Skip to content
Sundar Shahi ThakuriSundar Shahi.
Book a call

free_tool

Is your AI agent actually production-ready?

The model is rarely the problem; the loop around it is. Eight plain questions across the things that break agents in production: termination, escalation, tool-output integrity, idempotency, context, cost, and observability. Get a score, a per-dimension breakdown, and the two or three fixes worth doing first. About two minutes.

Question 1 / 80%
What stops your agent from looping forever?

how_scoring_works

How the score is built

Each answer carries weighted points, from "we trust the model to decide" up to a tested, guarded setup. We sum them and express your score as a percentage of the maximum, then map that to a band. Every dimension carries equal weight, so one strong area can't paper over a weak one.

It's a fast self-assessment, not an audit. It surfaces where the loop is most likely to break first, which is exactly where a real review would start.

0–44%At riskReal gaps between a demo and a dependable agent: runaway loops, injected instructions, side effects fired twice.
45–77%Getting thereThe core loop is sound; the risk now lives in the edges you haven't tested.
78–100%Production-readyDisciplined: it stops when it should, fails safely, and you can see and test what it does.

dimensions

What the assessment looks at:

  • Termination & loop caps
  • Escalation & failure handling
  • Tool-output integrity
  • Idempotency & side effects
  • Context management
  • Cost & rate control
  • Observability & evals

See how I can help →

faq

Questions & answers

What does the AI Agent Reliability Scorecard assess?
It scores whether an agent loop is production-ready across seven disciplines: termination and loop caps, escalation and failure handling, tool-output integrity, idempotency and side effects, context management, cost and rate control, and observability and evals. You answer eight weighted questions and get a banded score.
How does the scoring work?
Each answer is worth 0 to 3 points, summed as a percentage of the maximum from the questions you answered. It bands the result into at risk below 45%, getting there from 45 to 77%, and production-ready at 78% and above, and it surfaces the lowest dimensions with fixes.
How does it think about prompt injection?
The tool-output integrity question treats anything the model reads back, like web pages, emails and API responses, as untrusted. It rewards moving up a ladder from raw appending toward a sanitization layer that strips injection patterns before the content reaches the model.
Does it test my agent's real code?
No. It scores your self-assessment and does not run your agent, inspect code, or read production logs. It is a quick gut check of your loop engineering, not a formal audit.
Is anything I enter sent to a server?
The questions and scoring run in your browser, so your answers stay local. Nothing is transmitted unless you submit the optional lead form, which sends your email and score so someone can follow up.

Want your agent loop reviewed?

I'll go through what this scorecard surfaced and tell you where your loop breaks first. Book a call, or leave your email and I'll reach out.

Book a call

No spam. You'll get a reply from me.

Prefer proof first? See how this plays out in real case studies →