Deployment Strategies
Master deployment strategies for AI agents including staging, canary releases, and rollback procedures
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0 / 5 completedWhy Deployment Strategy Matters
Deploying AI agents to production is high-risk. A bad deployment can break user experiences, generate incorrect responses, or cause costly failures. Safe deployment requires staging environments, gradual rollouts, continuous monitoring, and instant rollback capabilities. Never deploy directly to production.
The Cost of Bad Deployments
- •Knight Capital (2012): $440M loss in 45 minutes from deployment bug
- •GitLab (2017): 6-hour outage, 300GB data deleted from bad migration
- •AI agents: Hallucinations, wrong actions, user trust destroyed instantly
Interactive: Deployment Environments Explorer
Explore the three core deployment environments and their purposes:
Deployment Principles
- ✓Test in staging first: Catch bugs before users see them
- ✓Roll out gradually: Start with 1% traffic, increase slowly
- ✓Monitor continuously: Watch error rates, latency, user satisfaction
- ✓Prepare rollback: Be ready to revert in seconds, not hours
Deployment is a process: staging validation → gradual rollout → monitoring → optimization or rollback. A "successful deployment" means users are happy 72 hours later, not that the code reached production. Plan for the worst, hope for the best, and always have an escape hatch.