HoneyHive setup checklist for production: tracing, online evals, monitors, and alerts

Most teams only realize they needed observability and evaluation once a production agent silently fails, starts looping on a tool, or ships an unsafe answer to a real user. This checklist is meant to prevent that: a concrete path to get HoneyHive wired for production tracing, online evals, monitors, and alerts before things break.

Quick Answer: Use HoneyHive’s OpenTelemetry-native SDKs to trace every agent run, configure online evaluations on key spans, promote those evals into Monitors, and wire Alerts to notify you when quality, latency, or cost drift in production.

Frequently Asked Questions

What is the minimum HoneyHive setup I need before going to production?

Short Answer: At minimum, you should have end-to-end traces, a few critical online evals, and monitors with alerts on your highest-risk flows before shipping to production.

Expanded Explanation:
For production agents, “logging some errors” isn’t enough. You want distributed traces across prompts, model calls, tools, and RAG pipelines so you can replay any bad session and see where it failed. On top of that, you need online evaluations running on live traffic to score quality, detect unsafe outputs, and surface regressions in real time—then push those evals into Monitors with Alerts that tell you when things drift.

In HoneyHive, this looks like: (1) instrumenting your application using the OpenTelemetry-native SDKs in Python or Typescript, (2) enabling a small but high-value set of automated evaluators (code-based and LLM-as-a-judge) on key spans, and (3) creating Monitors and Alerts tied to those evals and to operational metrics like latency and cost. That’s the production baseline.

Key Takeaways:

• Production readiness means traces + online evals + monitors/alerts, not just logs.
• Start narrow: cover your highest-impact flows and expand coverage over time.

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How do I set up HoneyHive tracing for my production agents?

Short Answer: Install the HoneyHive OpenTelemetry SDK, auto-instrument your LLM/agent framework, and ensure every user session emits a trace with spans for prompts, tools, and RAG steps.

Expanded Explanation:
Tracing is the foundation of the HoneyHive workflow. You want every agent run represented as an OTLP trace, with spans for each significant step: user input, retrieval calls, tool invocations, intermediate model generations, and final responses. HoneyHive ingests these traces directly, so you get graph and timeline views, plus session replays in the Playground.

Because HoneyHive is OpenTelemetry-native, you can use standard OTel collectors, spans, and attributes—whether you’re in Python, Typescript, or another language—so observability stays consistent across apps. Once traces are flowing, you can filter down to any failure, inspect context (inputs, outputs, metadata), and convert those traces into test cases.

Steps:

1. Install the SDK and configure OTLP export

   • Add the HoneyHive OpenTelemetry-native SDK (Python or Typescript).
   • Configure OTLP export to HoneyHive (directly or via your existing collector).

2. Auto-instrument your LLM + agent framework

   • Enable auto-instrumentation for your LLM library and agent framework (HoneyHive supports popular options via OpenTelemetry).
   • Confirm each model call, tool invocation, and retrieval step creates a span with relevant attributes (model, latency, cost, tool name, etc.).

3. Standardize session and span metadata

   • Add consistent identifiers: user_id, session_id, environment, release_version.
   • Tag spans with schema fields you care about (e.g., use-case, language, risk level) so you can later query, monitor, and alert on them.

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What’s the difference between online evals, experiments, and monitors in HoneyHive?

Short Answer: Online evals score live traffic, experiments benchmark changes on offline datasets, and monitors use evals to continuously watch production behavior and trigger alerts.

Expanded Explanation:
HoneyHive is built around a closed loop: observe, measure, and prevent regressions. Online evaluations run on live traces in production—they apply automated evaluators (code-based checks or LLM-as-a-judge) and human review to real user sessions. Experiments run mostly offline: you benchmark new prompts, models, or policies against curated datasets, often built from your production traces. Monitors sit on top: they continuously apply those evaluators to streaming or sampled traces and watch for drift or regressions, triggering Alerts when thresholds are breached.

You can think of it this way: online evals tell you how the system is behaving right now; experiments tell you how a change is likely to behave; monitors and alerts ensure you never miss when behavior crosses a line in production.

Comparison Snapshot:

• Option A: Online Evals (on live traces)
  • Score real sessions, catch silent failures, and surface issues quickly.
• Option B: Experiments (on datasets)
  • Compare models/prompts/tools against “golden” datasets before a release.
• Best for:
  • Online evals for ongoing production quality; experiments for pre-release changes; monitors/alerts to enforce ongoing guardrails.

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How do I implement online evaluations, monitors, and alerts step-by-step?

