A Technical Overview for Architects, Engineers, and Decision Makers
Section 1: Introduction
Before diving into AgentCore, let’s make the idea of Agentic AI concrete and relatable. In simple terms, we can say that an AI Agent behaves like LLMs in a loop: that is, an AI system that repeatedly reasons about your goal, decides the next step, takes actions with tools, observes results, and continues until the goal is reached. Instead of a single prompt-in/answer-out exchange, it maintains persistent context across steps and sessions, adapting as new information appears. The main steps involve:
- Thinking: analyze the request and plan the next step;
- Deciding: choose to call a tool or ask a clarifying question;
- Acting: invoke tools/APIs (search, knowledge bases, AWS SDKs, internal services);
- Observing: read tool results and update context/memory;
- Iterating: repeat until a stopping condition (goal met, human approval, or guardrail block).
This think-decide-act-observe loop gives agents autonomy: they can decompose problems, use tools to take real actions, and incorporate guardrails, policies, and feedback into their decisions. The Strands Agents framework structures this loop and manages state so you focus on capabilities, not plumbing.
Takeaway: Use an agent when success requires multi-step decisions, tool usage, changing strategies, and measurable progress toward a goal. Choose the simpler alternatives for fixed, deterministic flows or one-off transformations; Strands lets agents handle the rest by orchestrating the loop, preserving context, and enforcing guardrails.
Deploying production-ready agents to AWS using Bedrock AgentCore
Amazon Bedrock AgentCore represents AWS's latest evolution in AI agent technology, announced in July 2025 at the AWS Summit New York. Unlike experimental AI applications, AgentCore is designed specifically for enterprise-grade production deployments, where scale, security, and reliability are paramount.
AgentCore enables developers to deploy and operate highly effective agents using any framework, model, or protocol. Whether you're working with LangGraph, CrewAI, or Strands Agents, AgentCore provides the infrastructure backbone to bring these agents into production environments. The platform is composable, meaning you can use its services independently or together based on your specific requirements.
Most importantly, AgentCore doesn't lock you into proprietary solutions; it enhances your existing open-source workflows with enterprise capabilities that would typically require months of custom infrastructure development.
Why AgentCore
Traditional AI agent development faces significant hurdles when transitioning from prototype to production. Teams often struggle with session management, where multiple users accessing the same agent can interfere with each other's conversations. Security becomes complex when agents need access to both internal systems and external APIs while maintaining proper authentication and authorization.
Memory management presents another challenge: agents need to remember context across conversations while efficiently handling both short-term dialogue state and long-term learned behaviors. Infrastructure scaling demands expertise in containerization, load balancing, and monitoring, which diverts focus from core agent logic.
AgentCore addresses these challenges through purpose-built services that handle infrastructure complexity, allowing developers to focus on agent intelligence rather than operational concerns. This architectural approach transforms agent development from a custom infrastructure project into a focused application development effort.
Amazon Bedrock AgentCore is AWS's managed orchestration service that transforms foundation models into autonomous agents capable of reasoning, planning, and executing complex multi-step tasks. Unlike traditional chatbots that simply generate responses, AgentCore enables models to interact with external systems, retrieve real-time data, and maintain conversation state across extended workflows.
Why It Matters: Traditional AI implementations require extensive custom orchestration code to connect models with business systems. AgentCore eliminates this complexity by providing managed reasoning, automatic tool selection, and robust error handling, reducing development time from months to weeks while ensuring enterprise-grade reliability.
Section 2: Core Concepts
Key Building Blocks
Amazon Bedrock AgentCore includes eight modular services that function independently or together. Runtime offers secure, serverless hosting with session isolation; Identity handles authentication; Memory manages context and knowledge. Gateway converts APIs into agent tools, Code Interpreter runs secure code, and Browser enables web automation. Observability supports monitoring via OpenTelemetry and CloudWatch. Evaluations use an "LLM-as-a-judge" approach to automate the assessment of agent behavior based on specific criteria. Policy provides a layer of deterministic governance and compliance by applying guardrails and safety checks across agent operations to intercept and then verify an agent’s intended action against defined business rules before an action is executed. Each service meets enterprise needs like scalability, integration, and visibility. A diagram shows how these components interact.
AgentCore Runtime
Runtime is the foundational service for hosting agent applications, offering session isolation to prevent data leakage and ensure consistent performance. It supports extended execution up to 8 hours, far beyond traditional serverless limits, and handles scaling, security, and both HTTP and MCP protocols.
Runtime is framework-agnostic, compatible with LangGraph, CrewAI, and custom Python agents. It features fast cold starts (300–800 ms), true session isolation via microVMs, and built-in identity integration. Payloads up to 100 MB are supported, enabling rich data processing in a single call.
