Revolutionizing higher education: How Amazon Bedrock AgentCore enables production-ready AI at scale

Higher education’s AI journey is defined not by lack of ambition, but by entrenched obstacles that slow true transformation. According to UPCEA, universities are eager to move toward an era where agentic AI—systems that proactively review student work, generate feedback, and automate administrative operations—becomes an everyday reality. Yet, most institutions find themselves “stuck in pilot purgatory,” unable to scale AI campus-wide due to significant barriers: fragmented legacy systems, complex integrations, privacy and regulatory demands, limited IT capacity, and uncertainty about managing rapid technological change.

Amazon Bedrock AgentCore, a new AI service released by Amazon Web Services (AWS), enables developers to deploy and operate AI agents with the scale, reliability, and security critical to real-world applications. It provides purpose-built infrastructure to scale agents securely, powerful tools to enhance agent capabilities, and essential controls to ensure trustworthy operations. It was designed to overcome various challenges for higher education. Its modular, purpose-built infrastructure and secure building blocks enable educational institutions to rapidly unify fragmented systems and deploy advanced AI agents at scale. Amazon Bedrock AgentCore offers five modular capabilities:

• Amazon Bedrock AgentCore Runtime
• Amazon Bedrock AgentCore Memory
• Amazon Bedrock AgentCore Gateway
• Amazon Bedrock AgentCore Identity
• Amazon Bedrock AgentCore Observability

The following diagram illustrates how these Amazon Bedrock AgentCore modular capabilities can work together.

Figure 1: Amazon Bedrock AgentCore offers modular capabilities to meet higher education requirements

AgentCore Runtime provides a secure, low-latency serverless environment with session isolation and a framework-agnostic environment—such as LangGraph, Strands, and CrewAI—that lets universities deploy, scale, and update AI tutors, research assistants, and administrative bots across any models offered by Amazon Bedrock, Anthropic’s Claude, Google Gemini, and OpenAI. This allows institutions to innovate rapidly without infrastructure bottlenecks or vendor lock-in. AgentCore Runtime also lets AI agents communicate with other tools using Model Context Protocol (MCP) and extends long-running workloads up to 8 hours, enabling complex agent reasoning and asynchronous workloads.

AgentCore Memory delivers managed, context-aware memory services for both short-term and long-term memory, enabling AI learning assistants and advisors to remember individual student progress, student preferences, semantic facts, interactions, and educational journeys over time—so every interaction is tailored for sustained learning success.

AgentCore Gateway acts as a universal integration layer by converting APIs, AWS Lambda functions, and existing services or databases into MCP-compatible tools. This empowers agentic AI to securely connect with learning management systems, library databases, student information systems, and external APIs and creates seamless digital workflows for students and staff across the campus from querying databases to sending messages to analyze documents. Moreover, AgentCore Gateway provides both comprehensive ingress and egress authentication in a fully managed service. The following figure shows the AgentCore Gateway workflow.

Figure 2: AgentCore Gateway allows developers to convert APIs and services into MCP-compatible tools

AgentCore Identity delivers secure authentication, authorization, and credential management capabilities that enable AI agents and tools to access AWS resources and third-party services on behalf of users and integrates deeply with campus identity providers. This means granular, role-based access for all users and agents, which is essential for safe, compliant, multi-role collaboration in academic environments.

AgentCore Observability provides advanced agent-specific tracing, debugging, real-time monitoring, and audit-ready logging in production environments through Amazon CloudWatch and standardized OpenTelemetry (OTel)-compatible format. This enables universities to track AI agent actions, support accountability, and continually optimize student outcomes. It offers detailed visualizations of each step in the agent workflow, enabling a campus to inspect an agent’s execution path, audit intermediate outputs, and debug performance bottlenecks to optimize agent performance. The following diagram shows AgentCore Observability dashboards.

Figure 3: AgentCore Observability offers real-time monitoring, debugging, and tracing of the AI agent workflow

AgentCore built-in tools empower higher education institutions with two tightly integrated, secure, and fully managed tools: Code Interpreter and Browser Tool. Together, these built-in capabilities create an advanced environment for AI-powered agents to perform both computational and web-based tasks—enabling entirely new academic workflows and research automation, all within a safe, compliant, and scalable framework.

Real benefits for higher education

Amazon Bedrock AgentCore empowers higher education institutions to finally move beyond AI experimentation to deliver intelligent, personalized support at a campus-wide scale. By providing secure infrastructure and managed memory, AgentCore enables universities to launch persistent AI tutors, research assistants, and digital advisors that adapt to each student’s journey. With seamless integration into learning management, student information, and library systems through AgentCore Gateway, universities can automate manual processes that strain IT and administrative resources. Due to the robust observability and role-based access controls of AgentCore, automation is transparent, auditable, and compliant with evolving privacy and security standards.

Finally, the open, modular, and extensible architecture of AgentCore future-proofs educational innovation. By supporting any AI framework or model and providing powerful built-in tools for code execution and web-based automation, universities can rapidly build, deploy, and adapt new solutions whether for classroom learning, institutional research, or student services.

Next steps

Amazon Bedrock AgentCore is currently available in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Frankfurt) AWS Regions. For pricing information, refer to the Amazon Bedrock AgentCore Pricing page. To learn more, visit the Introducing Amazon Bedrock AgentCore: Securely deploy and operate AI agents at any scale (preview) in the AWS News Blog and explore in-depth implementation in the Amazon Bedrock AgentCore documentation.