Amazon Bedrock AgentCore: The Complete Guide to AWS’s Enterprise Agent Platform for Agentic AI Architects (2026)

A Fortune 500 insurance company spent fourteen months building an agentic claims-processing system. They stitched together LangGraph for orchestration, a custom DynamoDB schema for session state, hand-rolled Lambda containers for tool execution, a bespoke OAuth flow for Salesforce integration, and three months of security review to satisfy their compliance team. Total engineering cost: north of $2 million. Then AWS launched Bedrock AgentCore — and a comparable architecture became deployable in days, not quarters.

That gap between “works on my laptop” and “runs in production at enterprise scale” has been the defining bottleneck in agentic AI adoption. According to a 2026 enterprise AI survey cited by Futurum Group, the most common future-state orchestration strategy among enterprises is a hybrid mix of DIY and managed platforms (41.8%), followed by fully managed AI development platforms at approximately 28%. The message is clear: enterprises want managed infrastructure for agents, and they want it yesterday.

Amazon Bedrock AgentCore is AWS’s answer — a comprehensive set of thirteen enterprise-grade services designed to let developers securely deploy and operate AI agents at scale using any framework and any model. Announced at general availability in October 2025 and expanded steadily through 2026, AgentCore now includes managed runtimes with 8-hour session windows, MCP-native tool gateways, deterministic policy enforcement, multi-framework support (Strands, LangGraph, CrewAI, LlamaIndex, Google ADK, and OpenAI Agents SDK), and — as of September 29, 2026 — Bedrock Managed Agents powered by OpenAI, bringing GPT-5.6 frontier models into the AWS-native agent stack.

This guide walks Agentic AI Architects and RPA professionals through the entire AgentCore platform: its thirteen-service architecture, the Managed Agents announcement, practical deployment patterns, competitive positioning against Azure AI Foundry and Google Vertex AI, and what this means for teams currently running traditional RPA workflows on UiPath, Automation Anywhere, or Blue Prism.

What Is Amazon Bedrock AgentCore?

At its core, AgentCore is a managed infrastructure layer that sits between your chosen agent framework (the orchestration logic) and the underlying AWS cloud services (compute, storage, identity, networking). It handles the undifferentiated heavy lifting that every production agent deployment needs: session isolation, tool authentication, memory persistence, policy enforcement, and observability — so your engineering team can focus on the agent’s actual business logic rather than the plumbing.

Think of it as the difference between renting an apartment (AgentCore manages the building, plumbing, electrical, and security — you bring the furniture) versus building a house from scratch (you manage everything, including the foundation). Both get you a place to live, but only one lets you move in this quarter.

AgentCore’s architecture rests on three layers:

The critical architectural decision: AgentCore is model-agnostic and framework-agnostic. You can run Claude, Nova, GPT-5.6, Llama, Mistral, or any Bedrock-supported model. You can orchestrate with Strands, LangGraph, CrewAI, or your own custom framework. The platform layer stays the same regardless of what you plug in above and below it.

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The Thirteen Services: What Each One Does and Why It Matters

AgentCore isn’t a single product — it’s thirteen independently consumable services. Here’s what each one does, organized by function.

Build and Host

1. AgentCore Harness — The managed agent loop. You provide a model, a prompt, and a set of tools; the Harness handles the reasoning loop, tool dispatch, memory injection, and provider switching automatically. The deployment surface is two API calls: CreateHarness and InvokeHarness. That’s it. What previously required 6–8 weeks of LangGraph loops, DynamoDB schemas, and Lambda containers now collapses to hours. The Harness also supports mid-session model provider switching — you can start a conversation on Claude Sonnet and, if the task requires heavier reasoning, escalate to Claude Opus without losing session context.