Takeaway: Like the engine that drives the core parts of the platform to move.
| Framework and model-independent deployment | Use case independent | Secure workload with enterprise-grade isolation |
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AgentCore Identity
Identity is the secure access layer that enables agents to operate safely and at scale. It integrates with existing enterprise identity providers, avoiding user migration and accelerating deployment without sacrificing security. Through fine-grained permission delegation and a secure token vault, agents gain scoped access to AWS, third-party APIs, and enterprise systems—minimizing consent fatigue.
What sets Identity apart is its ability to bind agent behavior to authenticated context, enforce role-based access, support persona switching, and maintain audit trails. It empowers developers to build secure, contextual agent experiences without rebuilding identity infrastructure.
Takeaway: Like the key that starts the engine.
AgentCore Policy
Policy provides real-time, deterministic controls for agent behavior and tool access. It is built on the Cedar policy language (the same engine behind Amazon Verified Permissions), allowing developers to define what an agent is allowed to do in a human-readable, auditable format.
Takeaway: Like the steering and brakes that control the engine.
- Deterministic Controls: Hard limits on agent actions (e.g., "Never delete files").
- Cedar-based Logic: Human-readable policies that are easy to audit.
- Safe Tool Invocation: Verifies permissions before the Gateway executes a call.
AgentCore Memory
Memory is the intelligent service that lets agents retain and recall information across interactions without complex infrastructure. It offers precise control over what agents remember, forget, or share, supporting both short-term and long-term memory for continuity and personalization.
Designed for developers, Memory delivers high retrieval accuracy, scoped sharing, and seamless runtime integration—whether for single agents or collaborative fleets. It’s the contextual backbone that makes AI feel intelligent.
Takeaway: Like the fuel that energizes the engine.
- Short-term memory: smooth multi-turn conversations
- Long-term memory: shareable across agents and sessions
| Short-term and long-term memory management | Fully managed and secure | Retrieval and extraction of memory |
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AgentCore Gateway
Gateway is a fully managed AWS service that connects agents to tools, transforming APIs, Lambda functions, and enterprise services into agent-compatible interfaces. It eliminates the need for custom wrappers or manual security, offering scalable, secure tool invocation.
Agents interact with existing systems via standardized protocols, with built-in authentication, input/output handling, and dynamic tool registration. Gateway integrates with AgentCore Identity for scoped access and auditability, enabling fast, secure deployment.
Takeaway: Like the gear that transmits the engine’s power to the wheels.
- Semantic search for tool discovery
- Built-in security and encryption
- Low-latency access to thousands of tools
| Simplify tool development and integration | Secure and unified tool access | Intelligent tool discovery capabilities |
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AgentCore Code Interpreter
Code Interpreter is a secure sandbox that lets agents run dynamic code with enterprise-grade security and flexible configuration. It supports Python, R, and domain-specific code, integrating with popular frameworks for data analysis and automation.
Developers can set resource limits, control packages, and tailor environments. Each sandbox is ephemeral, scoped, and auditable—enabling safe, real-time execution for financial models, scientific tasks, or operational logic.
Takeaway: Like the software development toolkits (SDKs) used to program and manage the Engine Control Unit (ECU)
- Sessions: 15 mins default, up to 8 hours
- Isolated microVMs are wiped after each use for compliance
AgentCore Browser
Browser is a cloud-native runtime that lets agents interact with the web securely and at scale. It offers fast, managed access for navigating sites, extracting content, and performing actions—without infrastructure overhead.
With session isolation, network controls, and audit logging, agents can render pages, execute JavaScript, and handle complex workflows. Built-in observability and automatic cleanup ensure secure, scalable automation across any website.
Takeaway: Like the user-interface (UI) browser used to display an integrated development environment (IDE) of the Engine Control Unit (ECU)
- Sessions: 15 mins default, up to 8 hours
- Live view, network logs, console logs
- Secure cleanup after each session
AgentCore Observability
Observability is the diagnostics layer that gives developers deep insight into agent behavior in production. It offers unified dashboards and OpenTelemetry-compatible telemetry for tracing, debugging, and optimizing workflows.
The service captures execution data like tool usage, memory access, and latency, with support for alerts, analysis, and visualizations. Integrated across the AgentCore stack, it provides an end-to-end view that helps teams iterate quickly and maintain enterprise-grade reliability.
