What is Amazon Bedrock AgentCore? AI Agent Platform for Enterprise Deployment

Most AI agents never make it to production. The problem usually isn't the agent—it's the infrastructure.

Frameworks make it straightforward to create agents that reason, use tools, and automate workflows. But getting those agents into production reveals a hard truth: agents need infrastructure that traditional applications don't. 

Session isolation to prevent data leakage. Persistent memory across conversations. Identity management for third-party services. Tool connectivity at scale. Observability into autonomous decisions. Building this infrastructure from scratch means months of engineering work replicating what cloud providers already perfected. 

Amazon Bedrock AgentCore provides that infrastructure. Released to general availability in October 2025, it delivers seven fully-managed services purpose-built for production agents: runtime, memory, identity, gateway, code interpreter, browser tool, and observability. With over one million SDK downloads, AgentCore has become the platform that moves agents from prototype to production.

The Six Barriers Preventing AI Agent Production

Understanding why agents fail to reach production requires examining the specific challenges that emerge when moving from demo to deployment.

Security and Scaling Complexity

Agents don't operate in isolation. They access sensitive data, invoke APIs, and make decisions affecting business operations. Each agent session needs complete isolation to prevent data leakage between users or sessions. Scaling infrastructure to handle unpredictable workloads while maintaining this isolation requires sophisticated orchestration. Building this from scratch means replicating the engineering work that cloud providers spent years perfecting.

Memory Management Without Infrastructure

Effective agents need memory. Short-term memory maintains context within a conversation. Long-term memory enables agents to learn from past interactions and apply that knowledge to new situations. Managing this memory across sessions, storing it securely, and retrieving it efficiently requires a robust database infrastructure, caching layers, and effective data governance policies. Few organizations want to build and maintain this infrastructure themselves.

Identity and Access Control Across Systems

An agent helping a customer service representative needs different permissions than an agent supporting a security analyst. Agents must integrate with existing identity providers, respect authorization boundaries, and maintain audit trails. When agents access third-party APIs on behalf of users, they need secure credential management and OAuth workflows. Implementing this correctly is complex, and mistakes create security vulnerabilities.

Tool Integration Friction

Agents create value by using tools: calling APIs, querying databases, executing code, and browsing websites. Each tool needs to be transformed into a format that agents can understand. APIs designed for human developers require adaptation for agent consumption. Connecting to existing systems involves writing integration code, handling authentication, managing rate limits, and handling failures gracefully.

Resource Discovery at Scale

As organizations deploy multiple agents with access to dozens or hundreds of tools, discovery becomes critical. Agents require a semantic understanding of available tools to select the most suitable one for each task. Building this discovery layer requires vector databases, embedding models, and intelligent routing logic. The complexity compounds as the tool catalog grows.

Monitoring and Compliance Requirements

Production systems require observability. Teams need visibility into what agents are doing, why they made specific decisions, and how they're performing. Compliance requirements demand detailed audit trails. Debugging agent behavior requires tracing through multi-step reasoning processes. Building comprehensive monitoring for dynamic, autonomous systems presents challenges that traditional application monitoring tools weren't designed to address.

Organizations face an unsatisfying choice: invest engineering resources in building this infrastructure, or limit agent ambitions to simple use cases. The former diverts resources from building business value. The latter leaves transformative opportunities unexplored.

Amazon Bedrock AgentCore: Infrastructure for the Agent Era

Amazon Bedrock AgentCore provides a comprehensive agent platform with fully managed services designed specifically for deploying and operating AI agents at enterprise scale. Rather than forcing organizations to choose between open-source flexibility and enterprise-grade infrastructure, AgentCore delivers both.

The platform consists of seven integrated services, each addressing one of the core challenges in agent deployment. These services work together seamlessly but can also be used independently, giving organizations flexibility in how they adopt AgentCore.

1. AgentCore Runtime: The Foundation

AgentCore Runtime provides secure, serverless infrastructure for deploying agents. Each agent session runs in complete isolation, utilizing microVM technology, which prevents data leakage. Runtime supports both low-latency real-time interactions and extended runtimes up to eight hours for complex workflows. This represents the longest session duration available in the industry for asynchronous agent workloads. The serverless nature means automatic scaling without capacity planning.

