MoEngage Leverages AWS AgentCore Gateway to Securely Integrate 400+ Internal APIs for AI Agents

MoEngage, a customer engagement platform, has successfully deployed AWS AgentCore Gateway to integrate over 400 internal REST APIs with its AI agents. These APIs, developed by 25 engineering teams and running in private virtual private clouds (VPCs) across seven AWS Regions, were previously isolated due to data residency and security requirements. The AgentCore Gateway provides a secure and managed way to expose these APIs to internal AI agents, addressing the critical need for AI agents to access existing business logic and data without compromising security or exposing internal systems to the public internet. This development is highly significant for enterprise practitioners. It tackles a common hurdle in AI adoption: how to operationalize AI agents by connecting them to an organization's vast ecosystem of existing applications and data. By centralizing API integration through AgentCore Gateway, MoEngage's product teams no longer need to manage complex networking, identity, or security policies for each agent integration. This dramatically lowers the barrier to entry for developing and deploying AI agents that can leverage the company's full range of capabilities, leading to faster innovation and more efficient workflows. The solution also ensures that data residency commitments are met and provides a consistent security model. This move by MoEngage aligns with a broader trend in enterprise AI, where the focus is shifting from pure experimentation to secure, scalable, and governed production deployments. As AI agents become more sophisticated and autonomous, the need for robust control planes that manage their access to internal systems and data becomes paramount. Solutions like AWS AgentCore Gateway facilitate this by providing a standardized, secure, and auditable mechanism for agent-to-API communication. This approach is a natural evolution of API management and microservices architectures, extending governance and security principles to the burgeoning field of agentic AI. Other cloud providers are also focusing on integrated platforms that bring together infrastructure, data, models, applications, and developer tools to support enterprise AI. In practice, this means that DevOps and cloud engineers should prioritize understanding and implementing agent orchestration and API gateway solutions for their AI initiatives. Key considerations include establishing clear security policies, leveraging identity and access management (IAM) for agent authentication, and ensuring auditability of agent actions. Organizations should also evaluate how such gateways can reduce the operational burden on individual product teams, allowing them to focus on agent logic rather than infrastructure concerns. The trade-off often involves centralizing some control to gain broader, secure access for AI agents, which is a necessary step for scaling enterprise AI effectively. Practitioners should look for solutions that offer fine-grained access control, robust logging, and seamless integration with existing cloud security frameworks.