Enterprise organizations more and more depend on web-based functions for important enterprise processes, but many workflows stay manually intensive, creating operational inefficiencies and compliance dangers. Regardless of important know-how investments, information employees routinely navigate between eight to 12 completely different internet functions throughout customary workflows, always switching contexts and manually transferring info between programs. Knowledge entry and validation duties eat roughly 25-30% of employee time, whereas handbook processes create compliance bottlenecks and cross-system information consistency challenges that require steady human verification. Conventional automation approaches have important limitations. Whereas robotic course of automation (RPA) works for structured, rule-based processes, it turns into brittle when functions replace and requires ongoing upkeep. API-based integration stays optimum, however many legacy programs lack fashionable capabilities. Enterprise course of administration platforms present orchestration however battle with advanced determination factors and direct internet interplay. Because of this, most enterprises function with blended approaches the place solely 30% of workflow duties are totally automated, 50% require human oversight, and 20% stay completely handbook.
These challenges manifest throughout frequent enterprise workflows. For instance, buy order validation requires clever navigation by means of a number of programs to carry out three-way matching between buy orders (POs), receipts, and invoices whereas sustaining audit trails. Worker on-boarding calls for coordinated entry provisioning throughout identification administration, buyer relationship administration (CRM), enterprise useful resource planning (ERP), and collaboration platforms with role-based decision-making. Lastly, e-commerce order processing should intelligently course of orders throughout a number of retailer web sites missing native API entry. Synthetic intelligence (AI) brokers characterize a big development past these conventional options, providing capabilities that may intelligently navigate complexity, adapt to dynamic environments, and dramatically scale back handbook intervention throughout enterprise workflows.
On this publish, we show how an e-commerce order administration platform can automate order processing workflows throughout a number of retail web sites by way of AI brokers like Amazon Nova Act and Strands agent utilizing Amazon Bedrock AgentCore Browser at scale.
E-commerce order automation workflow
This workflow demonstrates how AI brokers can intelligently automate advanced, multi-step order processing throughout various retailer web sites that lack native API integration, combining adaptive browser navigation with human oversight for exception dealing with.
The next elements work collectively to allow scalable, AI-powered order processing:
- ECS Fargate duties run containerized Python FastAPI backend with React frontend, offering WebSocket connections for real-time order automation. Duties mechanically scale based mostly on demand.
- Software integrates with Amazon Bedrock and Amazon Nova Act for AI-powered order automation. AgentCore Browser Device offers safe, remoted browser atmosphere for internet automation. Important Agent orchestrates Nova Act Agent and Strands + Playwright Agent for clever browser management.
The e-commerce order automation workflow represents a standard enterprise problem the place companies must course of orders throughout a number of retailer web sites with out native API entry. This workflow demonstrates the complete capabilities of AI-powered browser automation, from preliminary navigation by means of advanced decision-making to human-in-the-loop intervention. We’ve got a pattern agentic e-commerce automation constructed out which we’ve got open sourced on aws-samples repository on GitHub.
Workflow course of
Customers of the e-commerce order administration system submit buyer orders by means of an internet interface or batch CSV add, together with product particulars (URL, measurement, shade), buyer info, and delivery handle. The system assigns precedence ranges and queues orders for processing. When an order begins, Amazon Bedrock AgentCore Browser creates an remoted browser session with Chrome DevTools Protocol (CDP) connectivity. Amazon Bedrock AgentCore Browser offers a safe, cloud-based browser that allows the AI agent (Amazon Nova Act and Strands agent on this case) to work together with web sites. It contains safety features comparable to session isolation, built-in observability by means of dwell viewing, AWS CloudTrail logging, and session replay capabilities. The system retrieves retailer credentials from AWS Secrets and techniques Supervisor and generates a dwell view URL utilizing Amazon DCV streaming for real-time monitoring. The next diagram illustrates the order complete workflow course of.
