How should a company select an AI agent vendor?
Start with a defined workflow and hard requirements, then evaluate vendors against the same representative case. Decide whether you need a software platform, custom development firm, managed agent operator, or a combination. These categories transfer different responsibilities to the buyer.
Do not begin with a feature matrix copied from marketing pages. Require evidence that the vendor can work within your data, system, permission, evaluation, and operating constraints.
What belongs in an AI agent vendor scorecard?
Weight criteria according to workflow risk and team capability:
- Workflow and domain fit
- Integration depth and tool controls
- Identity, permissions, data handling, and auditability
- Evaluation, tracing, monitoring, and incident response
- Reliability, scale, latency, and recovery
- Implementation method and customer responsibilities
- Support, service boundaries, and change management
- Pricing at expected and peak use
- Data export, portability, and exit costs
- Company stability and dependency risk
Define what evidence earns each score. A roadmap promise should not receive the same rating as a capability demonstrated in the proof environment.
What questions should AI agent vendors answer?
Ask who can access prompts and traces, how credentials are stored, how tenant boundaries are enforced, whether models train on your data, where processing occurs, how tool calls are authorized, what logs are available, and how incidents are communicated.
Ask operational questions too: who monitors runs, what happens when an integration changes, how model upgrades are tested, what support includes, how costs are limited, and what artifacts you retain if the relationship ends.
How should an AI agent vendor proof be run?
Give finalists the same bounded workflow, approved sample data, common exceptions, and evaluation criteria. Include at least one real integration and a failure scenario. Observe the implementation process, not only the final demonstration.
Measure build effort, completion quality, tool accuracy, trace usefulness, recovery, user experience, latency, and projected cost. Record how much custom work remains and which party must perform it.
How should AI agent vendor pricing be compared?
Normalize platform fees, implementation, model usage, connector charges, run or task fees, storage, observability, premium support, minimum commitments, overages, and internal operating labor. Model expected and peak volume plus failure retries.
Review contract terms for data use, service levels, security obligations, change notice, liability, subcontractors, termination assistance, and export. Legal and procurement teams should evaluate the actual agreement for your organization.
Make the selection reversible
Keep business rules, tool schemas, evaluation cases, and workflow documentation portable where practical. Require exports and test them. Use staged commitments tied to evidence rather than committing the full operation after a generic demo.
ClawRevOps can use a War Room to define a workflow, vendor requirements, and proof scorecard. We may also be an implementation option, so that role and any potential conflict should be explicit during evaluation.
Book a War Room session to build the evaluation criteria.