Is your AI system
actually ready for production?
Use this 30-question AI production readiness checklist to assess evaluation coverage, RAG quality, agent permissions, hallucination controls, cost management, observability, provider coupling, privacy, rollback, and operational ownership.
Receive an instant score across ten production-readiness dimensions and a prioritized remediation backlog. No email or account is required to complete the assessment or see the result.
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Score by dimension
Prioritized remediation backlog
What does
production-ready AI mean?
A production-ready AI system is more than a model that returns good answers during a demonstration. It is a complete software system with measurable quality, controlled access, predictable operating cost, observable behavior, documented ownership, and tested responses to failure.
The model is only one component. Production reliability also depends on the retrieval layer, prompts, tools, APIs, data permissions, validation, fallback logic, deployment process, monitoring, and human operating model surrounding it.
An AI system may appear accurate while still being unready for production because it can't answer questions such as: what measurable threshold must a release pass? What happens when the model is uncertain or a dependency fails? Can a user retrieve information outside their access rights? Can an agent take an irreversible action without approval? Is there a hard limit on cost, steps, and retries? Can one interaction be traced across retrieval, model calls, and tools? Can a prompt, model, or provider change be rolled back quickly?
This assessment is designed to expose those operational gaps before they become customer incidents.
What the assessment
covers.
Each dimension is scored independently so a strength in one category doesn't hide a serious weakness in another.
Production AI needs repeatable evaluation rather than occasional manual testing. This checks whether evaluations run before releases, whether acceptable quality thresholds are defined, and whether test data includes realistic edge cases and adversarial inputs. The goal isn't one universal accuracy score — it's evidence that a release is at least as reliable as the version it replaces.
A production RAG system should retrieve relevant information, expose sources when verification matters, respect document-level permissions, and keep indexed content fresh when source data changes. Retrieval quality and answer quality should be evaluated separately — a fluent answer can't repair missing or unauthorized context.
An agent that calls tools can create consequences beyond a poor answer. This checks whether each tool is explicitly scoped, whether high-impact actions require approval, and whether hard limits prevent uncontrolled loops, retries, or spend. Reading a public knowledge base is not equivalent to issuing a refund or deleting data.
No model is correct on every input. Readiness depends on how the system behaves when evidence is weak, an intent is out of scope, validation fails, or a dependency is unavailable — abstention, structured-output validation, deterministic checks, human handoff, and defined escalation paths.
Small per-call costs become material when requests trigger multiple model calls, tool loops, retries, or long context windows. This checks whether the system enforces cost limits in code, bounds retry behavior, measures spend per workflow, and avoids unnecessary calls through caching or routing.
When an AI interaction fails, the team should be able to reconstruct what happened — a trace across the request, retrieved context, prompt version, model, completion, validation, tool calls, latency, cost, and final response. Observability should surface regressions before they become repeated complaints.
Direct provider SDK calls scattered through a codebase make model changes, outages, and API deprecations harder to manage. This checks whether model access is isolated behind an internal interface and whether an alternative provider has been tested — the architecture should contain the differences, not require identical model behavior.
Teams should know what data reaches each model provider, how long it's retained, where it's processed, and whether sensitive information must be removed before inference. Assumptions about zero retention or regional processing aren't a substitute for verified configuration and contractual terms.
Prompts, models, retrieval settings, and agent policies should deploy with the same operational discipline as other production changes — versioning, gradual rollout, rollback, alerts, and an incident plan for hallucination spikes, cost anomalies, unsafe actions, or provider outages.
A production system needs a named owner and documented operating procedures. A new engineer should understand the system's intended scope, known limitations, quality thresholds, common failure modes, escalation path, and rollback procedure without relying on tribal knowledge.
How the readiness
score works.
Each question is answered Yes, Partial, or No. The tool scores each dimension independently so a strength in one category doesn't hide a weakness in another.
Yes means the control is implemented, documented, and used in practice. Partial means some elements exist but coverage, enforcement, documentation, or ownership is incomplete. No means the control is absent, unknown, or dependent on informal practice.
The result is a planning aid, not a certification. A high total score doesn't cancel a critical failure in data permissions, irreversible actions, privacy, or rollback — review the dimension-level result and individual remediation items, not just the overall number.
When to run
this checklist.
Use the assessment at the moments that actually change your risk.
- Before moving an AI proof of concept into production.
- Before giving an agent access to customer data or business systems.
- Before launching a RAG assistant to employees or customers.
- Before a security, architecture, or procurement review.
- After a major model, provider, retrieval, or agent-framework change.
- When inheriting an AI system with unclear controls or ownership.
- After an incident, cost spike, or unexplained drop in quality.
What to do
with the result.
Start with critical gaps that can create data exposure, irreversible actions, uncontrolled cost, or an inability to recover from failure. Then address gaps that prevent reliable measurement, such as missing evaluation data and incomplete traces.
- Contain high-impact permissions and sensitive-data risks.
- Add hard limits, fallbacks, and rollback paths.
- Establish evaluation data and release thresholds.
- Add end-to-end tracing and cost visibility.
- Document ownership, runbooks, and review cadence.
Retake the assessment after material improvements and compare the dimension-level scores. The objective isn't a perfect score for presentation purposes — it's evidence that the controls appropriate to your use case are working.
Before you
share this internally.
What this tool is, and what it isn't.
What is an AI production readiness checklist?
An AI production readiness checklist is a structured review of the controls required to operate an AI system reliably after launch. It extends beyond model quality to cover evaluation, data access, agent permissions, fallback behavior, cost, monitoring, privacy, deployment, incident response, and ownership.
Is this the same as an AI readiness assessment for a company?
Not exactly. A company-level AI readiness assessment usually examines strategy, use cases, data availability, skills, governance, and investment priorities. This tool focuses on whether a specific AI system or workflow is ready to operate in production — see our AI Readiness Assessment service for the company-level version.
Can I use this checklist for a RAG system?
Yes. It includes retrieval quality, source citations, document permissions, source freshness, answer evaluation, privacy, tracing, and rollback — see our RAG Development page. These controls apply whether the RAG system supports employees, customers, or an AI agent.
Can I use it for an AI agent?
Yes. The assessment covers tool permissions, human approval, maximum steps, retries, cost limits, tracing, escalation, and irreversible actions — see our AI Agent Development page. For a detailed action-by-action policy, use the Agent Permission Matrix Generator after completing the assessment.
Does a high score certify that our system is safe or compliant?
No. The score is not a security audit, legal opinion, compliance certification, penetration test, or guarantee of safety. It helps identify areas that require evidence and expert review — see our Trust & Safety page for how Dwayo approaches that work.
How often should we repeat the assessment?
Repeat it before a major launch and after material changes to models, providers, tools, data sources, permissions, or deployment architecture. It's also useful after an incident or when operational ownership changes.
What happens if we request a Dwayo review?
A Dwayo engineer reviews the scorecard and the assumptions behind it, identifies the highest-risk gaps, and recommends the next engineering step. Sharing the result is optional and happens only after you explicitly request it — see our AI Readiness Assessment service for a deeper, paid version of this review.
A score tells you where to look.
Evidence tells you it's fixed.
Complete the assessment to get your score and remediation backlog. If the gaps are bigger than a quick fix, a Dwayo engineer can review the result with you.