Takeaway: Like the health condition dashboard to monitor the Live performances of the Engine Control Unit (ECU) in production
- Step-by-step execution traces
- OpenTelemetry integration
- Unified dashboards for performance debugging
AgentCore Evaluations
Evaluations is a fully managed service designed to assess the quality, accuracy, and reliability of AI agents across thousands of simulated scenarios. Because agents are non-deterministic (they might take different paths to solve the same problem), Evaluations provide a structured way to measure success using "AI-as-a-judge" and predefined benchmarks.
It allows developers to run batch tests on agentic workflows to verify that the agent selects the right tools, follows the correct logic, and provides accurate answers without regressing when code or models are updated.
Takeaway: Like the “dry run” as a systematic testing ground to ensure the success of the deployment
- Managed Testing at Scale: Run thousands of test cases automatically to simulate user interactions.
- Built-in Evaluators: Uses specialized models to grade agent performance on accuracy, helpfulness, and safety.
- Continuous Assessment: Integrates into CI/CD pipelines to ensure new agent versions meet quality bars before going live.
Key Differentiators
| Aspect | Bedrock Agent Configuration-first Product | AgentCore Code-first Infrastructure |
|---|---|---|
| Level | Managed SaaS Solution: High-level managed service to build end-to-end agents for speed-to-market | Modular Infrastructure: Low-level "primitives" used to build custom agentic stacks for maximum architectural control of multi-agent behaviors |
| User Access | Direct via Console/ API calls for Business Analysts & App Developers | Indirect via Agent for AI Engineers & DevOps Architects |
| Configuration | Visual & Declarative: Managed via AWS Console and simple API schemas | Programmatic & Composable: Defined via SDKs/ code for deep framework integration |
| Abstraction | High-level business logic | Low-level execution |
| Customization | Templates & settings | Code-level modifications |
| Security & Governance | Probabilistic Guardrails: Filters content (PII/Hate speech) via model-based checks | Deterministic Policy: Logic-based security gates (Cedar) that strictly enforce access to tool calls via GateWay |
| Execution | Managed Orchestration: AWS handles the "thought" process and loop | Custom Runtime: You control the loop (LangGraph/CrewAI) within isolated microVMs |
| Monitoring | Operational Dashboards: Out-of-the-box CloudWatch metrics for business success | Granular Observability: OpenTelemetry-compatible traces for step-by-step technical debugging |
| Quality Assurance | Test Sets & Red Teaming: UI-based manual and batch verification to reduce hallucinations | Automated Evaluations: 13 built-in programmatic evaluators in CI/CD for multi-agent performance |
Summary
Bedrock Agents serves as the high-level conversational interface and management plane (similar to a SaaS product or AWS Console configuration). It is designed for developers who want a rapid, declarative way to set up business logic, content filtering (Guardrails), and simple task execution without managing the underlying compute.
AgentCore is the low-level, modular execution engine (the "Infrastructure-as-an-Agent") that Bedrock Agents runs on top of. While Bedrock Agents provides the "what," AgentCore provides the "how." It offers the secure, isolated microVMs (Runtime) and deterministic gates (Policy) required to host any agent framework. While it is optimized for high-performance frameworks like Strands, it is framework-agnostic, allowing users to host LangGraph, CrewAI, or custom Python/ LangChain agents with full control over the orchestration loop.
Key Comparison Points
Relationship: Think of Bedrock Agents as the pre-assembled Control Panel and user interface; it provides the 'what' through configuration. AgentCore represents the Inner Components and high-performance chassis. While the Control Panel offers a quick start for standard navigation, going 'under the hood' with AgentCore allows you to swap out the engine and wiring, hosting custom orchestration like Strands or LangGraph on enterprise-grade infrastructure.
Orchestration: Bedrock Agents provides the Conversational Interface and simplified config layer (pre-defined logic loops), while AgentCore provides the Orchestration Logic to let you bring your own orchestration (e.g., a complex multi-agent Strands graph) and secure execution environment. Bedrock Agents is where you define the mission; AgentCore is the engine room where the agent is actually launched, allowing you to host anything from a simple script to a complex multi-agent system.
Security: Bedrock Agents focuses on conversational safety (Guardrails); AgentCore focuses on operational security (Identity and deterministic Policy).
Deployment: Bedrock Agents is the Turnkey Cockpit—a managed experience for rapid deployment via the AWS Console with Standardized Workflows. AgentCore is the Modular Drivetrain—the raw primitives (Runtime, Memory, Gateway) that power the cockpit. Developers use Bedrock Agents for speed, but they choose AgentCore to build a custom-engineered vehicle with full control over every internal component. So AgentCore is best for Enterprise-Scale Customization and complex multi-agent ecosystems.
