Most AI support agent evaluations in B2B SaaS are decided on a demo. A vendor shows a polished chat window answering a handful of prepared questions, the resolution numbers on the slide look good, and the RFP that follows is written to confirm the choice rather than test it. Six months later the team discovers the agent cannot see which plan the customer is on, cannot do anything except link to a help article, and is silent in the Slack channels where half the real support conversations happen.

Knowing how to evaluate an AI support agent means knowing which questions separate a demo from a deployment. This post is a ten-point RFP checklist built for support and CX leaders at B2B SaaS companies. Each point comes with the question to put in the RFP, what a strong answer looks like, and the failure it is designed to catch. The underlying argument is simple: in B2B, the agent's access to account context, its ability to act, and where it lives matter more than how well it chats.

Why do B2B SaaS teams need a different AI support agent checklist?

B2B SaaS support is account-specific, multi-channel and technical, so an AI support agent built for consumer-scale FAQ traffic will look great in a demo and fail on the questions that actually generate tickets. A B2B checklist has to test whether the agent can reason about a specific account's configuration, integrations and entitlements, whether it can take actions rather than only answer, and whether it works in the surfaces where your customers already are.

A consumer support agent handles a large volume of similar, low-context questions: order status, password reset, return policy. The hard part is scale and language coverage. B2B SaaS support is the opposite shape. Volume is lower, but almost every question depends on who is asking. "Why didn't my sync run" has a different answer for every account, and the correct answer usually lives in the product, the CRM and the helpdesk at the same time. An evaluation that measures deflection on generic FAQ questions is measuring the wrong thing.

The checklist below is ordered by how often each item gets skipped, not by how easy it is to score.

What are the ten evaluation criteria for an AI support agent?

The ten criteria are: account context, knowledge sources, actions versus answers, surfaces, escalation and handoff, proactive detection, time to live, control and change management, measurement, and security. A vendor that scores well on all ten is not guaranteed to be the right fit, but a vendor that fails any of the first five is not a B2B support agent, whatever the demo shows.

1. Account context: can the agent see who is asking?

RFP question: Which systems does the agent read at answer time to identify the account, the plan, the enabled features and the open issues for the user asking?

Strong answer: The agent resolves the user to an account and pulls live data from the CRM, the helpdesk and the product before it responds. Ask the vendor to answer a question in the demo that is only correct for one specific test account.

Failure it catches: Agents that answer from documentation alone. They will produce a plausible generic answer to an account-specific question, which is worse than no answer because the customer has to discover it is wrong.

2. Knowledge sources: what does it learn from, and what does it refuse to say?

RFP question: List every source the agent can draw on (help center, internal docs, resolved tickets, Slack threads, product data) and describe what happens when the answer is not in any of them.

Strong answer: Multiple sources with clear precedence, and an explicit "I don't know, here is a human" path. Resolved tickets and internal engineering notes matter more in B2B than the public help center, because that is where the real answers to edge cases live.

Failure it catches: Agents that only index the public knowledge base and hallucinate confidently when a question falls outside it.

3. Actions versus answers: can it do anything?

RFP question: Which actions can the agent take on the customer's behalf, in which systems, and how is permission handled?

Strong answer: The agent can complete the task, not just describe it: reset an integration, change a setting, create a ticket with the right fields, update a CRM record, with a permission check where the action is consequential. Ask for a live demonstration of one write action end to end.

Failure it catches: "Agents" that are answer engines. If every response ends with a link to a settings page, you are buying a better search box, and your ticket volume will not move much.

4. Surfaces: where does the agent live?

RFP question: List the surfaces where the agent operates natively, and confirm whether they run on one configuration or separate ones.

Strong answer: Inside the product, in shared Slack channels and Microsoft Teams, and in the helpdesk, from a single engine with one set of knowledge and rules. In B2B SaaS, a large share of support happens in Slack Connect channels and in the product itself, not in a chat widget on the marketing site.

Failure it catches: Widget-only agents, and vendors that cover several surfaces with separately configured bots that drift apart over time.

5. Escalation and handoff: how does a human get involved?

RFP question: Describe the escalation path, what context the human receives, and how the agent decides when to hand off.

Strong answer: The agent escalates on defined signals (sentiment, account tier, repeated failure, topic), passes the full conversation plus the account data it already gathered, and can pull the right human into the existing channel rather than forcing the customer to start over.

Failure it catches: Dead ends. An agent that escalates by telling the customer to email support has added a step, not removed one.