2. AgentCore Runtime — Serverless hosting with Firecracker-based microVM isolation. Each user session runs in its own isolated environment with dedicated CPU, memory, and filesystem. Key specs: 8-hour session windows (compared to Lambda’s 15-minute timeout or Cloud Run’s 60-minute limit), persistent filesystems within sessions, bidirectional WebSocket streaming, and 100MB request payloads. The long session windows are a genuine differentiator — complex enterprise workflows that span multiple tool calls, human approvals, and intermediate computations no longer need to be artificially broken into sub-15-minute chunks.

Connect

3. AgentCore Gateway — Transforms existing APIs, Lambda functions, and OpenAPI specifications into MCP-compatible tools. This is where AgentCore becomes immediately practical for RPA teams: your existing API integrations — whether they’re REST endpoints, Lambda functions, or Smithy models — become agent-ready tools without rewriting them. The Gateway also proxies to remote MCP servers, meaning the growing ecosystem of MCP servers and A2A protocol implementations plugs directly into your AgentCore agents.

4. AgentCore Identity — Manages both agent and user principals with OAuth 2.0 support and secure token vaults. Identity integrates with Cognito, Okta, Microsoft Entra ID, and Auth0 — meaning your existing enterprise identity provider works without a parallel auth system. Each agent gets its own IAM role with scoped permissions, and the Identity service injects credentials from AWS Secrets Manager at runtime rather than embedding them in agent configurations. For A2A (Agent-to-Agent) communication, Identity provides JWT-based agent identity with scoped tool permissions enforced per invocation.

5. AgentCore Memory — Manages both short-term session turns and long-term factual memory using built-in extraction strategies. The memory service supports managed or self-managed extraction policies for user preferences, summarization, and semantic memory extraction. This solves a persistent pain point in production agent deployments: maintaining context across sessions without building a custom memory store. Memory state is stored independently from model choice, so switching providers doesn’t lose conversation history.

Act

6. AgentCore Code Interpreter — Isolated environments for executing agent-generated Python, JavaScript, and TypeScript code safely. When your agent needs to perform calculations, generate charts, or manipulate data, Code Interpreter provides a sandboxed execution environment without exposing your production infrastructure.

7. AgentCore Browser — Managed Chromium instances for web automation at scale. Supports Playwright and BrowserUse for form-filling, web scraping, and browser-based workflows. For RPA teams accustomed to browser automation through UiPath or Automation Anywhere, this is a direct capability overlap — except the browser is managed, scaled, and integrated with the rest of the agent infrastructure rather than running on dedicated bot machines.

8. AgentCore Payments — x402 micropayments protocol support with Coinbase CDP and Stripe wallets. This is still in preview, but it signals AWS’s expectation that agents will transact autonomously — a capability that traditional RPA platforms are only beginning to address.

Observe and Govern

9. AgentCore Observability — OpenTelemetry-native spans flowing to CloudWatch by default, with OTLP protocol support for Datadog, Dynatrace, LangSmith, Langfuse, Arize, Honeycomb, and Grafana. Every harness invocation automatically emits structured spans with latency breakdowns (model vs. tool calls), error rates, and cost attribution per tool — zero additional instrumentation required. The observability stack logs to /aws/bedrock-agentcore/runtimes/<agent_id>-<endpoint_name> in CloudWatch.

10. AgentCore Evaluations — Structured testing against sessions and traces. Define eval criteria, run them against recorded agent interactions, and track quality metrics over time. This is the missing piece most custom agent deployments skip — and then regret when agent behavior drifts in production.

11. AgentCore Optimization — A/B testing of prompt and tool changes against live traffic (preview as of May 2026). Split traffic between agent configurations, collect statistical significance data, and surface recommendations automatically. The ability to A/B test prompt variations in production, with full observability, is something that previously required building an entire experimentation framework from scratch.

