Released by AWS Labs on GitHub in February 2026, Agent Plugins for AWS is a "plugin library that grants executable skill sets to AI agents," distinguishing itself from simple code completion or natural language assistance.

In addition to being officially supported as a Claude Code plugin, this OSS can be installed and used via the official marketplace in Cursor. It supports the entire workflow end-to-end—from AI-driven design assistance, recommendations, and cost estimation to IaC generation and deployment to AWS.

/plugin marketplace add awslabs/agent-plugins

/plugin install deploy-on-aws@agent-plugins-for-aws

In that sense, existing CLI-based agents are capable of similar actions if we only consider the aspect of "execution." However, Agent Plugins differs in that it goes beyond mere task automation; it is provided as a capability layer that systematizes the design process.

This article explores "what it achieves," "why it is important," and "what value it creates."

Why It’s More Than Just a "Completion Tool"

Previously, development support using LLMs typically followed these patterns:

• Providing natural language advice in response to questions like, "How should I implement this architecture on AWS?"
• Humans designing and coding based on that advice.
• Deployment and testing performed manually by humans.

In this flow, the AI's role is centered on "design assistance," while the actual application to the environment depends on humans.

In contrast, "Agent Plugins for AWS" is a suite of plugins that allows AI agents to proactively handle everything from Design → Recommendations → Cost Estimation → IaC Generation → Deployment. This is qualitatively different from automation tools that simply suggest command completions or trigger a CLI via natural language.

Differences from CLI Automation: Why It's Not Just "Terminal Operations"

Many might think that with current tools like Claude Code or various AI CLIs, one can already:

• Generate IaC
• Execute the AWS CLI
• Proceed to deployment without manual intervention ...provided that agent mode is enabled with appropriate permissions. Many are likely already practicing this.

If we look solely at "whether it can be executed," traditional CLI-based agent environments can perform similar tasks.

So, what is the differentiator for "Agent Plugins for AWS"?
The difference lies not in "executability," but in "at which layer the capability is integrated."

1. Ad-hoc Inference vs. Structured Capability

In CLI-based automation, the agent reasons based on a prompt each time to generate and execute necessary commands. Design decisions and service selections tend to rely on the model's internal knowledge and the immediate context.

In contrast, Agent Plugins for AWS is characterized by explicitly defining the AWS design workflow itself—Analyze, Recommend, Estimate, Generate, Deploy—as extended capabilities of the agent. This is not just a sequence of commands to execute a task; it is a capability that stages the entire design process.

In other words, it represents a shift from a model that "executes operations thought up on the spot" to one that "internalizes the AWS design process as a structured capability."

While mechanisms like Claude's Skills or Kiro Powers can indeed grant agents additional specialized knowledge or scripts, they act as modules to enhance behavior or knowledge for specific domains. They do not systematize the entire design workflow.

"Agent Plugins for AWS" takes a fundamentally different role by officially packaging the sequence from design to execution and providing it as a capability integrated with live data (pricing, documentation, etc.) via MCP.

2. Inference-Centric vs. Live Data-Connected

While recommendations and generation are possible via CLI execution alone, those judgments tend to rely on the model's internal training data.

Agent Plugins connects via MCP servers to:

• awsknowledge (Official documentation)
• awspricing (Real-time pricing)
• aws-iac-mcp (IaC best practices)

This structure ensures that design recommendations and cost estimates are tied to the latest official information and real-world data. The difference is not "whether it can be done," but "whether the information sources backing the judgment are systematically integrated."

3. Operational Automation vs. Domain Capability Expansion

CLI automation is primarily about making "operations" more efficient.
Agent Plugins grants the agent specific AWS domain knowledge, such as:

• Service selection logic
• Cost evaluation flows
• IaC output patterns

This can be viewed as an attempt to "expand the design capability within the AWS domain" rather than just automating command execution.

Positioning in the Capability Stack

Structurally, Agent Plugins is positioned in the following layer:

While CLI automation optimizes the "CLI / API Execution Layer," Agent Plugins adds an "AWS Domain Capability" intermediate layer above it. This design elevates the agent's capability stack rather than just adding another tool.

