Amazon Bedrock AgentCore Introduces Proxy, Profile, and Extension Controls for AI Web Browsing

Amazon Bedrock AgentCore Introduces Proxy, Profile, and Extension Controls for AI Web Browsing

Amazon Web Services (AWS) has announced a significant upgrade to its Amazon Bedrock AgentCore platform, introducing three critical enhancements designed to give enterprises granular control over how AI agents interact with the web: proxy configuration, browser profiles, and browser extensions. These features collectively empower organizations to tailor AI browsing behavior to meet stringent security, compliance, and operational requirements — a crucial step as AI-driven automation increasingly assumes complex web tasks ranging from data aggregation to customer service orchestration.

According to AWS’s official documentation and sample repositories, proxy configuration allows users to route AI agent traffic through designated servers, enabling geographic targeting, data sovereignty compliance, and network-level filtering. This is particularly valuable for multinational corporations subject to regional data regulations such as GDPR or China’s PIPL. By specifying proxy endpoints, organizations can ensure AI agents do not inadvertently access or transmit data across unauthorized jurisdictions.

Browser profiles provide a mechanism for isolating session states, cookies, and authentication tokens between different AI tasks. Unlike traditional stateless web scraping, these profiles enable persistent, context-aware browsing — for example, an AI agent can maintain login sessions for multiple enterprise SaaS platforms simultaneously without cross-contamination. This capability significantly improves efficiency and reduces the risk of authentication failures or session timeouts during multi-step workflows.

Perhaps the most innovative addition is support for browser extensions. AI agents can now load and execute custom extensions, such as ad blockers, privacy enhancers, or domain-specific scrapers, to modify web page rendering or extract structured data more effectively. This transforms AI agents from passive observers into active participants that can adapt their behavior based on real-time page content. For instance, a financial research agent could deploy a financial data parser extension to automatically extract earnings call metrics from investor relations pages, bypassing the need for brittle HTML parsing logic.

While GitHub repositories such as awslabs/amazon-bedrock-agentcore-samples provide practical code examples for implementing these features, the underlying infrastructure is built on AWS’s robust SDK ecosystem, as documented in the bedrockagentcore Go package. Developers can now programmatically configure these settings via API calls, integrating them into CI/CD pipelines and infrastructure-as-code workflows.

Industry analysts note that these enhancements signal a broader shift in enterprise AI adoption — away from monolithic, black-box models toward configurable, auditable agents. "This isn’t just about better web scraping," said Dr. Lena Torres, an AI governance researcher at the Center for Digital Ethics. "It’s about building trust. When you can audit exactly which proxy an agent used, which cookies it retained, and which extensions it loaded, you can begin to explain its decisions — a prerequisite for regulatory approval and internal accountability."

Merriam-Webster defines "customize" as "to make or adapt for a particular purpose or person," a definition that now finds new relevance in the AI domain. With these updates, AWS is not merely adding features — it is enabling enterprises to shape AI behavior with the same precision as human operators. This level of control is essential as AI agents move from experimental tools to mission-critical systems in finance, healthcare, legal research, and supply chain management.

Organizations looking to deploy these capabilities should begin by auditing their existing web access policies, identifying which agents require isolation or geographic restrictions, and selecting appropriate extensions for their use cases. AWS has provided starter templates in its GitHub repository to accelerate implementation, but developers are encouraged to conduct thorough security reviews before deploying extensions in production environments.

As AI agents become more autonomous, the ability to customize their digital footprint — not just their logic — will define the next frontier in responsible AI deployment. With proxy, profile, and extension controls, Amazon Bedrock AgentCore is setting a new standard for enterprise-grade AI web interaction.