What is rendfly

rendfly is production-time monitoring for conversational AI agents — Pingdom for AI agents.

rendfly watches conversational AI agents in production and tells you when they go off-script. Think Pingdom, but instead of checking whether a server responds, it checks whether your agent is still honoring the rules you set for it. When something goes wrong — wrong price quoted, wrong language, wrong policy — you hear about it in minutes, not when a customer screenshots it.

The problem

Infrastructure monitoring sees a healthy agent. The request came back 200. Latency is 240ms. Database queries look fine.

What it doesn’t see: your WhatsApp customer-service agent has been quoting last quarter’s shipping prices for the past two weeks. Every conversation gets a technically successful HTTP response. The content of those responses is wrong, and nobody noticed until a customer posted a screenshot comparing what the bot said to what the checkout page charged.

This is a silent failure — one of the most common modes of production AI regression. The model’s behavior shifts when its provider quietly rolls out a new version, when the knowledge base gets stale, or when someone edits the system message without realizing a rule was load-bearing. Research on LLM hallucination and behavioral drift shows these degradations are frequent and gradual, making them easy to miss without dedicated monitoring.

No alert fires. No dashboard turns red. Sentry stays green. The agent just starts being wrong.

What rendfly does

The core pipeline has three steps:

  1. Extract rules from your system message. When you connect a project, rendfly reads your agent’s system message and extracts the constraints it contains — refusal rules, tone requirements, routing conditions, factual claims — and surfaces them as an editable list in your dashboard.

  2. Judge every production conversation. Each conversation gets scored against the extracted rules by an LLM-as-judge. The judge uses a sandwich pattern that wraps your rules and the conversation in separate tagged blocks, so the judged content can’t manipulate the verdict. Per-rule pass/fail rolls up to a 0–100 aggregate score per conversation.

  3. Alert when behavior drifts. rendfly tracks a rolling 24-hour window of scores against a 7-day baseline. When the delta crosses a configurable threshold (default: 2 standard deviations), an alert fires to your email, Slack, or webhook of choice.

Who it’s for

Indie founders running a single agent — a WhatsApp support bot, a Telegram assistant, a web chat on your product site — who want to know immediately if something goes sideways without hiring a dedicated QA team.

Agencies managing conversational AI for multiple clients, where a misconfigured agent hurts a client relationship and you need multi-tenant visibility across all your projects in one place.

Enterprises with reliability SLAs who need audit trails, custom alert routing, multi-judge consensus to reduce false positives, and eventually SSO and BYOK when their security team gets involved.

What rendfly is not

rendfly is not Sentry or Datadog. Those tools cover infrastructure — latency, error rates, database queries — and they do it well. There’s no overlap. You still want them.

rendfly is also not Braintrust, Helicone, LangSmith, or Promptfoo. Those are dev-time eval frameworks: you run them before deploying a new prompt version to catch regressions in staging. That’s valuable and complementary. rendfly runs after deploy, on the live tail of real user conversations, and watches for behavior that no staging eval could have anticipated.

Updated 2026-05-09