6. Proactive detection: does it wait for a ticket?

RFP question: Can the agent detect a user who is stuck or failing before they ask for help, and what can it do about it?

Strong answer: The agent watches product behavior (repeated errors, abandoned setup, a feature that never gets used after activation) and intervenes in context. This is where support starts to affect activation, trial conversion and renewals, which is usually the business case that gets the budget approved.

Failure it catches: Purely reactive tools that reduce handle time on tickets that should never have been created.

7. Time to live: what does deployment actually involve?

RFP question: Describe the implementation plan, the roles required from our side, the engineering dependencies and the expected time to first live use.

Strong answer: Days to a few weeks, configured by the support or success team in plain language, with integrations connected through existing APIs or MCP rather than a custom build. Ask specifically whether a professional services engagement or a systems integrator is expected.

Failure it catches: Enterprise AI projects that consume a quarter before anything is live, and then need engineering time for every change.

8. Control and change management: who owns it after launch?

RFP question: How does a non-engineer change the agent's behavior, test the change and roll it back? How are updates to the product reflected?

Strong answer: A support lead can adjust rules, add knowledge and review transcripts without a ticket to engineering. Behavior changes can be tested against real conversations before they ship.

Failure it catches: Agents that need a vendor or a developer for every change, which in practice means they stop being updated after the first quarter.

9. Measurement: what does resolution mean?

RFP question: Define "resolved" as your reporting measures it, and show how we would verify that number independently.

Strong answer: Resolution means the customer's problem was fixed and they did not come back on another channel, verified against the helpdesk and CSAT, not "the conversation ended without escalation." Ask for the metric definitions in writing.

Failure it catches: Deflection numbers that count abandoned conversations as wins. A customer who gave up on the bot and emailed support is a failure that many dashboards report as a success.

10. Security and data handling: what does it see and where does it go?

RFP question: Describe data residency, retention, model training on customer data, access controls and the audit trail for actions the agent takes.

Strong answer: Clear answers to each, with customer data excluded from model training by default and a log of every action the agent took, which system it touched and on whose authority.

Failure it catches: Vendors that cannot explain what happens to a customer's account data once the agent has read it. In B2B this is a procurement blocker, so ask early.

How should you score an AI support agent RFP?

Score each criterion on a three-point scale (fails, partial, meets) and treat criteria one through five as gates rather than points: a vendor that fails any of them should not advance regardless of total score. Then weight six through ten according to your business case. If the budget was justified on activation or renewal, proactive detection carries the most weight; if it was justified on cost per ticket, measurement and time to live do.

Two practical rules improve the outcome. First, run the same scripted test set against every vendor, using real anonymized tickets from your last quarter rather than the vendor's sample questions, and include at least a third that require account context to answer correctly. Second, insist that every capability that scores "meets" is demonstrated live on your test set, not described in a slide. The gap between the two is where most disappointing deployments originate.

Where does Worknet fit in this evaluation?

Worknet is built for the B2B pattern this checklist describes: it runs as one AI engine inside your product, in Slack and Microsoft Teams, and alongside Salesforce and Zendesk, reads account context at answer time, and can take actions rather than only answer. It is not a widget-first chatbot, and it is not designed for consumer-scale FAQ traffic. Teams configure it in plain language and connect systems through existing APIs or MCP, so the first live use is measured in days rather than a quarter.

On criterion six, Worknet's starting point is proactive: it watches for users who are stuck or failing inside the product and steps in before a ticket exists, which is why we tend to frame it as an AI adoption agent rather than only a support agent. If you are comparing it against autonomous agents built around conversational channels (as of September 2026, Decagon's own site lists chat, voice and email, and Sierra's lists chat, SMS, WhatsApp, email, voice and ChatGPT; neither lists Slack, Teams or in-product surfaces on the pages we checked), our Decagon vs Worknet and Sierra vs Worknet posts walk through the differences by criterion.

One honest note for the pricing line of your RFP: Worknet's pricing is quote-based, as it is for most vendors in this category, so this checklist deliberately makes no claim about cost. Score on the ten criteria first and let pricing conversations follow.

Conclusion

Knowing how to evaluate an AI support agent for B2B SaaS comes down to testing the things a demo hides: whether the agent knows who is asking, whether it can act, where it lives, how it hands off, and whether its resolution numbers survive an independent check. Use the ten criteria as an RFP, treat the first five as gates, and run every vendor against your own tickets. If you want to see how Worknet answers each of the ten on a live account, book a demo and bring your hardest ticket.