12. AgentCore Policy — Deterministic, out-of-band controls that agents cannot reason around. This is, in the words of one analyst, “the underappreciated announcement.” Policies can be expressed in natural language or Cedar (the same policy language powering IAM and Verified Permissions) and enforce constraints before tool calls execute. Planned integrations include Checkpoint, Netskope, CrowdStrike, SentinelOne, and Zscaler for enterprise security stack integration. For regulated industries — financial services, healthcare, government — this is the governance layer that makes the difference between “interesting demo” and “approved for production.”

13. AgentCore Registry — A catalog for agents, MCP servers, tools, and skills with hybrid search. The Registry enables agent discovery and interoperability across teams, preventing the shadow-agent sprawl that becomes a governance nightmare at scale. It supports the A2A protocol for cross-boundary agent communication.

Bedrock Managed Agents: OpenAI Meets AWS Infrastructure

On September 29, 2026, AWS and OpenAI jointly announced Bedrock Managed Agents — a managed runtime that combines OpenAI’s Agents API framework with AWS-native infrastructure. The announcement landed with a pointed tagline from The Next Web: “Data never leaves AWS.”

Here’s what Managed Agents actually delivers:

The key architectural distinction: Managed Agents runs on the OpenAI harness, not the AWS-native Bedrock Agents harness. This is a strategic choice, not an accident. The OpenAI harness is optimized for OpenAI’s frontier models (GPT-5.6 Sol, Terra, and Luna are generally available on Bedrock), while the existing Bedrock Agents framework is optimized for Claude, Nova, and other Bedrock-native models.

Two Agent Products, One Platform: The Decision Matrix

This creates a critical decision point for enterprise teams:

The practical implication, as noted by the analysis at clawaws.com: “Switching harnesses later means rewriting your agent definitions, tool schemas, and orchestration patterns.” This isn’t a trivial migration — it’s a re-architecture. Choose deliberately.

When to choose Bedrock Agents (AWS-native): You want multi-model flexibility (Claude + Nova + open-source), you’re already invested in the Bedrock Agents API, or you need features at GA maturity for production compliance.

When to choose Managed Agents (OpenAI): You need OpenAI’s frontier reasoning capabilities, you’re migrating from direct OpenAI API access and want AWS governance, or your workload specifically benefits from GPT-5.6’s task-steering capabilities.

The Strands SDK: AWS’s Open-Source Agent Framework

Alongside AgentCore’s managed services, AWS shipped Strands Agents SDK — an MIT-licensed, open-source agent framework that has crossed 50 million downloads since its May 2026 open-sourcing. Strands is not required to use AgentCore (you can bring LangGraph, CrewAI, or anything else), but it’s optimized for the platform.

Strands 1.0 introduces four orchestration primitives that map to real enterprise patterns:

Agents-as-Tools (Hierarchical) — Sub-agents invoked as tools by an orchestrator. The practical pattern: run Claude Opus 4 as the orchestrator for complex reasoning, with Sonnet 4.6 specialists handling execution tasks — optimizing cost without sacrificing reasoning quality on the decisions that matter.

GraphAgent (Deterministic DAG) — Compliance-driven workflows where the execution path must be deterministic and auditable. Think regulatory reporting: the agent follows a defined graph of steps, and every branch point is logged.

SwarmAgent (Peer-to-Peer) — Multiple agents share context and can address any peer. Useful for collaborative analysis where different agents bring different domain expertise.

PipelineAgent (Sequential Chain) — One agent’s output feeds the next. The classic ETL pattern reimagined for agents: extract → validate → transform → load, where each stage is an agent with its own tools and guardrails.

Strands also ships with Strands Shell (sandbox file and code access launching in under a millisecond) and Strands Evals (adversarial testing at build time). The framework is model-agnostic — it supports Bedrock, OpenAI, Claude direct, Gemini, and LiteLLM endpoints — and runs anywhere: locally, Lambda, Fargate, EKS, or AgentCore Runtime.

What This Means for RPA Teams: The Migration Path

If you’re running traditional RPA workflows on UiPath, Automation Anywhere, or Blue Prism, AgentCore represents both a competitive threat and a complementary platform. Here’s how to think about it.