Browser automation with form-filling and order submission
Type-filling represents a important functionality the place the agent intelligently detects and populates numerous discipline varieties throughout completely different retailer checkout layouts. The AI agent visits the product web page, handles authentication if wanted, and analyzes the web page to establish measurement selectors, shade choices, and cart buttons. It selects specified choices, provides objects to cart, and proceeds to checkout, filling delivery info with clever discipline detection throughout completely different retailer layouts. If merchandise are out of inventory or unavailable, the agent escalates to human assessment with context about alternate options.
The pattern software employs two distinct approaches relying on the automation methodology. Amazon Nova Act makes use of visible understanding and DOM construction of the webpage, permitting the Nova Act agent to obtain pure language directions like “fill delivery handle” and mechanically establish type fields from the screenshot, adapting to completely different layouts with out predefined selectors. In distinction, the Strands + Playwright Mannequin Context Protocol (MCP) mixture makes use of Bedrock fashions to investigate the web page’s Doc Object Mannequin (DOM) construction, decide acceptable type discipline selectors, after which Playwright MCP executes the low-level browser interactions to populate the fields with buyer information. Each approaches mechanically adapt to various retailer checkout interfaces, eliminating the brittleness of conventional selector-based automation.
Human-in-the-loop
When encountering CAPTCHAs or advanced challenges, the agent pauses automation and notifies operators by way of WebSocket. Operators entry the dwell view to see the precise browser state, resolve the problem manually, and set off resumption. AgentCore Browser permits for human browser takeover and passing management again to the agent. The agent continues from the present state with out restarting your entire course of.
Observability and scale
All through execution, the system captures session recordings saved in S3, screenshots at important steps, and detailed execution logs with timestamps. Operators monitor progress by means of a real-time dashboard displaying order standing, present step, and progress share. For top-volume situations, batch processing helps parallel execution of a number of orders with configurable employees (1-10), priority-based queuing, and automated retry logic for transient failures.
Conclusion
AI agent-driven browser automation represents a basic shift in how enterprises strategy workflow administration. By combining clever decision-making, adaptive navigation, and human-in-the-loop capabilities, organizations can transfer past the 30-50-20 break up of conventional automation towards considerably greater automation charges throughout advanced, multi-system workflows. The e-commerce order automation instance demonstrates that AI brokers don’t exchange conventional RPA—they allow automation of workflows beforehand thought of too dynamic or advanced for automation, dealing with various consumer interfaces, making contextual selections, and sustaining full compliance and auditability.
As enterprises face mounting stress to enhance operational effectivity whereas managing legacy programs and sophisticated integrations, AI brokers supply a sensible path ahead. Slightly than investing in costly system overhauls or accepting the inefficiencies of handbook processes, organizations can deploy clever browser automation that adapts to their present know-how panorama. The result’s lowered operational prices, quicker processing occasions, improved compliance, and most significantly, liberation of information employees from repetitive information entry and system navigation duties—permitting them to deal with higher-value actions that drive enterprise impression.
In regards to the authors
Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI workforce, the place he has led the design and improvement of a number of Bedrock AgentCore companies from the bottom up, together with Runtime, Browser, Code Interpreter, and Identification. He beforehand labored on Amazon SageMaker since its early days, launching AI/ML capabilities now utilized by hundreds of corporations worldwide. Earlier in his profession, Kosti was an information scientist. Outdoors of labor, he builds private productiveness automations, performs tennis, and enjoys life together with his spouse and children.
Veda Raman is a Sr Options Architect for Generative AI for Amazon Nova and Agentic AI at AWS. She helps clients design and construct Agentic AI options utilizing Amazon Nova fashions and Bedrock AgentCore. She beforehand labored with clients constructing ML options utilizing Amazon SageMaker and likewise as a serverless options architect at AWS.
Sanghwa Na is a Generative AI Specialist Options Architect at Amazon Internet Providers. Primarily based in San Francisco, he works with clients to design and construct generative AI options utilizing massive language fashions and basis fashions on AWS. He focuses on serving to organizations undertake AI applied sciences that drive actual enterprise worth.
