---
Title: "AI agent vendor selection scorecard: the RFP template for agent platforms"
Url: "https://devrev.ai/blog/ai-agent-vendor-selection-scorecard"
Published: "2026-09-17"
Last Updated: "2026-09-17"
Author: "Akshaya Seshadri"
Category: "Blog, Computer"
Excerpt: "Your SaaS vendor scorecard won't work for agents. Here's the weighted scorecard and RFP question set that scores the dimensions that actually separate agent platforms - memory, governance, action, and identity."
Reading Time: 10
---

# AI agent vendor selection scorecard: the RFP template for agent platforms

Most AI agent evaluations start with a demo and end with a gut call. The enterprises that get it right use a scored framework – but the standard SaaS vendor scorecard doesn’t cover the dimensions that actually separate agent platforms. This one does.

The instrument below is vendor-neutral by design. Score every shortlisted platform on the same weighted dimensions, ask every vendor the same RFP questions, and run them all against your own data. That’s how vendor selection stops being a trust exercise and becomes an evidence exercise. It’s current as of October 2026.

## TL;DR – the scorecard at a glance

- Your SaaS procurement scorecard doesn’t cover the dimensions that separate AI agent platforms. This one does – memory architecture, action governance, integration breadth, agent identity, accuracy, cost, and vendor maturity.
- Each dimension carries a weight and a paired RFP question that exposes the real answer, not the marketing one.
- Most enterprises still evaluate AI agents without a structured framework – they run a demo, form an impression, and make a gut call – which is exactly why a reusable weighted scorecard is worth bookmarking.
- Score independently, calibrate together, and let the numbers decide.

## What is an AI agent vendor scorecard?

An AI agent vendor scorecard is a structured evaluation tool that scores agent platforms across weighted dimensions – memory, governance, action, and identity – so a buying committee can compare vendors on the criteria that actually predict production success, not demo dazzle. Each dimension gets a weight, a definition of “good,” and an RFP question.

## Why generic SaaS scorecards miss for agents

### The dimensions that don’t appear on standard templates

Standard templates were built for software that stores data and renders screens, so they score uptime, support tiers, and license terms. But an AI agent remembers, decides, and acts across your systems, and three dimensions that decide production success never appear on a generic template: whether memory persists and stays permission-aware across sessions, whether actions are governed before they execute, and whether the agent operates under a real identity rather than a shared service account. Skip those, and you’re scoring the wrong product.

### Why model accuracy alone isn’t enough

Ranking vendors on model accuracy alone doesn’t work, because most platforms draw from the same foundation models. What separates them is how they ground answers in your data, govern what the agent can touch, and turn a decision into a safe action. Accuracy still counts – it’s a scored dimension below – but on its own it tells you little about which platform survives contact with production.

## The weighted scorecard

Copy this, hand it to procurement, and score every shortlisted vendor on the same seven dimensions. The weights are our methodology – they favor the memory-first, governance-heavy architecture that predicts production success. Adjust them to your context, but keep the ratios honest: memory and governance carry the most weight because they fail the quietest.

| Dimension | Weight | What “good” looks like | Sample RFP question |
| --- | --- | --- | --- |
| Memory architecture | 20% | Persistent, structured, permission-aware memory across sessions and users | “How does your platform persist and govern agent memory across sessions and users?” |
| Action governance | 20% | Granular human-in-the-loop controls, inherited permissions, an immutable audit trail | “Show us the audit trail for a high-risk action the agent executed last week.” |
| Integration breadth | 15% | Pre-built connectors plus an extensible API, with bi-directional sync | “How many production integrations ship out of the box, and how do we build custom ones?” |
| Agent identity and permissions | 15% | Per-agent identity, inherited user permissions, never a generic service account | “Does the agent execute as the requesting user’s identity or a shared service account?” |
| Accuracy and reliability | 15% | Benchmarked on your data at your scale, not the vendor’s demo set | “Run your agent on our dataset at two data scales and report accuracy and cost.” |
| Cost and pricing model | 10% | Usage-based with predictable scaling and no hidden inference surcharges | “Provide a 3-year total cost at 10K, 100K, and 1M monthly actions.” |
| Vendor maturity and support | 5% | SOC 2, ISO 27001, an enterprise SLA, and a named support contact | “Share your SOC 2 Type II report and incident response SLA.” |

Score each dimension 1 to 5, multiply by the weight, and sum. A vendor can dazzle on integrations and still fail production if memory resets every session or actions run ungoverned.

For why each dimension earns its place and who on your committee owns it, see the [buying criteria and stakeholder map](https://devrev.ai/blog/enterprise-ai-agent-buying-criteria) – this page is the instrument, that one is the rationale. For technical evaluation and benchmarking at the code level rather than a procurement instrument, see [evaluating AI agents](https://devrev.ai/blog/evaluating-ai-agent).

## The RFP question set – by dimension

The scorecard tells you what to score; these questions tell you what to ask so the score reflects reality, not the vendor’s pitch. Each one’s drawn from real enterprise evaluation patterns and written to separate real answers from marketing. When a vendor answers “yes” to a broad claim, the follow-up “show us” is where the truth surfaces.

### Memory architecture (weight: 20%)

- How does your platform persist and govern agent memory across sessions and users?
- When our CRM renames a field, who maintains the semantic mapping, and how long does it take?
- Is memory permission-aware, so an agent only recalls what the current user is allowed to see?
- Show us how context accumulates across a multi-step resolution rather than resetting each turn.

### Action governance (weight: 20%)

- Show us the audit trail for a high-risk action the agent executed last week, from trigger to execution.
- Which actions require human-in-the-loop approval, and how is that boundary configured?
- Can we set different autonomy levels per action type – propose only, approve-then-execute, or fully automated?
- Is the audit trail immutable, and who can alter or delete it?