Where AgentCore Overlaps with Traditional RPA

Browser automation: AgentCore Browser provides managed Chromium instances with Playwright/BrowserUse support. This directly overlaps with the screen-scraping and browser-automation capabilities that are traditional RPA’s bread and butter — except the browser is managed, auto-scaled, and doesn’t require dedicated bot machines or VDI infrastructure.

API integration: AgentCore Gateway converts existing REST APIs and Lambda functions into MCP-compatible tools. Traditional RPA’s API activities (HTTP Request, REST connector) serve the same purpose, but Gateway adds automatic authentication, credential injection from Secrets Manager, and CloudWatch logging without configuration.

Scheduled and event-driven execution: AgentCore Runtime supports both scheduled invocations and event-driven triggers — the same execution patterns RPA orchestrators (UiPath Orchestrator, AA Control Room) provide.

Where AgentCore Goes Beyond Traditional RPA

Reasoning and decision-making: Traditional RPA follows deterministic rules. AgentCore agents reason about ambiguous inputs, handle exceptions through LLM-powered judgment, and adapt to variations without explicit if/else branches for every scenario. A claims-processing agent on AgentCore can read a doctor’s handwritten note, understand the context, cross-reference policy terms, and route appropriately — something that would require dozens of custom activities and ML models in a traditional RPA workflow.

Natural language interfaces: AgentCore agents can accept instructions in natural language, translate them into tool calls, and report back in natural language. Traditional RPA requires structured inputs (a spreadsheet, a queue item, a form submission).

Multi-agent collaboration: The A2A protocol and Strands’ orchestration primitives enable multiple agents to collaborate on complex workflows. Traditional RPA’s multi-bot patterns are limited to queue-based handoffs between sequential automations.

The Pragmatic Approach: Hybrid Deployment

For most enterprise RPA teams, the realistic path is not “rip and replace” but “extend and complement.” Here’s the pattern that’s emerging:

Keep RPA for: High-volume, deterministic, screen-scraping-heavy workflows where the rules are clear, the UI is stable, and the ROI is proven. Legacy mainframe integrations. Citrix/VDI automation. Anything where the primary value is speed and reliability on a known, unchanging process.

Add AgentCore for: Exception handling (the 20% of cases that consume 80% of RPA development effort). Document understanding with ambiguity. Multi-step workflows that require reasoning. New automation candidates that were previously “too complex for RPA” — the processes where building rule-based automation would take longer than the manual work it replaces.

Connect them via: MCP servers that expose your RPA orchestrator’s queue and trigger APIs as agent tools. AgentCore Gateway makes this straightforward — your UiPath Orchestrator API becomes an MCP-compatible tool that AgentCore agents can invoke, and vice versa. The A2A protocol enables cross-boundary communication between your AgentCore agents and agents running on other platforms (Microsoft Agent Framework, Google ADK, etc.).

AgentCore vs Azure AI Foundry vs Google Vertex AI: The Enterprise Agent Platform Comparison

AWS isn’t building AgentCore in a vacuum. Microsoft has Azure AI Foundry (formerly Azure AI Studio), and Google has Vertex AI Agent Builder. Here’s how they compare on the dimensions that matter for production deployment.

Key Differentiators by Platform

AWS AgentCore’s advantage: Framework flexibility and breadth. Supporting six frameworks natively — plus custom — means teams aren’t locked into one orchestration pattern. The 8-hour session window is genuinely unique for complex, long-running agent workflows. The Cedar policy engine provides deterministic governance that models cannot reason around, which is a hard requirement for regulated industries.

Azure AI Foundry’s advantage: Microsoft 365 integration depth. If your organization runs on Entra ID, Teams, SharePoint, and the Microsoft 365 suite, Foundry’s tight integration saves weeks of identity and data-source plumbing. The EU Data Boundary commitment is also a differentiator for GDPR-sensitive deployments.

