Short answer: an AI agent is the wrong tool when the task is predictable, low-ambiguity, high-volume, tightly regulated, or easy to solve with rules, search, forms, workflow automation, or a human approval queue. Use agents when the system needs flexible reasoning across changing context, multiple steps, and uncertain decisions.
AI agents are useful when they can inspect context, choose tools, recover from partial failure, and adapt a plan. They are also more expensive and harder to test than deterministic software. The goal is not to avoid agents. The goal is to use agent autonomy only where it creates value that simpler automation cannot provide.
This checklist is written for teams deciding whether to build an AI agent, a RAG assistant, a rule engine, a workflow, or a human-in-the-loop process.
1. Use rules when the decision is already known
If the business logic is explicit, use deterministic rules. Do not ask a model to rediscover a policy that can be encoded directly.
Rules are usually better for:
- Eligibility checks.
- Price thresholds and discount limits.
- Required field validation.
- Compliance blocks and allowlists.
- Routing based on known categories.
- Structured data transformations.
- Simple if-this-then-that workflows.
Agents can still explain rule outcomes or help users provide missing information, but the rule should make the decision.
2. Use structured outputs when the task is extraction
If the core task is turning unstructured text into a known schema, start with structured extraction before building an agent. OpenAI Structured Outputs are designed to make model outputs conform to a supplied JSON Schema (OpenAI Structured Outputs docs).
Structured extraction is often enough for:
- Lead qualification fields.
- Invoice and receipt fields.
- Support-ticket labels.
- Contract clause extraction.
- Meeting action items.
- Search filters and metadata.
Add agent behavior only if the system needs to choose among tools, ask follow-up questions, resolve conflicts, or perform multi-step work after extraction.
3. Use search or RAG when the user mainly needs facts
If users need answers from a bounded knowledge base, a retrieval-augmented assistant may be enough. The system retrieves relevant sources and generates a grounded answer. It does not need broad tool autonomy.
Search or RAG is usually better when:
- The answer should come from approved documents.
- The main risk is stale or missing evidence.
- The user does not need the system to take action.
- Every answer should include citations.
- Authorization boundaries are document-based.
For retrieval quality, use the RAG evaluation checklist. If the assistant starts writing to systems, changing records, or making external calls, then agent controls become relevant.
4. Use workflow automation when the path is stable
Many “agent” projects are really workflow projects. If the process has a stable sequence, encode the sequence directly.
A workflow is usually better for:
- Intake forms.
- Approval chains.
- Invoice routing.
- Customer onboarding steps.
- Security review checklists.
- Incident response playbooks.
- Deployment release gates.
An LLM can summarize, draft, classify, or explain within a workflow. That does not mean the LLM should control the workflow.
5. Use human approval when the downside is high
When a mistake has meaningful customer, security, legal, financial, or operational impact, use an approval queue. Agent autonomy should not bypass accountability.
Require human approval for:
- External emails or customer commitments.
- Refunds, credits, purchases, or payments.
- Permission changes.
- Deleting or exporting sensitive data.
- Deployments, rollbacks, or production changes.
- Security exceptions.
- Actions that are hard to reverse.
OpenAI’s Agents SDK includes human-in-the-loop patterns for interrupting execution before sensitive tool calls (OpenAI Agents SDK human-in-the-loop docs). For implementation patterns, see the human-in-the-loop AI agents checklist.
6. Watch for excessive agency
The OWASP Top 10 for LLM Applications includes excessive agency as a risk category for systems that grant too much functionality, permission, or autonomy to an LLM-based component (OWASP LLM Top 10).
Warning signs include:
- The agent can call tools that are not needed for the task.
- Tool permissions are broader than the user’s own permissions.
- The agent can write, delete, or send data without approval.
- Prompt instructions are used instead of deterministic authorization.
- Logs do not show which tool was called and why.
- Rollback is manual or unclear.
If these controls are missing, reduce the agent’s tool scope before expanding its capabilities.
7. Prefer narrow agents over general agents
When an agent is justified, keep it narrow. A focused agent with a small tool set is easier to evaluate, monitor, and secure than a general assistant with broad access.
Constrain:
- The task it is allowed to perform.
- The data sources it can retrieve from.
- The tools it can call.
- The arguments it can pass to tools.
- The actions that require approval.
- The runtime budget for cost and latency.
For cost and budget controls, use the AI agent cost control checklist. For release evaluation, use the LLM regression test suite checklist.
8. Make the build decision explicit
Before approving an agent build, answer these questions:
- Can deterministic rules solve the decision?
- Can structured extraction solve the input problem?
- Can search or RAG solve the knowledge problem?
- Can workflow automation solve the process problem?
- Can human approval handle the risky step?
- Does the task require multi-step reasoning with uncertain context?
- Can the agent be evaluated before release?
- Can tool use, cost, latency, and safety be monitored in production?
If the first five answers are yes, an agent may be unnecessary. If the last three answers are no, the agent is not production-ready.
Minimum decision checklist
- Use rules for known policies and explicit thresholds.
- Use structured outputs for schema-bound extraction.
- Use search or RAG for grounded answers from approved sources.
- Use workflow automation for stable business processes.
- Use human approval for high-impact or hard-to-reverse actions.
- Use agents only when the task needs flexible multi-step reasoning and tool selection.
- Keep agents narrow, scoped, observable, and testable.
- Reject broad tool access without deterministic authorization and audit logs.
- Measure cost, latency, task success, and safety before production launch.