feat: Workflow Recording and Replay System for Amazon Q

## Problem Statement

Currently, Amazon Q operates in a stateless conversation mode where each interaction is independent. Users must manually repeat complex multi-step operations, leading to:

• Repetitive manual work for common task sequences
• Inconsistent execution of complex procedures
• Lost knowledge when conversations end
• No way to share proven automation patterns

## Proposed Solution

Amazon Q should support workflow recording and replay functionality with commands like:

• /workflow create - Start recording a new workflow
• /workflow create_from_conversation - Create workflow from current conversation history
• /workflow run - Execute a saved workflow
• /workflow list - Show available workflows
• /workflow stop - Stop current recording

## Key Features

1. Tool Call Chain Recording - Capture sequences of tool invocations (fs_read, fs_write, execute_bash, use_aws, etc.)
2. Logic Preservation - Record conditional logic, error handling, and decision points
3. Parameter Templating - Allow parameterization of workflows for reuse with different inputs
4. Error Handling - Record and replay error recovery patterns
5. Workflow Persistence - Save workflows locally for future sessions

## Use Cases

• Automate repetitive development tasks (setup, testing, deployment)
• Create reusable infrastructure provisioning workflows
• Build standardized troubleshooting procedures
• Share common automation patterns across teams

## Steps to Reproduce Current Limitation

1. Perform a complex multi-step task with Amazon Q (e.g., setting up a project, deploying infrastructure, debugging an
issue)
2. Need to repeat the same sequence later
3. Must manually re-enter all commands and recreate the logic flow
4. No mechanism exists to capture and replay the successful workflow

## Expected Behavior

Users should be able to record successful multi-step operations and replay them with different parameters, creating a personal automation library that grows over time and can be shared across teams.