AI Agent RFP Template for Business Buyers
A practical AI agent RFP template for comparing vendors on workflow scope, integrations, permissions, human approvals, pricing, auditability and pilot design.
AI agent RFP template
Use these sections when asking vendors, internal teams or agent builders to propose an AI-agent solution. The goal is to make proposals comparable instead of accepting vague automation claims.
1. Workflow and outcome
- Business process to automate
- Current manual steps and monthly volume
- Expected output and acceptance criteria
- Required turnaround time
2. Data and integrations
- Systems the agent may read
- Systems it may write to
- Required APIs, browser access, files or messaging channels
- Data that must never be exposed to the agent
3. Controls
- Human approval points
- Least-privilege permissions
- Credential storage
- Audit logs and execution history
- Pause/kill switch
4. Commercial model
- Setup fee
- Platform or hourly fee
- Model/API cost ownership
- Support and maintenance
- Exit and portability terms
5. Pilot plan
Require a narrow pilot with measurable acceptance tests before granting broader permissions. Ask vendors to describe failure handling, escalation and what evidence will be supplied at the end of the pilot.
Vendor comparison table
| Criterion | Vendor A | Vendor B | Vendor C |
|---|---|---|---|
| Total monthly cost | |||
| Model/API ownership | |||
| Human approvals | |||
| Audit history | |||
| Kill switch | |||
| Portability |
How to use the RFP template to compare AI agent vendors
A useful AI agent RFP describes the work to be completed, not just a list of fashionable technical features. Begin with the business outcome, current process, monthly volume, users involved and examples of acceptable output. Vendors should be able to respond against the same workflow so pricing and capability claims are comparable.
Specify inputs and evidence. Describe which systems, files, records or web sources the workflow may use, how fresh the information must be, and which sources are authoritative when data conflicts. Ask how the product records provenance or citations when reviewers need to verify an answer.
Separate integrations from permissions. A platform may technically integrate with a CRM, email system or browser while still lacking the granular controls your risk model requires. Request details on read versus write access, credential storage, tenant isolation, approval gates and how access can be revoked immediately.
Ask every vendor to demonstrate evaluation using a representative test set. Define task-success criteria, unacceptable failures, review requirements, latency targets and cost reporting. Require version information for the model, workflow and configuration used during the test so the result can be reproduced after a release.
Commercial questions should expose the full cost stack. Ask what the platform price includes, whether model/API usage is separate, how third-party tools are billed, whether setup time is charged, and what happens when a task retries or waits for human approval. Compare expected monthly cost and cost per accepted output rather than headline price alone.
Finally, define the pilot and exit plan. A good RFP states pilot duration, data volume, owners, success thresholds, security review, rollback conditions and the data or configuration that must be exportable if the relationship ends. Portability matters because agent workflows often accumulate prompts, instructions, integration mappings and operational history.
Frequently asked questions
What should an AI agent RFP include?
At minimum: workflow outcome, inputs and outputs, integrations, permissions, approval controls, evaluation criteria, security requirements, pricing structure, pilot plan and exit/portability requirements.
How many vendors should be compared?
Use enough vendors to reveal meaningful trade-offs, but keep the same test workflow and scoring method for every candidate so the comparison remains fair.
Should vendors be asked for benchmark scores?
Benchmarks can be useful context, but require evidence on your own representative tasks because general benchmark performance may not predict your workflow.