Energent AI

AI that audits AI — catching hallucinations before you ship them.

2.8Score#388 of 999 agents#2 of 5 in Data analyst

Why it ranks #388

Energent AI, an AI data analyst from YC S23, ranks #388 of 999 AI agents and #2 of 5 in Data analyst, with a score of 2.8 of 10. Data as of 3 Oct 2026.

The score adds four parts, each 0 to 10, by weight. Every point comes from public evidence. How scoring works ·

Proof 0 × 30% 0.0
Scale 4 × 30% 1.2
Momentum 3 × 25% 0.8
Autonomy 6 × 15% 0.9
= 2.8

Proof
Do customers use it? 30% of the score
0
No public claims of customers or revenue yet.
Not found: revenue or growth (+4), named customers (+3), a customer result (+2), a customer count (+2), work done at scale (+2)
Scale
Is there a real company behind it? 30% of the score
4
10 people.
- +4
  10 people on its YC page10 to 19 people
Not found: funding round in its news (+3)
Momentum
Is it shipping and growing now? 25% of the score
3
Shows a recent launch.
- +3
  Launched in the last 6 monthsEnergent.ai: Audit — The Fact-Checker for Every AI-Generated ResultLaunch YC post · 8 Jul 2026
Not found: open roles on YC (+2), new traction claim in the last 6 months (+2), funding news in the last year (+2), a batch in the last year (it is YC Summer 2023) (+1)
Autonomy
How much of the job does it do on its own? 15% of the score
6
Supervised agent: takes actions in other systems; a person approves key steps.
- +6
  Level 2 of 4: Supervised agentTakes actions in other systems; a person approves key steps.
What the model read
- Acts in other systems: 26% likely
- Finishes whole tasks: 29% likely
- Calls itself an agent: 98% likely
- Level 1 from the model's reading of its pages; the evidence review found, with quotes, that its AI acts in the work, so the level is 2
Level 2: Takes actions in other systems; a person approves key steps.

About Energent AI

Every AI tool will confidently hand you a wrong number — and in engineering and finance, a wrong number is expensive.

Energent.ai is an AI teammate that does your data work on a secure desktop: reading documents and engineering drawings, cleaning messy data, writing code, building charts. Then a second, independent agent audits that output against your original source files and flags every claim that doesn't hold up — catching roughly 3x more hallucinations than the model alone in our internal evals.

Engineering teams check drawing sets before issue. Finance teams tie numbers back to source. The best teams encode their own checking standards as reusable rules, so it checks the way their firm checks.