Enterprise organizations more and more depend on web-based purposes for vital 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 purposes throughout commonplace workflows, always switching contexts and manually transferring info between programs. Information entry and validation duties devour roughly 25-30% of employee time, whereas guide processes create compliance bottlenecks and cross-system knowledge 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 purposes 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 wrestle with complicated choice factors and direct internet interplay. Because of this, most enterprises function with combined approaches the place solely 30% of workflow duties are totally automated, 50% require human oversight, and 20% stay completely guide.
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 major development past these conventional options, providing capabilities that may intelligently navigate complexity, adapt to dynamic environments, and dramatically cut back guide intervention throughout enterprise workflows.
On this put up, we exhibit 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 complicated, 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 parts 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 robotically scale based mostly on demand.
- Utility integrates with Amazon Bedrock and Amazon Nova Act for AI-powered order automation. AgentCore Browser Software supplies safe, remoted browser surroundings for internet automation. Primary 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 have to course of orders throughout a number of retailer web sites with out native API entry. This workflow demonstrates the total capabilities of AI-powered browser automation, from preliminary navigation by means of complicated decision-making to human-in-the-loop intervention. We’ve 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 online interface or batch CSV add, together with product particulars (URL, measurement, coloration), buyer info, and transport 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 supplies 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 consists of security measures resembling session isolation, built-in observability by means of reside viewing, AWS CloudTrail logging, and session replay capabilities. The system retrieves retailer credentials from AWS Secrets and techniques Supervisor and generates a reside view URL utilizing Amazon DCV streaming for real-time monitoring. The next diagram illustrates the order total workflow course of.
Browser automation with form-filling and order submission
Type-filling represents a vital functionality the place the agent intelligently detects and populates varied discipline sorts 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, coloration choices, and cart buttons. It selects specified choices, provides gadgets to cart, and proceeds to checkout, filling transport info with clever discipline detection throughout completely different retailer layouts. If merchandise are out of inventory or unavailable, the agent escalates to human overview with context about alternate options.
The pattern utility 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 transport handle” and robotically 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 research 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 knowledge. Each approaches robotically adapt to various retailer checkout interfaces, eliminating the brittleness of conventional selector-based automation.
Human-in-the-loop
When encountering CAPTCHAs or complicated challenges, the agent pauses automation and notifies operators by way of WebSocket. Operators entry the reside 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 vital steps, and detailed execution logs with timestamps. Operators monitor progress by means of a real-time dashboard displaying order standing, present step, and progress proportion. For prime-volume eventualities, batch processing helps parallel execution of a number of orders with configurable employees (1-10), priority-based queuing, and computerized retry logic for transient failures.
Conclusion
AI agent-driven browser automation represents a basic shift in how enterprises method 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 larger automation charges throughout complicated, multi-system workflows. The e-commerce order automation instance demonstrates that AI brokers don’t substitute conventional RPA—they permit automation of workflows beforehand thought of too dynamic or complicated for automation, dealing with various consumer interfaces, making contextual choices, and sustaining full compliance and auditability.
As enterprises face mounting strain 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 guide processes, organizations can deploy clever browser automation that adapts to their present know-how panorama. The result’s diminished operational prices, sooner processing instances, improved compliance, and most significantly, liberation of data employees from repetitive knowledge entry and system navigation duties—permitting them to give attention to higher-value actions that drive enterprise impression.
In regards to the authors
Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI staff, the place he has led the design and growth of a number of Bedrock AgentCore providers from the bottom up, together with Runtime, Browser, Code Interpreter, and Id. He beforehand labored on Amazon SageMaker since its early days, launching AI/ML capabilities now utilized by 1000’s of firms worldwide. Earlier in his profession, Kosti was an information scientist. Exterior 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 in addition as a serverless options architect at AWS.
Sanghwa Na is a Generative AI Specialist Options Architect at Amazon Net Companies. Based mostly in San Francisco, he works with clients to design and construct generative AI options utilizing giant language fashions and basis fashions on AWS. He focuses on serving to organizations undertake AI applied sciences that drive actual enterprise worth.