Google Vertex AI’s advantage: The A2A protocol originated at Google, so Vertex has the deepest native implementation. Gemini’s multimodal capabilities (native image, video, and audio understanding) are best-in-class for workflows that process unstructured media. Google also achieved FedRAMP High first for generative AI — a meaningful differentiator for U.S. government workloads.

Real-World Deployments: Who’s Running AgentCore in Production

AWS has highlighted several production deployments that illustrate AgentCore’s enterprise applicability:

Druva — Deployed a support agent on AgentCore that resolves 68% of support tickets autonomously. The agent reads incoming tickets, queries internal knowledge bases, generates solutions, and escalates only the cases that require human judgment.

Thomson Reuters — Platform engineering automation covering 70% of routine operational work. Their agents handle infrastructure provisioning, monitoring alert triage, and routine maintenance tasks that previously required on-call engineers.

Cox Automotive — 17 production agents deployed within one year, covering vehicle valuation, dealer communications, and inventory management workflows.

Southwest Airlines — Deployed Kiro (AWS’s specification-first development agent) to 2,700+ developers, achieving what AWS describes as a “three-year acceleration of modernization timelines.” One cited outcome: a single engineer built a 170-indicator trading platform in eight weeks using Kiro on AgentCore — a project that traditionally required twelve engineers across 12–24 months.

Salesforce — Named as an early customer for Bedrock Managed Agents (OpenAI), planning to combine OpenAI models with its own data and governance controls. Salesforce’s Joe Inzerillo stated: “Security stops being the bottleneck between our customers and what comes next.”

Getting Started: From Zero to Production Agent

For teams ready to evaluate AgentCore, here’s the practical onboarding path:

Step 1: Choose Your Framework

If you’re starting fresh, Strands is the path of least resistance — it’s AWS-native, MIT-licensed, and optimized for AgentCore. If you’re already invested in LangGraph, CrewAI, or another framework, bring it — AgentCore supports them without modification.

Step 2: Deploy with the Starter Toolkit

The AgentCore starter toolkit enables single-command deployment:

pip install bedrock-agentcore bedrock-agentcore-starter-toolkit
agentcore configure --entrypoint your_agent.py --region us-west-2
agentcore launch

This creates an IAM execution role, deploys your agent to a Firecracker microVM, and configures CloudWatch observability — all in one command.

Step 3: Connect Your Tools

Use AgentCore Gateway to expose your existing APIs as MCP-compatible tools. If you have OpenAPI specs for your internal services, Gateway can convert them directly. For ServiceNow, Salesforce, Slack, and GitHub, AgentCore Identity handles OAuth flows automatically.

Step 4: Define Policies

Use the Policy service to define guardrails before going to production. Cedar-based policies enforce constraints that the agent cannot override through reasoning — for example, “never execute financial transactions above $10,000 without human approval” or “never access customer PII outside of the claims-processing workflow.”

Step 5: Test with Evaluations

Run your agent against recorded sessions using AgentCore Evaluations. Define success criteria, track quality metrics, and identify failure modes before live traffic hits.

Step 6: Monitor and Optimize

Once in production, use Observability for real-time monitoring and Optimization (preview) for A/B testing prompt and tool changes. The observability integration with external vendors (Datadog, Langfuse, etc.) means you don’t need a separate monitoring stack for agents.

AWS Context: The Knowledge Layer Most Teams Miss

One service that deserves separate attention is AWS Context — a managed knowledge graph service that indexes both structured data (Redshift, Athena, databases) and unstructured sources (S3, SharePoint, Confluence) into a unified, queryable layer.

Traditional RAG (Retrieval-Augmented Generation) pipelines require teams to build and maintain their own ingestion, chunking, embedding, and retrieval infrastructure. AWS Context handles all of this as a managed service, with an agentic retriever that decomposes complex queries into sub-queries before fetching from multiple sources.

