The Right Way to Evaluate an AI Agent for Technical Support
Before an AI agent talks to your customers, how do you know it’s ready? Join our webinar to learn the method our engineers use to test agents on real questions, grade every answer, and keep them accurate after launch.
- (45 minutes)
- Free · Online via LinkedIn
With Vasanth Asokan, Ashikka Gupta and Jun Song
What will you take away from the session?
- 01
How do you know an AI agent is ready for your customers?
Build a test from your hardest real questions and grade every answer, so you launch on evidence instead of a hunch.
- 02
Why general-purpose AI models fall short on technical product questions
Without verified product data to draw on, a model fills the gaps with guesses. We show what that looks like on real spec questions.
- 03
What happens after you deploy an agent to production
Products, documents and questions keep changing. Learn how to keep checking answers so accuracy holds after launch.
- 04
Five questions to ask any AI vendor, including us
A short list you can take into your next vendor call, and what good evidence looks like for each question.
Who is speaking?
-
Vasanth Asokan
CTO and co-founder, Rapidflare
Writes about measuring what good looks like for AI agents in electronics.
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Ashikka Gupta
Founding Engineer, Rapidflare
Builds the systems Rapidflare uses to test its agents.
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Jun Song
Founding Engineer, Rapidflare
Co-author of Rapidbench and of our field notes on proving accuracy without an answer key.
Five questions to ask any AI vendor
- 1Did you test your agent on our questions?
- 2How do you grade an answer, and on which dimensions?
- 3How does your agent do against leading AI agents on our questions?
- 4Is your agent grounded in our product data?
- 5What happens when our products or docs change?
We’ll show you what good evidence looks like for each one.
Already looking at an AI agent?
Share one benchmark, accuracy claim or vendor evaluation. We’ll tell you what it proves for your use case and what to test next. Free, response within one business day.
Go deeper before the webinar
-
Evals First, Agents Second: A Field Guide for Enterprises
Why evaluation comes before the agent, and how we define what good looks like for product intelligence.
Vasanth Asokan Read → -
A Practical Framework for Evaluating AI Agents
The framework we use to test our agents, catch regressions, and ship with confidence.
Ashikka Gupta and Sathvik Murthy Read → -
Proving It: Evaluating Enterprise AI Without a Trusted Answer Key
What to do when a customer asks for proof of accuracy and there is no trusted answer key.
Jun Song and Jahnavi Singh Read → -
Your LLM Judge Is Just One More Model to Test
How we tune LLM judges so the scores are something you can trust.
Sathvik Murthy and Ashikka Gupta Read → -
Accuracy: The Key to Effective AI-Powered Technical Sales
Why precision and clarity are the basis of trust in technical sales, and how to build for them.
Vasanth Asokan Read → -
Rapidbench: benchmark results
116 real electronics product queries, scored across models with and without verified product data.
Ashikka Gupta, Jun Song and Sathvik Murthy Read → -
Build vs. buy: AI for technical sales
A decision framework and scorecard for building or buying AI for technical sales.
Team Rapidflare Read →
Webinar and AI agent evaluation questions
Who is this webinar for?
It is for technical support, sales engineering, product and AI leaders who are evaluating or running an AI agent. Our focus is electronics, semiconductor, industrial automation and physical security companies, where answers depend on detailed product data. If a wrong spec, part number or warranty answer would cost you a customer, this session is for you.
Is the webinar free?
Yes, the webinar is free. You register through the LinkedIn Event, and there is nothing to buy. Registering also means you receive the replay and the Rapidbench report by email afterward, so you can still catch up if the time does not work for you.
Will there be a recording?
Yes, every registrant receives the replay by email. The email also includes the Rapidbench report, which shows how the same AI models score with and without verified product data. Register even if you cannot attend live, so the recording and the report land in your inbox after the session.
How many questions do you need to evaluate an AI agent?
Start with 20 to 50 of your hardest real questions. A small set of hard questions tells you more than a thousand easy ones, because easy questions pass on almost any agent. Pull them from your support queue and sales calls, include part numbers and table lookups, and add a question every time the agent gets one wrong.
How do you grade an AI agent’s answer?
Check four things: is the answer correct, complete and clear, and does it escalate when needed. An answer must pass all four. A correct answer that lists three of seven matching products still fails on completeness, and a confident guess on a warranty question fails on escalation.
Why isn’t a general-purpose AI model enough for technical product questions?
Accuracy depends on the product data the model can reach, not only on the model. In Rapidbench, the same models scored 34 to 39 points higher when grounded in verified product data than when using web search. Without your current documentation, a model has to guess at specs, and customers act on those guesses.
Can Rapidflare evaluate an agent we’re already considering?
Yes. Share one benchmark, accuracy claim or vendor evaluation through our free review page, and we will tell you what it proves for your use case and what to test next. We reply within one business day, with no obligation. The review covers any agent, not only ours.
Nov 12 · Free webinar
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