Start with the actual job
Describe the task in terms of its trigger, inputs, decisions, exception cases and owner. If the same structured input always produces the same output, conventional automation may be cheaper to operate and easier to audit. AI is useful when the work includes language, messy documents or judgment over incomplete information. An agent is a further step: it selects and performs actions, which introduces more operational risk.
Compare three implementation patterns
Rule-based automation follows explicit conditions and predetermined steps; it excels at moving records, notifying owners and checking required fields. An AI-assisted workflow adds classification, extraction or drafting while a human approves the result. An agent can select tools and decide between possible next actions. The final option requires tighter permissions and clearer recovery procedures than a simple workflow.
Design the guardrails before autonomy
Choose which actions can happen without review and which require authorization. Define the data an agent can read, the systems it can update, transaction limits and escalation triggers. Record what happened and why. The ability to pause, reverse or route a decision to a person is as important as the success path shown in a demonstration.
Run a narrow pilot
Measure outcomes that matter to the team: time to resolution, accuracy, exception rates, review burden and maintainability. Start with one workflow and representative real examples. If the initial design still creates more manual cleanup than it removes, simplify it. A smaller automation that runs reliably is a better business investment than an impressive but unpredictable agent.
Use a decision table before selecting technology
If every input follows a documented pattern and the next action can be expressed as explicit rules, begin with conventional workflow automation. If the trigger is predictable but the content is messy, add a tightly scoped AI interpretation step that returns structured data for rule-based validation. Only consider a tool-using agent when the process genuinely requires branching, reasoning over changing context or selecting between permitted actions. The extra autonomy must earn its cost in testing and oversight.
- Rule-based routing: predictable decisions and low autonomy.
- AI-assisted workflow: language classification with controlled outputs.
- Agent: multiple bounded tool choices with audit and approvals.
Define an action budget and approval boundary
Write an allowlist of tools, records and environments an agent may use. A draft email and an actual payment transfer are not equivalent actions. Require human approval for sensitive changes, set limits on task duration and requests, and stop on repeated failures. Protect prompts and retrieval sources from untrusted instructions, because a document or webpage can try to redirect an agent. Store useful audit records without exposing customer content unnecessarily.
Budget for ongoing evaluation
A workflow can fail when field mappings change; an AI step can also fail when policy, source material or model behavior changes. Build a small evaluation set from real, permission-cleared examples. Include ordinary cases, ambiguous requests, malicious instructions and expected escalations. Define quality and intervention metrics before a pilot. Compare operational cost, latency, recoverability and the human work still required; do not assume an agent will always be more capable or cheaper.