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Automating the Experimentation Lifecycle with Kiro, AWS DevOps Agent, and LaunchDarkly
AWS DevOps Agent, Kiro CLI, and LaunchDarkly enable a closed-loop autonomous system that plans, implements, deploys, and measures feature-flagged experiments.
What: Greg Eppel and Jonathan Nolen describe a system where an autonomous agent reviews goals, generates hypotheses, writes code, merges pull requests, and runs A/B experiments using LaunchDarkly. It includes 'Guarded Releases' which automatically revert flag state if operational metrics (like error rates) regress.
Why it matters: This signals a major shift in DevOps from 'human-in-the-loop' to 'human-on-the-loop' management, where the system itself is responsible for the entire lifecycle of an experiment, including safety rollbacks.
Takeaway: Register the LaunchDarkly MCP server in your AWS DevOps Agent Console and deploy the Experiment MCP Server to begin automating your experimentation lifecycle.
Deep dive
- The loop consists of three phases: Plan (hypothesis generation), Prove (A/B testing at 10% traffic), and Iterate (using past outcomes to improve future experiments).
- Uses Kiro CLI for headless code generation and feature flag injection.
- Leverages LaunchDarkly's Guarded Releases to monitor guardrail metrics (error rate, p95 latency) during rollouts.
- Automated rollbacks trigger at the flag level, meaning no redeploy is required to revert a bad change.
- Maintains a record of experiments and outcomes to feed future hypothesis generation, preventing repetitive failures.
- Requires the implementation of a custom Experiment MCP Server to handle mutation operations like code writing and deployment triggering.
Decoder
- MCP (Model Context Protocol): An open standard that allows AI models to connect to external systems and tools consistently.
- Closed-loop: A control system that uses feedback from the output to automatically adjust its own operation.
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