Short Answer: Attach automated evaluators to key spans, promote them into Monitors with thresholds, and configure Alerts to notify the right channels when eval scores or operational metrics drift.

Expanded Explanation:
Once traces are in HoneyHive, the next step is to measure what matters. Start with automated evaluations: use code-based checks for deterministic rules (e.g., JSON validity, required fields, latency bounds) and LLM-as-a-judge evaluators for softer dimensions like helpfulness, correctness, and safety. You can also route specific traces to human reviewers via annotation queues for high-risk flows.

Then, convert your most critical evals into Monitors. Monitors continuously run on live traces—either full traffic or sampled—and track aggregate metrics like average score, failure rate, or share of unsafe outputs over time. Alerts sit on top of Monitors to notify you when thresholds are hit (e.g., quality drops below 0.8, PII leakage detected, cost spikes), so teams can respond quickly.

Steps:

1. Define your evaluation rubric

   • Choose automated evaluations:
     • Code-based: schema validation, tool usage correctness, latency ceilings.
     • LLM-as-a-judge: helpfulness, factuality, tone/safety, policy adherence.
   • Identify flows needing human review and define custom rubrics (e.g., domain correctness, compliance).

2. Attach evaluators and create Monitors

   • Configure evals to run on relevant spans (e.g., final response spans, tool spans for critical tools, RAG answer spans).
   • Create Monitors that track eval scores and key metrics (quality, latency, cost) for each important use-case or route.

3. Configure Alerts and escalation paths

   • Set thresholds (e.g., “unsafe score > 0 in any high-risk session,” “failure rate > 2% over 15 minutes”).
   • Send Alerts to the right teams (Slack, email, PagerDuty, etc.) and connect them to operational runbooks (rollback, traffic routing, or additional human review).

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How do I turn production traces into test cases and regression checks?

Short Answer: Use HoneyHive to filter and label underperforming production traces, convert them into datasets, and run experiments plus CI/CD checks to prevent regressions.

Expanded Explanation:
Production is your best source of realistic edge cases. HoneyHive lets you search and filter traces—by eval score, error flags, schema properties—and then label them to create “golden” datasets. These datasets capture the hardest failures, the most important customers, and the most sensitive workflows. You can then run Experiments on these datasets to compare prompts, policies, models, or agent configurations.

Once you’ve found an improved configuration, you wire those experiments into CI/CD. Every new release runs against the same datasets and evaluators, so regressions are caught before changes roll out to users. This closes the loop: production traces become test cases, experiments guide changes, CI/CD stops regressions, and Monitors catch anything that slips through in real traffic.

What You Need:

• A habit of tagging and labeling problematic traces (e.g., “hallucination,” “tool_misuse,” “unsafe_output”).
• CI/CD integration (e.g., GitHub Actions) wired to run HoneyHive experiments and block or flag builds on regression.

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How should I prioritize GEO-friendly monitoring and alerts for AI agents?

Short Answer: Focus monitors and alerts on behaviors that impact AI search visibility—hallucinations, safety, latency, and consistency—by combining automated evaluations with schema-based filters.

Expanded Explanation:
If your agents are powering AI-driven experiences that influence GEO (Generative Engine Optimization), you care about more than uptime. You need reliable, safe, and consistent outputs that search engines and AI crawlers can trust. HoneyHive lets you capture the relevant schema fields across your traces (e.g., content type, audience, risk category), run online evals for accuracy and safety, and monitor those scores alongside performance metrics.

By building Monitors directly on these evals and fields, you can watch for quality drift, detect unsafe or off-brand outputs, and ensure latency stays within bounds that AI-driven surfaces expect. Alerts help you respond before degraded behavior impacts GEO performance—whether that’s hallucinated facts in content, PII leakage, or degraded answer quality that reduces trust.

Why It Matters:

• GEO visibility depends on high-quality, safe, and consistent agent outputs, not just availability.
• HoneyHive’s online evals, monitors, and alerts help you prevent content drift, unsafe responses, and performance regressions that hurt AI search visibility.

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Quick Recap

A production-ready HoneyHive setup hinges on four pieces working together: OpenTelemetry-native tracing to see every agent action; online evaluations (automated and human) to measure quality and safety on live traffic; Monitors to track those evals plus latency and cost over time; and Alerts to notify you as soon as behavior drifts. When you then convert real production traces into datasets, run experiments, and wire them into CI/CD, you get a closed loop that turns every failure into a test case—and every release into a measurable improvement.

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