aws_iam_role" "scheduler_role" {
+  name = "${var.prefix}-scheduler-role"
+
+  assume_role_policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Action = "sts:AssumeRole"
+      Effect = "Allow"
+      Principal = { Service = "scheduler.amazonaws.com" }
+    }]
+  })
+}
+
+resource "aws_iam_role_policy" "scheduler_invoke_lambda" {
+  name = "${var.prefix}-scheduler-invoke-lambda"
+  role = aws_iam_role.scheduler_role.id
+
+  policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [
+      {
+        Sid      = "InvokeLambda"
+        Effect   = "Allow"
+        Action   = "lambda:InvokeFunction"
+        Resource = aws_lambda_function.orchestrator.arn
+      },
+      {
+        Sid      = "SendToDLQ"
+        Effect   = "Allow"
+        Action   = "sqs:SendMessage"
+        Resource = aws_sqs_queue.scheduler_dlq.arn
+      }
+    ]
+  })
+}
+
+resource "aws_sqs_queue_policy" "allow_scheduler" {
+  queue_url = aws_sqs_queue.scheduler_dlq.id
+
+  policy = jsonencode({
+    Version = "2012-10-17"
+    Statement = [{
+      Sid       = "AllowSchedulerSendMessage"
+      Effect    = "Allow"
+      Principal = { Service = "scheduler.amazonaws.com" }
+      Action    = "sqs:SendMessage"
+      Resource  = aws_sqs_queue.scheduler_dlq.arn
+      Condition = {
+        ArnEquals = {
+          "aws:SourceArn" = "arn:aws:scheduler:${var.aws_region}:${data.aws_caller_identity.current.account_id}:schedule/default/${var.prefix}-trigger-ai-agent"
+        }
+      }
+    }]
+  })
+}
+
+resource "aws_cloudwatch_log_group" "scheduler_logs" {
+  name              = "/aws/scheduler/${var.prefix}-trigger-ai-agent"
+  retention_in_days = var.log_retention_days
+}
+
+resource "aws_scheduler_schedule" "trigger_agent" {
+  name                = "${var.prefix}-trigger-ai-agent"
+  schedule_expression = var.schedule_expression
+
+  flexible_time_window {
+    mode = "OFF"
+  }
+
+  target {
+    arn      = aws_lambda_function.orchestrator.arn
+    role_arn = aws_iam_role.scheduler_role.arn
+
+    input = jsonencode({
+      taskType      = "scheduled-report"
+      scheduleName  = "${var.prefix}-trigger-ai-agent"
+      scheduledTime = "REPLACED_AT_RUNTIME"
+    })
+
+    retry_policy {
+      maximum_retry_attempts       = 3
+      maximum_event_age_in_seconds = 3600
+    }
+
+    dead_letter_config {
+      arn = aws_sqs_queue.scheduler_dlq.arn
+    }
+  }
+
+  depends_on = [aws_cloudwatch_log_group.scheduler_logs]
+}
+
+############################################################
+# 7. OUTPUTS
+############################################################
+
+output "prefix" {
+  value = var.prefix
+}
+
+output "schedule_name" {
+  value = aws_scheduler_schedule.trigger_agent.name
+}
+
+output "schedule_arn" {
+  value = aws_scheduler_schedule.trigger_agent.arn
+}
+
+output "orchestrator_lambda_name" {
+  value = aws_lambda_function.orchestrator.function_name
+}
+
+output "action_group_lambda_name" {
+  value = aws_lambda_function.action_group.function_name
+}
+
+output "bedrock_agent_id" {
+  value = aws_bedrockagent_agent.task_agent.agent_id
+}
+
+output "bedrock_agent_alias_id" {
+  value = aws_bedrockagent_agent_alias.live.agent_alias_id
+}
+
+output "bedrock_agent_alias_arn" {
+  value       = aws_bedrockagent_agent_alias.live.agent_alias_arn
+  description = "The alias ARN that the orchestrator Lambda is authorized to invoke"
+}
+
+output "dynamodb_table_name" {
+  value = aws_dynamodb_table.task_executions.name
+}
+
+output "dlq_queue_url" {
+  value = aws_sqs_queue.scheduler_dlq.url
+}
\ No newline at end of file
diff --git a/eventbridge-scheduler-ai-agent-trigger/orchestrator.py b/eventbridge-scheduler-ai-agent-trigger/orchestrator.py
new file mode 100644
index 000000000..3bc6577c1
--- /dev/null
+++ b/eventbridge-scheduler-ai-agent-trigger/orchestrator.py
@@ -0,0 +1,72 @@
+import boto3
+import json
+import os
+import uuid
+import logging
+from datetime import datetime, timezone
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+bedrock_agent_runtime = boto3.client("bedrock-agent-runtime")
+
+AGENT_ID = os.environ["BEDROCK_AGENT_ID"]
+AGENT_ALIAS_ID = os.environ["BEDROCK_AGENT_ALIAS_ID"]
+
+
+def lambda_handler(event, context):
+    """
+    Orchestrator Lambda — invoked by EventBridge Scheduler.
+    Parses the schedule payload, enriches it with a real timestamp,
+    and sends it to the Bedrock Agent for processing.
+    """
+    logger.info("Received event: %s", json.dumps(event))
+
+    # ── Enrich the payload with the actual invocation time ──
+    task_type = event.get("taskType", "scheduled-report")
+    schedule_name = event.get("scheduleName", "unknown-schedule")
+    scheduled_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    prompt = (
+        f"Process this scheduled task execution:\n\n"
+        f'{{"taskType": "{task_type}", '
+        f'"scheduleName": "{schedule_name}", '
+        f'"scheduledTime": "{scheduled_time}"}}\n\n'
+        f"Parse the payload, generate a summary, and record the execution."
+    )
+
+    # ── Invoke the Bedrock Agent ──
+    session_id = str(uuid.uuid4())
+    logger.info(
+        "Invoking agent %s (alias %s) | session %s",
+        AGENT_ID,
+        AGENT_ALIAS_ID,
+        session_id,
+    )
+
+    response = bedrock_agent_runtime.invoke_agent(
+        agentId=AGENT_ID,
+        agentAliasId=AGENT_ALIAS_ID,
+        sessionId=session_id,
+        inputText=prompt,
+    )
+
+    # ── Collect the streaming response ──
+    agent_response = ""
+    for stream_event in response.get("completion", []):
+        if "chunk" in stream_event:
+            agent_response += stream_event["chunk"]["bytes"].decode("utf-8")
+
+    logger.info("Agent response: %s", agent_response)
+
+    return {
+        "statusCode": 200,
+        "body": json.dumps(
+            {
+                "sessionId": session_id,
+                "taskType": task_type,
+                "scheduledTime": scheduled_time,
+                "agentResponse": agent_response,
+            }
+        ),
+    }
\ No newline at end of file