# Reversibility Architect Prover MCP for AI Agents AI Agent Connect > Reversibility Architect Prover acts as a mandatory safety gate for your production environment. It stops your AI client from proposing destructive database migrations or big-bang deployments by forcing a rigorous SRE-style validation. It ensures every architectural change has a clear rollback path, a defined blast radius, and a concrete feature flag strategy before a single line of code goes live. ## Overview - **Category:** devops - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Yu3o4BBRqOWxr3Aek5fueq38ENcp7Ow6rdrGSBfM/ai-agent-connect - **Tags:** reversibility, sre, rollbacks, canary, multilingual, deployment-safety ## Description The Reversibility Architect Prover forces your agent to think like an SRE before it touches production. It's easy for an AI to suggest a "quick" database fix that actually wipes out a table or locks the site for hours. This Connector stops that by acting as a cognitive gate. It forces your agent to map out rollbacks, define exact error thresholds for aborts, and plan for a limited blast radius before anything goes live. You'll see your agent stop suggesting "rename-then-pray" tactics and start building in feature flag kill switches as a standard. By adding this to your Vinkius catalog, you're essentially giving your agent a senior ops brain. It moves the workflow from high-risk guessing to a validated process where every change is checked for its impact on your users and your data. ## Tools ### validate_reversibility This tool forces the agent to analyze data migrations, rollback criteria, blast radius, downtime, feature flags, and post-mortems. It provides a clear verdict on whether a proposed change is safe to ship. ## Prompt Examples **Prompt:** ``` I need to rename the 'username' column to 'display_name' in our users table. What's the plan? ``` **Response:** ``` ### ❌ Verdict: DATA_CORRUPTION_RISK Renaming a column is an irreversible operation that will break any service still querying the old name. **Recommended Reversible Approach:** 1. **Add** a new column: `display_name`. 2. **Backfill** data: `UPDATE users SET display_name = username;`. 3. **Update** application code to read from `display_name`. 4. **Verify** for 1 week. 5. **Drop** the old `username` column. This follows the **expand-then-contract** pattern. ``` **Prompt:** ``` Can we deploy the new payment flow to production tonight? ``` **Response:** ``` ### ⚠️ Verdict: ALL_OR_NOTHING_DEPLOY To proceed safely, we need to define a rollout strategy. Please confirm the following: * **Canary Strategy**: Start at 1% traffic, then 10%, 50%, and 100%. * **Feature Flag**: Ensure there is a kill switch to set `payment_v2` to 0% in under 30 seconds. * **Rollback Criteria**: Define the threshold (e.g., Error rate > 4% for 2 minutes). ``` **Prompt:** ``` I want to add an index to the orders table which has 200 million rows. ``` **Response:** ``` ### ⚠️ Verdict: DOWNTIME_SURPRISE Adding an index to a 200M-row table on a standard production instance can lock the table for hours. **Safety Requirements:** * **Method**: Use `CREATE INDEX CONCURRENTLY` (PostgreSQL) to avoid locking. * **Timing**: This may take 6+ hours. Please schedule a maintenance window or notify users. * **Verification**: Confirm the timing on a production-scale replica before execution. ``` ## Capabilities ### Prove data migration reversibility The agent must confirm that data changes can be reverted without permanent loss. ### Define measurable rollback thresholds It forces the AI to set specific error rates and latency targets that trigger an automatic abort. ### Map out blast radius isolation The agent identifies exactly how many users are affected by a failure and plans canary rollouts. ### Estimate production-scale downtime It requires honest timing estimates based on production data rather than small development sets. ### Verify feature flag kill switches The agent must include a 0% to 100% rollout strategy for all user-facing changes. ### Simulate post-mortem failure scenarios It forces the agent to imagine a 2 AM failure and describe the exact steps to fix it. ## Use Cases ### The Rename-then-Pray Migration An agent wants to rename a column. validate_reversibility catches the risk and demands an additive approach to keep old code working. ### The Big-Bang Payment Flow A dev wants to launch a new payment system. The tool forces a 1% canary rollout and a feature flag kill switch to protect revenue. ### The Quick Index Creation An agent suggests adding an index. The tool forces a production-scale timing check to avoid locking the table for hours. ### The Dashboard Redesign A team wants to swap UI components. The tool ensures the old version stays active via flags until the new one is verified. ## Benefits - Stop irreversible data loss by forcing additive migrations with validate_reversibility. - Avoid all-or-nothing deployments by requiring canary strategies and feature flags. - Reduce 2 AM pages by defining exact error thresholds for automatic rollbacks. - Get honest downtime estimates by checking production-scale data instead of dev environments. - Limit the blast radius of bugs to a small percentage of users using canary rollouts. - Identify hidden dependencies early by simulating failures with post-mortem reflections. ## How It Works The bottom line is that it forces your agent to prove a deployment is safe before it's allowed to proceed. 1. Connect the Connector to your AI client via the Vinkius dashboard. 2. Instruct your agent to run the validation tool before any production deployment or migration. 3. Review the safety verdict and the required rollback steps provided by the agent. ## Frequently Asked Questions **Can Reversibility Architect Prover help me prevent database migration errors?** Yes. It forces your AI agent to prove that every migration follows an additive pattern, ensuring that you can always revert changes without losing data. **How does Reversibility Architect Prover handle rollback plans for my AI agent?** It requires the agent to define specific, measurable metrics that trigger an automatic rollback, removing guesswork from your deployment process. **Does Reversibility Architect Prover support canary deployment strategies?** Yes. It ensures your agent includes a step-by-step rollout plan, like moving from 1% to 100% of users, to limit the blast radius of any bugs. **Can I use Reversibility Architect Prover to estimate production downtime?** It forces the agent to provide honest downtime estimates based on production-scale data rather than just guessing based on your local development environment. **How does Reversibility Architect Prover help with feature flag safety?** It requires your agent to include a feature flag kill switch for every user-facing change, allowing you to instantly revert to the old version if needed. **Is Reversibility Architect Prover for all types of code changes?** It is specifically designed for high-stakes changes like database migrations, infrastructure updates, and production deployments where safety is the priority. **Why force a post-mortem simulation?** Optimism bias. Forcing the AI to explain WHY the deployment failed before it happens exposes edge cases it ignored. **Why is data corruption the first pivot?** Code can be rolled back. Data loss is permanent. If data isn't safe, the architecture is invalid. **What counts as a rollback criterion?** Measurable SLA violations, like '5xx errors > 1%' or 'Latency > 200ms'.