Choosing between Managed vs Modular AI Agents - Amazon Bedrock Agents vs Amazon Bedrock AgentCore
This blog gives an overview of the difference between Amazon Bedrock Agents and Amazon Bedrock AgentCore
Introduction
The era of 'what is an AI agent' is over. Today, enterprises move beyond experimental prototypes to mission-critical production grade systems. With this, developers face a common crossroads: should they use a fully managed service to ship faster, or a modular infrastructure to gain total control?
To ease this, back in 2023, AWS released Agents for Amazon Bedrock.
Amazon Bedrock Agents: Managed AI
Amazon Bedrock Agents is a fully managed capability that is designed for developers who want to deploy agents without managing the underlying orchestration logic. It uses a "configuration-first" approach where AWS handles the heavy lifting of the Reasoning-Act (ReAct) loop .
Key Benefits:
- Zero-Infrastructure Orchestration: You provide the instructions and the tools (via AWS Lambda), and Bedrock automatically determines the sequence of actions, handles prompt engineering, and manages the state.
- Rapid Deployment: Ideal for common business patterns like "Check my Deliveroo status" or "Summarize the board meeting notes."
- Integrated Knowledge Bases: Cloud Native, one-click integration with Bedrock Knowledge Bases for seamless Retrieval-Augmented Generation (RAG).
However, with complexity of the agents — requiring multi-agent collaboration, custom memory strategies, or open-source frameworks integration — developers require granular control in building their agents.
To simplify this, in 2025, Amazon released Amazon Bedrock AgentCore, which is a suite of modular, serverless services for building production-grade agents.
Amazon Bedrock AgentCore: The Agent Factory
Unlike Bedrock Agents, AgentCore is framework-agnostic. Whether you are using LangGraph , CrewAI , or LlamaIndex , AgentCore provides the secure, scalable infrastructure to run them.
The AgentCore Service Pillars:
Image Source: AWS Documentation
- AgentCore Runtime : The serverless engine for agentic execution. It supports secure, isolated sessions and asynchronous workloads that can run for up to 8 hours, perfect for complex, multi-step background tasks.
- AgentCore Memory : A managed memory layer that persists context beyond a single chat. It provides episodic and semantic memory, allowing agents to "remember" user preferences and past interactions across different sessions.
- AgentCore Gateway : The standardized interface for tool use. It utilizes the Model Context Protocol (MCP) to allow agents to dynamically discover and securely connect to enterprise APIs, databases, and third-party SaaS tools.
- AgentCore Identity : Handles secure, delegated authentication for AI. It allows agents to act on behalf of specific users with fine-grained permissions, integrating natively with AWS IAM and enterprise providers like Okta.
- AgentCore Code Interpreter : A secure, sandboxed Python environment. It empowers agents to execute complex data analysis, generate visualizations, and solve mathematical problems that go beyond the capabilities of standard text generation.
- AgentCore Browser : A managed, headless browsing service for web-based research. It allows agents to securely navigate websites, extract data, and perform multi-step web workflows while keeping the execution isolated from your core network.
- AgentCore Observability : Built on OpenTelemetry, it provides deep tracing of every reasoning step, tool call, and memory retrieval, pushing real-time metrics directly to Amazon CloudWatch.
- AgentCore MCP Server : It helps you transform, deploy, and test AgentCore-compatible agents directly from your preferred development environment. With built-in support for runtime integration, gateway connectivity, and agent lifecycle management, the MCP server simplifies moving from local development to production deployment on AgentCore services. Supported MCP clients: Kiro, Cursor, Claude Code, Amazon Q CLI.
- AgentCore Policy : Security that lives outside the model logic using the Cedar policy language. It defines deterministic "no-go" zones, ensuring agents stay within legal and operational boundaries regardless of the prompt.
- AgentCore Evaluation : A built-in "LLM-as-a-Judge" quality assurance service. It automatically scores agent performance on accuracy and helpfulness, allowing you to catch regressions or hallucinations before they reach production.
Summary
The table below summarises the major feature differences between the two services -
| Feature | Bedrock Agents | Bedrock AgentCore |
|---|---|---|
| Primary Goal | Simplicity & Speed. Fully managed "no-code/low-code" orchestration. | Control & Scale. Infrastructure for custom agents and multi-agent systems. |
| Orchestration | Proprietary AWS orchestration (managed loop). | Bring your own (LangGraph, CrewAI, LlamaIndex, or custom Python). |
| Model Choice | Limited to models supported natively by Bedrock. | Model Agnostic. Any model on Bedrock or external models via API. |
| Execution | Orchestrated by Bedrock; you only provide Lambda "Action Groups." | AgentCore Runtime. Serverless microVMs (Fargate-like) running your full agent code. |
| Memory | Managed session memory (standard context). | Advanced Memory. Persistent, episodic, and long-term memory across sessions. |
| Connectivity | Action Groups (Lambda) and Knowledge Bases. | AgentCore Gateway. Open-standard MCP (Model Context Protocol) and A2A support. |
| Security | Bedrock Guardrails and IAM. | Deep Isolation. Per-session microVM isolation and native Identity (OAuth/Cognito). |
| Complexity | Best for linear, task-oriented workflows. | Best for complex, long-running (up to 8h), and multi-agent systems. |
When and What to choose?
Select Amazon Bedrock Agents if:
- You want to move from idea to production in days. If your workflow is relatively linear and you want AWS to handle the complexities of the agent’s "thinking" process, Bedrock Agents is the most efficient choice.
Go for Amazon Bedrock AgentCore if:
- You are building an "Agentic Enterprise." If you require custom coordination between multiple specialized agents, need to maintain long-term memory across thousands of sessions, or must enforce strict, auditable policies on how AI interacts with your production APIs, AgentCore provides the necessary modularity.
Get Started Today
Both services are available in the Amazon Bedrock Console . You can start by building a prototype with Bedrock Agents and, as your requirements evolve, migrate your logic to AgentCore for deeper customization.
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