For RPA teams, this solves a recurring problem: your automation needs access to knowledge that lives across SharePoint sites, Confluence wikis, internal databases, and S3 document repositories. Instead of building custom connectors for each source, AWS Context provides a single, searchable knowledge layer that your AgentCore agents can query with natural language.

Pricing and Cost Considerations

AgentCore’s pricing spans thirteen independently consumable services rather than a flat infrastructure cost. AWS recommends “a few weeks of cost monitoring before production” to forecast spending accurately, which is practical advice given the number of moving parts.

Key cost optimization strategies:

Model tiering: Use expensive models (Claude Opus, GPT-5.6) only for complex reasoning tasks. Route execution tasks to cheaper models (Claude Sonnet, Nova). Strands’ Agents-as-Tools pattern makes this straightforward — the orchestrator runs on the expensive model, while specialist agents run on cheaper ones.

Session management: Long-running sessions consume runtime resources. Design agents to complete tasks efficiently rather than holding sessions open for potential follow-up.

Managed Agents (OpenAI) commercial logic: For organizations on AWS Enterprise Discount Program (EDP) or Enterprise License Agreement (ELA), every dollar of OpenAI inference routed through Bedrock counts against committed spend. This converts AI tool costs into committed-spend contributions, reducing the risk of under-utilization penalties.

Bedrock Managed Agents preview pricing: During the preview phase, there is no additional charge for Managed Agents beyond the underlying AWS resources consumed. This will change at GA.

Limitations and Trade-Offs

No platform guide is complete without an honest assessment of limitations:

AWS-only: AgentCore runs exclusively on AWS. If your organization requires multi-cloud agent deployments, you’ll need a separate solution for non-AWS environments. The Strands SDK itself is cloud-agnostic (it runs anywhere), but the AgentCore managed services are AWS-only.

Preview services: Optimization, Payments, and some Gateway features are still in preview. Documentation on preview services is thinner than GA services. Build production systems on GA services and experiment with preview features in staging.

Complexity: Thirteen services is a lot of surface area. The conceptual startup curve is steeper than single-framework stacks. Teams accustomed to “pip install langchain and go” may find the platform-level thinking unfamiliar.

Two-harness decision: The Bedrock Agents (AWS-native) vs. Managed Agents (OpenAI) choice is strategic and difficult to reverse. Teams need to commit to one harness for a given agent, and switching later means re-architecture.

Tool description dependency: As AWS’s own documentation acknowledges, “agents are only as capable as the tools you expose.” AgentCore provides the infrastructure, but the quality of tool descriptions, prompt engineering, and eval design remains engineering work that the platform cannot automate away.

Frequently Asked Questions

Can I run AgentCore agents alongside my existing UiPath or Automation Anywhere bots?

Yes. The recommended integration pattern is to expose your RPA orchestrator’s API (UiPath Orchestrator API, AA Control Room API) as an MCP-compatible tool via AgentCore Gateway. This lets AgentCore agents trigger RPA bots for specific tasks (screen scraping, legacy system interaction) while handling the higher-level orchestration and decision-making. The reverse also works — your RPA bots can call AgentCore agents via API when they encounter exceptions that require reasoning.

Does Bedrock AgentCore support the Model Context Protocol (MCP)?

Yes. AgentCore Gateway provides native MCP support — it can consume MCP servers (connecting to external tools and services) and produce MCP-compatible tool interfaces (exposing your APIs, Lambda functions, and OpenAPI specs as MCP tools). The Gateway also proxies to remote MCP servers, meaning the growing ecosystem of community MCP servers plugs directly into AgentCore.

How does AgentCore pricing compare to building a custom agent stack?

AWS hasn’t published comprehensive pricing guides, but the general pattern is pay-per-use across thirteen services versus the fixed costs of building and maintaining custom infrastructure (DynamoDB for state, Lambda/ECS for compute, custom observability, bespoke auth). For most enterprise teams, the managed services will be cheaper than the engineering headcount required to build and maintain equivalent infrastructure — but “a few weeks of cost monitoring before production” (AWS’s own recommendation) is essential to forecast accurately for your specific usage patterns.

