Automating Experiments with Kiro, AWS DevOps Agent & LaunchDarkly

Continuous improvement through experimentation is a key driver of better outcomes in software development. However, the overhead of planning, measuring, and iterating on experiments can slow teams down significantly. Recently, AWS introduced a reference solution integrating Kiro, AWS DevOps Agent, and LaunchDarkly to automate the experimentation lifecycle, aiming to address these challenges.

What Changed?

The AWS solution simplifies the experimentation process by automating the various stages involved, from planning to execution and iteration. The integration involves Kiro for code generation, AWS DevOps Agent for orchestration, and LaunchDarkly for feature flag management and experimentation. This setup aims to close the gap between defining a goal and achieving it, allowing teams to state an improvement objective and let the system handle the rest. Specifically, the system automates the creation of feature flags, the implementation of code changes, and the opening of pull requests, all while managing the deployment process and rollouts behind feature flags.

Why It Matters

For teams maintaining production codebases, the introduction of this automated cycle could mean significant time savings and reduced costs. By automating the planning, measurement, and iteration phases, teams can focus on higher-level strategic work rather than getting bogged down in manual processes.

Key barriers that this solution addresses include:

• Planning Cost: Simplifies the conversion of proposed changes into testable experiments by automating feature flag strategy and implementation. This involves creating a feature flag in LaunchDarkly, invoking Kiro CLI to implement the change behind the flag, and opening a pull request automatically.
• Disconnected Measurement: By linking metrics directly to the experimentation process, teams can automate remediation without manual intervention. LaunchDarkly monitors operational guardrails and automatically reverts flag states if thresholds are breached, eliminating the need for manual rollbacks.
• Stalled Iteration: By maintaining a record of changes and outcomes, the system informs the next hypothesis, ensuring continuous iteration. This is achieved by querying LaunchDarkly’s Change History API to associate specific flag modifications with outcomes, thereby guiding future experiments.

Implementing the Solution

To get started with this solution, follow these steps:

1. Enable AWS DevOps Agent: Set up your agent and create an Agent Space in available AWS regions. This involves navigating to the AWS DevOps Agent console, creating an Agent Space, and configuring it to connect with other tools.
2. Create a LaunchDarkly Account: Sign up through AWS Marketplace or directly via the LaunchDarkly website. This is a straightforward process of account creation and will be necessary for managing feature flags.
3. Integrate LaunchDarkly MCP Server: Register and configure the MCP server in your AWS account to manage feature flags and experiments. This includes setting up OAuth authentication, registering the server at the account level, and ensuring it is available to all Agent Spaces.
4. Connect MCP Server to Agent Space: Add the server to your Agent Space and validate the connection with test queries. This ensures that the setup is working correctly and that the agent can interact with LaunchDarkly to retrieve feature flag data.

This architecture allows AWS DevOps Agent to orchestrate the entire experimentation cycle autonomously, leveraging Kiro for code implementation and LaunchDarkly for feature management and safe rollouts.

Example Code Integration

import { useFeature } from 'launchdarkly-react-client-sdk';

function MyComponent() {
  const { isFeatureEnabled } = useFeature('exp-add-to-cart');
  return (
    

      {isFeatureEnabled ?  : }
    
  );
}

This code snippet demonstrates how to use LaunchDarkly to toggle between new and old features based on a feature flag, streamlining the experimentation process.

What I'd Do on Monday

To integrate this solution in a production environment, I would start by setting up the AWS DevOps Agent and LaunchDarkly account as detailed in the steps above. Next, I would focus on defining clear improvement goals that can be measured through business KPIs. This setup allows for continuous monitoring and adjustment of experiments, which is crucial for achieving desired outcomes.

Additionally, I would configure the AWS DevOps Agent to run as a Custom Agent, encoding specific workflows and decision logic. This involves defining the agent's schedule, safety constraints, and connected tools to ensure it operates autonomously without needing manual input unless an error occurs.

It's also important to scope the server's operations carefully, ensuring it only touches intended repositories and branches to maintain security and integrity. This involves configuring tool access in the MCP Servers section to allow only the necessary tools, especially in production environments.

What It Does Not Solve

While the solution automates many aspects of the experimentation cycle, it does not replace the need for strategic decision-making. Teams must still define the goals and interpret the results. Moreover, setting up the initial integration and ensuring all components communicate effectively requires a solid understanding of both AWS and LaunchDarkly systems.

In particular, the solution does not cover error handling beyond automated rollbacks. Teams must be prepared to intervene manually in cases where automated systems fail to resolve issues. Furthermore, while the system can suggest hypotheses based on past outcomes, it cannot generate novel ideas or strategies, which remain the purview of human creativity and insight.

Overall, the solution offers a promising way to reduce the overhead of experiments, but it requires careful setup and ongoing strategic oversight. It is particularly beneficial for teams with frequent experimentation needs, who will see the most value from the automation of repetitive tasks and the structured approach to feature rollouts and iterations.