### Integration breadth (weight: 15%)

- How many production integrations ship out of the box?
- How do we build a custom integration, and what does that require from our team?
- Is sync bi-directional, and how do you handle conflicts and rate limits at scale?
- Which systems can the agent act in beyond your own product?

### Agent identity and permissions (weight: 15%)

- Does the agent execute as the requesting user’s identity or a shared service account?
- How does the agent authenticate to downstream systems?
- Do you support SSO and SCIM, and how do agent permissions stay in sync with our IAM?
- If a user’s access changes, when and how does the agent’s effective permission change?

### Accuracy and reliability (weight: 15%)

- Run your agent on our dataset at two data scales and report accuracy and cost at each.
- What’s your accuracy on our data versus your published demo benchmark?
- How does accuracy hold up as our knowledge base grows tenfold?
- What happens when the agent is uncertain – does it escalate, or does it guess?

### Cost and pricing model (weight: 10%)

- Provide a 3-year total cost at 10K, 100K, and 1M monthly actions.
- Are there inference or token surcharges beyond the license, and how do they scale?
- Can we bring our own model to control inference cost?
- For a full cost breakdown, model it against the [AI agent TCO model](https://devrev.ai/blog/ai-agent-total-cost-of-ownership).

### Vendor maturity and support (weight: 5%)

- Share your SOC 2 Type II report and incident response SLA.
- Who is our named support contact, and what’s the escalation path?
- How long have you run this platform in production at enterprise scale?
- What’s your roadmap for the dimensions above over the next 12 months?

## Red flags that should zero a vendor

Some answers aren’t a low score – they’re a disqualification. If a shortlisted vendor does any of the following, treat it as a hard stop, not a deduction. You’re not scoring around these – you’re ruling the vendor out.

- **Refuses to run benchmarks on your data.** A vendor confident in production accuracy will test on your dataset at your scale. One that insists on its own demo set is protecting the number.
- **Identity is a shared service account for all agents.** If every user shares the agent’s permission level, you’ve built a permission-escalation path straight into your stack.
- **No immutable audit trail.** If you can’t reconstruct what the agent did, when, and on whose behalf – or if those logs can be edited – governance is theater.
- **Pricing requires a custom quote with no published methodology.** Opaque pricing at evaluation time becomes a budget surprise at scale. For deeper security disqualifiers, cross-check the [security review checklist](https://devrev.ai/blog/ai-agent-security-review).
- **Governance lives in the prompt, not the data layer.** “We instruct the model not to” is not enforcement. Permissions have to hold even when the prompt is manipulated.
- **Memory resets every session.** An agent that starts fresh each time can’t handle complex, multi-step resolution – no matter how good the demo looked.

## How to run the scoring

A scorecard’s only as good as the process around it, so here’s how to run it so the numbers actually mean something.

### Who scores

Give each dimension to the stakeholder who owns it. Security scores action governance and identity, while IT and engineering take accuracy, reliability, and integration breadth. Procurement and finance handle cost and vendor maturity, and the platform lead scores memory architecture. Nobody scores a dimension they don’t own – that’s how a single loud voice skews the result. For the full committee map, see the [buying criteria and stakeholder map](https://devrev.ai/blog/enterprise-ai-agent-buying-criteria).

### The POC-to-score pipeline

Shortlist 3 to 5 vendors, then put each through a structured proof of concept on your own data – same criteria, same benchmarks, same dataset for everyone, so it’s a level playing field. Score each POC against the full scorecard. It’s built to test the hard parts – memory at scale, permissions under load, action governance across systems – not the polished happy path the vendor wants to show.

### Normalizing across evaluators

Have each evaluator score independently before anyone compares notes. Then calibrate: walk through any dimension where scores diverge by more than a point and reconcile the evidence, not the opinions. That’s what keeps the loudest evaluator from anchoring the room, and it surfaces exactly where your team disagrees about what “good” really looks like.

## FAQ

### How many vendors should you shortlist for AI agent evaluation?

Shortlist 3 to 5. Fewer than three and you have no basis for comparison; more than five and the proof-of-concept effort balloons past what most teams can run rigorously. Use the scorecard’s early-round dimensions to narrow a long list before the POC stage.

### What’s the most important criterion for AI agent selection?

There’s no single winner, which is why the scorecard weights rather than ranks. That said, memory architecture and action governance carry the most weight – 20% each – because they fail the quietest. A platform can look flawless in a demo and still reset context every session, or it’ll run actions ungoverned once it hits production.

### Should you weight all evaluation criteria equally?

No. Equal weighting treats a nice-to-have like a deal-breaker. Weight by production risk: memory and governance carry the most, integration and identity and accuracy sit in the middle, cost and vendor maturity round it out. Adjust the ratios to your context, but keep them deliberate.

## Sources and methodology

The seven dimensions and their weights (20/20/15/15/15/10/5) are our own evaluation methodology, refined from patterns across real enterprise agent evaluations. The RFP questions are drawn from anonymized enterprise RFP and proof-of-concept criteria; we don’t name any enterprise. Any statistic here is hyperlinked to its primary source or flagged for verification before publication.

Vendor selection stops being a trust exercise the moment it becomes an evidence exercise. Score every shortlisted vendor on the same weighted dimensions, with the same RFP questions, against your own data, and let the numbers do the talking. And if you’re still weighing whether to buy a platform at all or build your own, that decision sits one level up in the [build-vs-buy framework](https://devrev.ai/blog/build-vs-buy-ai-agents).