What’s the difference between Bedrock Agents and Bedrock Managed Agents?

Bedrock Agents uses the AWS-native harness optimized for Claude, Nova, and other Bedrock models. Bedrock Managed Agents uses the OpenAI harness optimized for GPT-5.6 frontier models. Both run on AWS infrastructure with IAM, CloudTrail, and VPC support. The key difference is the orchestration engine and which models each is optimized for. Switching between them requires rewriting agent definitions and tool schemas — it’s not a configuration change.

Is AgentCore suitable for regulated industries (healthcare, financial services, government)?

Yes, with specific considerations. AgentCore holds SOC 2 Type II, HIPAA BAA, PCI DSS, IRAP, and FedRAMP certifications. The Cedar-based Policy engine provides deterministic controls that agents cannot reason around — a hard requirement for regulated environments. AgentCore is also available in AWS GovCloud (US-West) as of May 2026. However, regulated deployments should use GA services (not preview features) and implement comprehensive evaluation suites before production deployment.

Key Takeaways

• AgentCore is an infrastructure platform, not a framework. It manages the production plumbing (runtime isolation, identity, memory, policy, observability) so you focus on business logic with your chosen framework.
• Thirteen independently consumable services cover build, connect, act, observe, and govern — a full stack for enterprise agent deployment.
• Bedrock Managed Agents (OpenAI) brings GPT-5.6 into the AWS ecosystem with data sovereignty guarantees, but creates a strategic harness decision that’s difficult to reverse.
• Framework-agnostic: Strands, LangGraph, CrewAI, LlamaIndex, Google ADK, and OpenAI Agents SDK all run on AgentCore without modification.
• The Cedar Policy engine provides deterministic governance that agents cannot reason around — the key enabler for regulated industry adoption.
• RPA teams should extend, not replace: Use AgentCore for exception handling, document understanding, and multi-step reasoning workflows while keeping deterministic RPA for high-volume, rule-based processes.
• The 8-hour session window is a genuine differentiator over Lambda (15 min) and Cloud Run (60 min) for complex enterprise workflows.
• 50 million Strands downloads since May 2026 indicate strong developer adoption of the open-source SDK.
• Cost optimization through model tiering — route complex reasoning to Opus/GPT-5.6 and execution tasks to Sonnet/Nova — is the primary lever for production cost management.

References

1. AWS. “Introducing Amazon Bedrock AgentCore: Securely Deploy and Operate AI Agents at Any Scale.” AWS News Blog.
2. AWS. “Amazon Bedrock Managed Agents, powered by OpenAI, is now available in preview.” September 29, 2026. AWS What’s New.
3. AWS. “Top Announcements of the What’s Next with AWS, 2026.” AWS News Blog.
4. The Next Web. “‘Data never leaves AWS’: OpenAI agents move into Amazon’s cloud.” The Next Web.
5. Futurum Group. “AWS Pushes the Agent Stack: Quick, Connect Verticals, OpenAI on Amazon Bedrock.” Futurum Group.
6. clawaws.com. “OpenAI Models on Amazon Bedrock: Architecture, Managed Agents, and AWS Implications.” clawaws.com.
7. Efficiently Connected. “AWS Agentic AI Platform: Bedrock Agent Core, Q, and More.” Efficiently Connected.
8. Reactify Solutions. “Amazon Bedrock AgentCore in Production 2026.” Reactify Solutions.
9. Tutorials Dojo. “Building Production AI Agents on AWS: AgentCore Harness, Strands 1.0.” Tutorials Dojo.
10. GMI Cloud. “Enterprise Agent Hosting Compared: AWS Bedrock vs Google Vertex AI vs Azure AI Foundry.” GMI Cloud.

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