2. AgentCore Memory: Context Without Complexity

AgentCore Memory simplifies memory management by providing both short-term memory for conversation context and long-term memory that persists across sessions. Agents can store and retrieve information with just a few lines of code. Organizations maintain complete control over what agents remember, without needing to manage databases or implement caching layers.

3. AgentCore Gateway: Universal Tool Connectivity

AgentCore Gateway automatically transforms existing APIs and AWS Lambda functions into agent-compatible tools. The service supports the Model Context Protocol (MCP) and provides context-aware semantic search for tool discovery. Instead of writing custom integration code for each tool, developers describe their APIs, and Gateway handles the transformation.

4. AgentCore Identity: Enterprise-Grade Access Management

AgentCore Identity integrates with existing identity providers, including Microsoft Entra ID, Amazon Cognito, and Okta. Agents inherit authentication and authorization context from users. Identity manages API keys and OAuth credentials securely, handling the complexity of third-party authentication while maintaining authorization boundaries.

5. AgentCore Code Interpreter: Safe Execution

AgentCore Code Interpreter enables secure code execution in isolated sandbox environments. Agents can write and execute JavaScript, TypeScript, and Python code for data analysis, calculations, and workflow automation. VM-level isolation prevents code execution from affecting other agents or accessing unauthorized resources.

6. AgentCore Browser Tool: Web Interaction at Scale

AgentCore Browser Tool provides a cloud-based browser runtime with sub-second latency. Agents can navigate websites, fill forms, and extract information. The browser includes observability features, such as Live View and Session Replay, for debugging agent behavior.

7. AgentCore Observability: Complete Visibility

AgentCore Observability provides detailed insights into agent execution, including reasoning steps, tool invocations, and latency breakdowns. The service integrates with existing monitoring tools through OpenTelemetry compatibility. Teams can trace through decision-making processes to understand agent behavior and troubleshoot production issues.

These seven services address every major barrier to agent production deployment. Organizations no longer need to choose between building their own infrastructure or limiting their agent ambitions.

The Framework-Agnostic Advantage

One of AgentCore's most significant differentiators is its framework-agnostic architecture. The platform works with any agent framework, including CrewAI, LangGraph, LlamaIndex, Google ADK, OpenAI Agents SDK, and AWS's Strands Agents.

This flexibility matters for several reasons. Different teams have different preferences and areas of expertise. Rather than forcing standardization, AgentCore lets teams work with familiar tools. Framework agnosticism also future-proofs investments. New frameworks emerge constantly with innovative approaches. Organizations can experiment with new frameworks or migrate between them without having to rebuild their infrastructure.

Model flexibility extends this advantage. Agents can use any foundation model available on Amazon Bedrock, or connect to models from OpenAI, Anthropic, or other providers. Organizations select the best model for each use case rather than being constrained by platform limitations. As model capabilities evolve, teams can swap models without needing to rewrite their applications.

The framework-agnostic approach enables gradual adoption. Organizations can start with one framework, prove value, then expand to others as different teams and use cases emerge. There's no requirement to standardize before beginning. This accelerates time to value and reduces organizational friction.

Security and Scale: Built for Enterprise

Enterprise adoption requires assurance that systems meet security, compliance, and operational standards.

AgentCore Runtime provides session-level isolation using microVM technology. Each agent session runs in its own isolated computing environment, preventing data leakage. Memory isolation ensures conversation context remains private. Network access is controlled, preventing agents from reaching unauthorized resources.

For organizations with strict network isolation requirements, AgentCore supports deployment within Amazon Virtual Private Clouds (Amazon VPC). Agents can access internal resources without having to traverse the public internet. Integration with existing network architecture happens through standard AWS networking controls.

AgentCore Identity integrates seamlessly with existing enterprise identity providers. Organizations don't need to replicate user directories or implement new authentication systems. Agents inherit authentication context from existing systems, respecting the same access controls that apply to human users.

AgentCore Observability provides detailed logging and tracing required for compliance. Every agent action generates audit trails showing what was done, by whom, and why. These logs integrate with existing SIEM systems for centralized compliance monitoring.