Section 3: Architecture Overview of Use Cases
Case 1: Enterprise Web Intelligent Agent
An enterprise-grade automated web intelligence gathering system powered by Amazon Bedrock AgentCore, demonstrating two different architectural approaches: LangGraph and Strands.
A notable marketing firm plans to leverage AgentCore to accelerate intelligent agent development for dynamic content creation and customer journeys, expecting to cut campaign build times by up to 30% and boost engagement metrics across key verticals. Meanwhile, a cloud company is experimenting with deploying agents using Bedrock AgentCore Runtime, built with Strands Agents, to allow their customers to scale AI capabilities while maintaining top-tier security and compliance standards for intelligent cloud content management.
These implementations and planned deployments demonstrate AgentCore's versatility across industries with stringent security, compliance, and performance requirements. The platform enables organizations to deploy AI agents that enhance customer experiences while maintaining enterprise-grade reliability and security standards.
Case 2: Healthcare appointment agent
An AI agent for the immunization-related healthcare appointments is built upon Amazon Bedrock AgentCore Gateway using the Model Context Protocol (MCP) to expose the tools. This AI agent supports enquiring about current immunization status/schedule, checking appointment slots, and booking appointments. It also provides a personalized experience by knowing the logged-in user (adult) and his/her children and uses AWS Healthlake as the FHIR R4 (Fast Healthcare Interoperability Resources) database.
Section 4: Best Practices
Design Approaches And Common Pitfalls
We’re now shifting from the creation of simple chatbots (that just talk back) to developing AI Agents (that actually do things). However, building these agents seems daunting, because we worry about memory, security, orchestration, and finally, we ask ourselves: Where do we actually host these agents?
The Body (Bedrock AgentCore):
Imagine you want to build a house, you can either build it from scratch, laying every brick and wire yourself (the self-hosted way), or you can buy a modular home with plumbing and electricity pre-installed (the AgentCore way). Bedrock AgentCore is a convenient modular home for AI agents. It is a “Serverless” platform designed specifically for agents. We don’t manage servers, patch OS updates, or worry about scaling. Why we choose AgentCore Over Self-Hosting (EC2/ECS): (1) Zero Infrastructure Management - just point AgentCore to my Docker image, and AWS handles the rest; (2) Seamless IAM Security - AgentCore uses native IAM roles without managing .env files with API secret keys, and security groups; (3) Automatic Scaling - AgentCore scales the compute resources up and down instantly without the need to configure auto-scaling policies.
The Spirit (Agentic Framework):
In the AI world, choosing an orchestration framework is a critical architectural decision. Since AgentCore Runtime is framework-agnostic, you can host virtually any Python logic. Here is a categorized list of frameworks that are proper for AgentCore, ranging from AWS-native to popular open-source options.
Strands (The "Recommended" Choice)
Strands is a new, high-performance SDK specifically optimized for AgentCore.
- Why use it with AgentCore: It is designed for multi-agent systems that need to scale. It uses a "Model-Driven" approach where agents can autonomously determine their own workflows.
- Best for: Complex, enterprise-grade multi-agent collaboration and "Supervisor" patterns.
- AgentCore Synergy: Seamlessly integrates with the AgentCore Gateway and Memory modules out of the box.
LangGraph (The "Industry Standard" for Control)
A library for building stateful, multi-agent applications with graphs.
- Why use it with AgentCore: It provides the most granular control over cycles and loops. You can host a LangGraph "compiled graph" inside an AgentCore Runtime microVM.
- Best for: Highly customized, deterministic workflows where you need to map out every possible decision node.
- AgentCore Synergy: Excellent for long-running sessions (up to 8 hours) since LangGraph handles state checkpoints well.
CrewAI (The "Team-Based" Orchestrator)
Focused on role-playing, collaborative AI agents.
- Why use it with AgentCore: It simplifies the creation of "Crews" where agents have specific roles, goals, and backstories.
- Best for: Process-driven tasks like research, content creation, or multi-step engineering analysis.
- AgentCore Synergy: AgentCore provides the secure isolation needed when multiple "Crew" members are executing code via the Code Interpreter.
OpenAI (Swarm / Assistants API Patterns)
While OpenAI is a provider, their orchestration patterns (like the experimental Swarm) can be replicated or integrated.
- Why use it with AgentCore: You can host an agent in AgentCore that calls OpenAI models (via Cross-Region/Cross-Provider logic) while using AgentCore’s Identity and Policy for enterprise security.
- Best for: Developers already heavily invested in the OpenAI ecosystem who want to migrate to AWS for better data governance.
- AgentCore Synergy: Use AgentCore Gateway to provide OpenAI agents with access to internal AWS data sources.
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