Amazon Bedrock AgentCore: Bringing Agentic AI to Production
The gap between building an impressive AI agent demo and shipping it to production has historically been enormous. With Amazon Bedrock AgentCore, AWS closes much of that gap. AgentCore provides the unglamorous but critical infrastructure needed to run AI agents at enterprise scale, while Amazon Q Developer and emerging standards like the Model Context Protocol (MCP) round out a practical stack for agentic workflows that work in the real world.
Why Agentic Workflows Matter
Most AI implementations today are glorified chatbots: you ask a question, you get an answer, and the conversation ends. Agentic workflows are different. They maintain context, make decisions, take actions, and learn from outcomes across extended interactions.
The business impact shows up in familiar places. In customer support, agents can remember previous issues and proactively suggest solutions. In software delivery, they can understand a team’s codebase and deployment patterns. In analytics, they adapt their approach based on data quality and business context. The problem is that many promising demos fall apart at scale—session management becomes brittle, memory usage spirals, and security is bolted on after the fact.
What AgentCore Provides
Released in September 2025, Amazon Bedrock AgentCore is production infrastructure for agentic workflows. It addresses the specific challenges that typically stall projects on their way to production.
Runtime
The AgentCore Runtime offers a serverless environment that supports your preferred frameworks—whether that’s LangGraph, CrewAI, or a custom implementation. It handles multimodal workloads, supports long-running tasks, isolates sessions for safety, and keeps latency low for real-time interactions. Instead of building and operating this scaffolding yourself, you can focus on the agent logic.
Memory
Memory is where most agent implementations struggle. AgentCore manages both session memory, which preserves context within a conversation, and long-term memory, which lets agents improve across conversations. You don’t need to assemble bespoke storage and retrieval layers to get reliable persistence.
Identity
Agents often need to authenticate with multiple services, apply different permission levels by context, and rotate credentials without breaking workflows. AgentCore Identity centralizes this complexity, integrating with AWS IAM and supporting OAuth 2.0 so agents can securely access AWS services, third-party APIs, and internal systems.
Observability
When an agent makes a mistake, you need to understand why. AgentCore Observability provides step-by-step visibility into execution: how decisions were made, which tools were used, and how the system is performing over time. That level of insight is essential for debugging and for building confidence in production.
Amazon Q as a Development Partner
Amazon Q Developer complements AgentCore by accelerating the build-measure-learn loop. Beyond code generation, Q can analyze an existing codebase, suggest agent implementations that fit established patterns, propose configurations aligned with your AWS environment, recommend security policies for agent-to-service interactions, and help debug behaviors by examining execution traces.
The Role of MCP
The Model Context Protocol (MCP) is emerging as a standard for connecting agents to tools and data sources. With AgentCore Gateway, existing APIs can be presented as MCP-compatible interfaces that agents can securely discover and use. This reduces one-off integrations, standardizes how capabilities are exposed, and simplifies version management and permissions.
Architecture and Risk Management
A minimal production architecture brings together the runtime, memory, identity, observability, and gateway pieces into a cohesive stack. The result is a system that scales with traffic, preserves context across sessions, provides secure access to external services, and offers the telemetry needed to optimize.
Running agents in production still requires guardrails. Costs can grow through excessive API calls, large memory footprints, or long-running tasks. Security demands least-privilege access and regular audits. Quality assurance matters because errors can compound over extended workflows. With cost monitoring, access controls, and validation steps in place, these risks can be managed effectively.
From Demos to Production
AgentCore marks a maturation of agentic AI infrastructure. Instead of reinventing the foundation, teams can focus on delivering business value while AWS handles the operational burden. Together, AgentCore’s infrastructure, Amazon Q’s development assistance, and MCP’s standardized integrations make it practical to move beyond demos and ship agentic workflows at enterprise scale.
The pragmatic path forward is to start with a focused pilot on a single use case using the AgentCore Runtime, accelerate development with Amazon Q, and use Observability to understand agent behavior. Once validated, expand to additional scenarios with the confidence that the underlying platform can scale.
The future of enterprise software is agentic—and with AWS, that future is now within reach for production teams.