Agent workloads make traditional capacity planning impossible. Usage patterns are unpredictable. A single request might complete in seconds or run for hours. AgentCore Runtime handles this complexity automatically, scaling in response to actual demand without manual intervention.

Real-World Impact: Organizations Building with AgentCore

AgentCore has moved beyond early adopters into production deployments at some of the world's most demanding organizations.

Ericsson is transforming telecommunications R&D with AgentCore. Their 3G/4G/5G/6G systems span millions of lines of code across thousands of interconnected subsystems. AgentCore powers agents that help engineering teams navigate this complexity, scaling to double-digit productivity gains across a workforce in the tens of thousands. According to Dag Lindbo, Head of AI and Emerging Technologies in Business Area Networks at Ericsson, "AgentCore also lets us use any agent framework, which is critical to help us scale across many teams and use cases."

Itaú Unibanco, one of Latin America's largest banks, uses AgentCore to support hyper-personalized, secure digital banking experiences. Banking presents unique challenges: stringent regulatory requirements, sensitive customer data, and the need for explainable decision-making. AgentCore's security features enable deployment in customer-facing roles while meeting compliance requirements.

Innovaccer built a Healthcare Model Context Protocol (HMCP) on top of AgentCore Gateway. This protocol enables healthcare-specific agents to access clinical data and medical knowledge bases through standardized interfaces. Healthcare represents perhaps the most demanding environment for AI agents, with life-critical decisions, strict privacy requirements, and complex system integration needs.

The diversity of industries adopting AgentCore signals platform maturity. Telecommunications infrastructure, financial services, and healthcare each present distinct challenges. The fact that a single platform addresses all these use cases demonstrates the comprehensive capabilities of AgentCore.

Getting Started and Why Partner Expertise Matters

AgentCore lowers the barrier to entry for organizations wanting to move beyond agent prototypes. The platform includes starter toolkits that enable developers to deploy their first production agent in under an hour. 

The platform uses consumption-based pricing with no upfront commitments. Runtime, Browser, and Code Interpreter services are priced per second based on usage. Gateway charges per tool invocation. Memory costs are based on data volume. This pricing model aligns costs with value.

AWS Marketplace now includes pre-built agents and tools designed to work with AgentCore. This enables organizations to discover, purchase, and deploy agents developed by AWS Partners without needing to start from scratch.

Having access to tools doesn't automatically mean knowing how to use them effectively. Organizations still face decisions about architecture, framework selection, security configuration, and deployment patterns.

Successful implementation requires expertise that most organizations lack in-house. Architecture design must match specific use cases. Integration with existing systems requires navigating decades of technical debt. Security configuration must align with organizational policies and compliance requirements. Framework and model selection require an understanding of the trade-offs between different options.

AWS Partners who specialize in AgentCore implementations bring experience from multiple deployments. They've seen common integration challenges and know how to address them. This experience compresses the learning curve and helps organizations avoid pitfalls that seem obvious only in hindsight.

Mission: Your Partner for AgentCore Success

Mission, a CDW Company, is an AWS GenAI Competency Partner, a designation awarded by AWS to partners who demonstrate deep expertise and proven success in generative AI implementations. With over 150 GenAI solutions built on AWS, Mission brings validated technical capability and real-world experience to every engagement.

Mission follows a structured methodology designed to efficiently move organizations from proof of concept to production. The process begins with discovery and use case identification, then moves through architecture design, security configuration, implementation, and ongoing optimization. This approach strikes a balance between speed and risk management, enabling organizations to realize value quickly while building production-grade systems.

The team's expertise extends across the entire AWS ecosystem. Agents don't exist in isolation. They integrate with data lakes, analytics platforms, security services, and application infrastructure. Mission's comprehensive AWS knowledge enables implementations that leverage the full platform rather than just individual services.

Mission understands the realities of enterprises: existing systems, compliance requirements, and organizational dynamics that influence technology decisions. This experience helps organizations navigate the gap between technical possibilities and practical implementation.

Ready to move your AI agents from prototype to production?

Contact Mission today to discuss your AgentCore implementation and discover how AI agents can transform your business operations.