Organizational Perspective

CLI automation improves individual efficiency. Agent Plugins standardizes the design workflow. This difference may seem small but becomes significant at the organizational level:

• Reproducibility of designs
• Consistency in cost evaluation
• Uniformity of IaC output
• Reviewable rationales for recommendations

Agent Plugins contributes to the "standardization of processes." Therefore, its differentiator is not "replacing the CLI," but "stacking AWS-specific capabilities on top of the CLI." If CLI automation is the "execution foundation," Agent Plugins is the "capability extension layer" built upon it.

Basic Structure and Workflow of Agent Plugins

Agent Plugins for AWS is a collection of plugin modules that grant AWS-related functionalities to AI agents. According to the README, the goal is to provide skills that allow AI coding agents to assist with everything from AWS design and deployment to operations.

The 5-Step Workflow

1. Analyze: Analyzes source code and project structure to identify frameworks, dependencies, and data stores.
2. Recommend: Suggests appropriate AWS service configurations and provides the reasoning.
3. Estimate: References real-time pricing via the AWS Pricing MCP server to estimate the cost of the recommended setup.
4. Generate: Converts the design into IaC (CDK or CloudFormation).
5. Deploy: Reflects and executes the generated IaC in the AWS environment after user approval.

This establishes a circuit for the AI agent to proactively drive the workflow from design to implementation and deployment.

Real-Data Integration Powered by MCP Servers

The Model Context Protocol (MCP) server is the crucial underlying mechanism supporting the utility of Agent Plugins. MCP is a standardized protocol for connecting AI models to external data sources and tools. AWS-side MCP servers provide official documentation, pricing, and best practices.

Welcome to Open Source MCP Servers for AWS

Get started with open source MCP Servers for AWS and learn core features.

awslabs.github.io

Key MCP Servers

This allows the agent to refer to the latest live data rather than relying solely on the model's internal knowledge.

Real-World Value

1) Cloud Migration & Architecture Design Support

In traditional cloud migration, humans had to handle multiple phases: analyzing current setups, selecting services, decision-making based on costs, designing IaC, and deploying.
By simply instructing an agent using Agent Plugins in natural language, much of this is automated.

"I want to optimize this project for an AWS serverless architecture and deploy it."
This single instruction can lead to recommendations, cost comparisons, IaC, and execution, significantly reducing manual effort and ensuring design accuracy.

2) Formalizing Team Knowledge

The tacit knowledge of veteran designers often leads to siloing. Because Agent Plugins outputs the rationale for recommendations, costs, and IaC, knowledge sharing and reviews become much easier. This results in:

• Transparency in the design decision process.
• Formalization of best practices.
• Reduced learning costs for new members.

3) Integration with CI/CD and Quality Evaluation

Generated IaC and configurations can be integrated directly into CI/CD pipelines.

• Automatically validating IaC in pull requests.
• Attaching cost comparison reports to the review stage.
• Linking to automated deployment approval workflows.

Considerations and Risks

• Model Errors and Recency: As stated in the official README, outputs may contain errors, and all results require human review.
• Security and Permissions: Careful design of AWS CLI and IAM settings is essential. Risks increase with excessive permissions; establishing proper approval flows for automated deployments is vital.

Future Outlook

Agent Plugins for AWS is a foundation for evolving AI agents from "explanatory assistants" into "orchestration engines for execution." The underlying MCP servers and ecosystem (Claude, Cursor, etc.) are continuously developing, potentially leading to further automation of cloud operations.

Furthermore, AWS has announced the preview of the AWS MCP Server, a remote/fully-managed Model Context Protocol server. This suggests a direction where governance—such as authentication/authorization via IAM and log collection via CloudTrail—will be natively supported.

https://aws.amazon.com/about-aws/whats-new/2025/11/aws-mcp-server/

Conclusion

Agent Plugins for AWS is a significant evolution that moves the role of AI from "assistance" to "execution." By providing a foundation based on real-time data, consistent workflows, and reasoned support, it enables both productivity and quality in cloud design, migration, and operations.