AI Agent RFP Template + Filled Vendor Scorecard for Enterprise Teams

This page gives you a reusable AI agent RFP scorecard in CSV format, plus a filled example you can adapt before sending vendors into demos or pilots. Use it when your team already has a real workflow in mind and needs a structured way to compare platforms, implementation partners, or custom-build proposals.

The scorecard is built for teams that care about more than model quality alone. It forces vendors to answer how the agent will use tools, how risky actions are controlled, how quality is evaluated, and how the system is monitored after launch.

When this template is the right move

• You already know the workflow you want to automate, such as support triage, order exception handling, account onboarding, or internal knowledge support.
• You need to compare multiple vendors or internal build options using the same rubric.
• Security, approvals, monitoring, and rollout risk matter as much as demo quality.
• You plan to run a pilot and want the RFP to lead directly into acceptance criteria.

If you are still at the stage of asking what should we automate first?, do not send an RFP yet. First narrow to one workflow, one owner, one launch boundary, and one measurable success target.

Copyable AI agent RFP scorecard

Use the CSV below in Google Sheets or Excel. Ask every vendor to answer the same questions, keep the weights fixed for round one, and score only against evidence they can actually show.

"Section","Question","Why it matters","Weight","Vendor response","Score (1-5)","Notes"
"Workflow fit","What exact workflow will the agent own from trigger to completion?","Prevents vague positioning and demo theater.","8","","",""
"Workflow fit","Which steps are deterministic workflows versus model-driven decisions?","Separates automation logic from probabilistic reasoning.","4","","",""
"Users and channels","Which users, channels, and languages are supported at launch?","Keeps scope realistic for phase one.","3","","",""
"Integrations","Which systems can the agent read from and write to on day one?","Tests real operational fit, not roadmap promises.","8","","",""
"Integrations","What authentication, permission, and least-privilege controls exist for tools?","Checks whether tool access is governable.","5","","",""
"Knowledge","How is enterprise knowledge connected, refreshed, and permissioned?","Reduces stale answers and over-broad retrieval.","5","","",""
"Security","How does the vendor mitigate prompt injection and unsafe tool use?","Critical for agent systems that can act, not just answer.","8","","",""
"Security","How are outputs validated before downstream actions execute?","Catches insecure output handling and bad automation paths.","6","","",""
"Governance","Can risky actions require human approval, escalation, or fallback?","Needed for refunds, account changes, approvals, and exceptions.","6","","",""
"Governance","Which admin controls, audit logs, and policy controls are included?","Supports legal, security, and operations review.","5","","",""
"Evaluation","How will success be measured before rollout starts?","Forces a checkable definition of done.","6","","",""
"Evaluation","Does the platform support repeatable test datasets, traces, or grading?","Makes future changes comparable instead of subjective.","5","","",""
"Observability","What traces, logs, and failure diagnostics are available?","Needed for debugging, auditability, and post-launch tuning.","5","","",""
"Observability","Which production metrics are monitored and alerted on?","Prevents blind spots after go-live.","4","","",""
"Deployment","Where does the agent run, and where is data stored or processed?","Affects security review and deployment fit.","4","","",""
"Commercials","How are usage, seats, and overages priced?","Avoids ROI surprises during the pilot or scale-up.","3","","",""
"Delivery","Who owns implementation, testing, and tuning during the pilot?","Clarifies delivery risk and resourcing.","4","","",""
"SLA","What uptime, support, and incident response commitments are offered?","Sets operating expectations before launch.","4","","",""
"Proof","Can the vendor show a comparable live deployment with measurable outcomes?","Separates real execution from polished positioning.","7","","",""

A simple scoring rule works well: 1 means unsupported or vague, 3 means partially supported with caveats, and 5 means proven with a live demo, documentation, or a reference deployment.

Filled example: SaaS customer support agent

Example scenario: a B2B software company wants an agent that handles billing questions, login issues, feature FAQs, and ticket triage before human escalation. The first release must integrate with the help desk, the knowledge base, and account data, but refunds and account deletions still require human approval.

Filled example score snapshot

In this example, a strong vendor is not the one with the flashiest demo. It is the one that can prove bounded workflow coverage, governed tool access, measurable pilot criteria, and operational visibility after launch.

How to send the RFP without getting demo theater

1. Send one concrete workflow. Do not ask vendors to solve five departments at once. A narrow workflow creates cleaner answers.
2. Share sample inputs. Include representative tickets, requests, documents, or exceptions so vendors respond to your real operating context.
3. Ask for live proof, not just slides. A recorded walkthrough or a sandbox run is more valuable than feature claims.
4. Define pilot success before demos. Decide what a pass looks like on quality, latency, escalation quality, completion rate, or operator effort.
5. Separate must-haves from differentiators. Security, approval gates, integrations, and auditability should not be buried under nice-to-have features.
6. Collect pricing in one format. Require the same assumptions for usage, seats, implementation, and overages so the comparison stays honest.

Minimum evidence to request

• A workflow map showing triggers, tool calls, approvals, and handoffs.
• A sample trace, log, or operator view for one completed run.
• A proposed pilot dataset with pass or fail thresholds.
• A short explanation of how risky outputs are validated before actions execute.
• A comparable customer reference or production example.

What to score separately

• Platform capability: what the product can do in principle.
• Implementation risk: how much custom work, tuning, and cross-team effort the rollout will need.
• Operating model fit: whether your team can monitor, govern, and improve the system after launch.

Common mistakes that lead to bad vendor choices

• Overweighting the base model. A better model does not fix missing approvals, weak integrations, or poor observability.
• Letting roadmap promises score like shipped features. Only score what can be shown or contractually committed.
• Skipping non-happy-path scenarios. Exceptions, retries, escalations, and policy breaches are where deployments usually fail.
• Using price without a usage shape. Token or seat pricing means little unless you define expected volume and tool usage.
• Treating a chatbot and an acting agent as the same product. Once tools can change systems, governance and validation matter much more.

If a vendor can answer the RFP only with slides, you still do not know whether the agent can run your workflow.

What to do after scoring vendors

1. Shortlist two or three options.
2. Run a time-boxed pilot with the same workflow and dataset for each option.
3. Instrument the pilot so failures, escalations, and tool errors are visible.
4. Review who will own the system after launch, not just who builds it.
5. Only expand to broader workflows after the first use case has a stable operating model.

If your use case is one bounded job, a single agent may be enough. If the workflow crosses multiple roles, approvals, and systems, score coordination and handoff design more heavily because you are evaluating an AI team, not just one worker.