AI features fail when teams treat them like normal UI features. Before launch, product teams need a readiness gate that covers behavior quality, operations, and risk controls.
Product readiness
Confirm:
- User problem is clearly defined
- Success metric is measurable
- Scope is intentionally narrow for first release
- Human fallback path exists
If your success metric is vague, postpone launch.
Data and context readiness
Validate:
- Retrieval sources are trustworthy
- Context formatting is deterministic
- Sensitive data filters are in place
- Data access permissions are audited
Bad context causes more failures than weak model choice.
Model and prompt readiness
Before launch:
- Document model selection rationale
- Version prompt templates
- Define guardrails and refusal behavior
- Test against known adversarial or edge inputs
Do not launch with unversioned prompts.
Evaluation readiness
Build an eval set with real task examples:
- Happy path cases
- Ambiguous intent cases
- High-risk failure cases
Define pass thresholds before shipping.
Operational readiness
Prepare:
- Monitoring for success rate and latency
- Budget alerts for cost spikes
- Rollback path for prompt and tool regressions
- On-call owner and incident playbook
No monitoring means no production visibility.
Related reading
- /blog/ai-agent-ops-checklist-monitoring-rollbacks/
- /blog/how-to-build-an-agent-a-practical-starting-point/
- /blog/spec-driven-development-sdd-guide/
If you are planning AI feature launches and need delivery support, contact our team at /contact/.
