Enterprise selection is a risk process
Enterprise teams should not buy an agent only because it demos well. The selection process needs task evidence, security review, data boundaries, cost controls, escalation rules, and model-change monitoring.
- Define workflows and unacceptable failures before choosing vendors.
- Separate draft assistance from autonomous actions.
- Keep evidence that procurement and risk teams can review.
The shortlist should be plural
Most enterprise teams need more than one candidate. One agent may be stronger in customer-facing writing, another in structured extraction, and another in cost-sensitive internal workflows.
Pilot design
Run a 30-day pilot with real samples, human repair tracking, failure tags, and a clear decision rule for expanding, limiting, or stopping automation.
Low-risk places to start
use-case guidance can usually start with drafting, tagging, summarization, routing, and internal notes. These steps create value while keeping humans in control of final commitments, customer-visible replies, and system writes.
- Use the agent as a recommender before it becomes an actor.
- Keep raw inputs and outputs for review.
- Measure human repair time, not only model score.
Where not to automate first
Refunds, compensation, legal obligations, account permissions, compliance claims, and angry escalations should not be fully automated until the evidence is strong. Start with evaluation, then draft mode, then limited automation.
Pre-launch checklist
Before using this use case in production, run a small retest with real inputs, edge cases, and a plan for what happens when the agent fails.
- Is there a clear human-review rule?
- Are model version and evaluation date recorded?
- Which outputs are not allowed to be sent or written automatically?
- Is there a fallback path when the agent fails?
A practical next step
If you are evaluating this use case, start with ten real samples: three normal cases, three edge cases, two high-risk cases, and two cases with strict language or formatting requirements. Run two or three candidate agents and compare quality, repair time, and critical failures.