Short primer + quick vendor list, what to compare, and a hands-on checklist.

What "AI agent observability" means (short)

• Observability for agentic/LLM systems captures structured traces of multi-step interactions (sessions → traces → spans), inputs/outputs (prompts, tool calls, retrievals, embeddings), quality signals (evaluations, human labels), and infra/ cost/latency metrics so you can detect hallucinations, prompt-injection, coordination failures and root‑cause multi-agent bugs. (arize.com, fiddler.ai)

Notable platforms and OSS you should evaluate (quick)

• Arize — full LLM & agent observability + evaluation tooling (tracing, online evals, dashboards). Good for enterprise ML teams. (arize.com)
• LangSmith (LangChain) — tracing, prompt/playground, evals; native OpenTelemetry ingestion for LangChain/LangGraph apps. Good if you use LangChain. (langchain.com, blog.langchain.com)
• Fiddler — positions itself for "agentic" or multi‑agent observability with hierarchical session→agent→span views and guardrails. (fiddler.ai)
• Datadog — LLM/agent tracing integrated with APM and infra telemetry for unified debugging and alerts. (datadoghq.com)
• WhyLabs (openLLMtelemetry / Optimize) — focuses on universal telemetry + guardrails and integrates OpenTelemetry conventions for LLMs. (docs.whylabs.ai)
• Honeycomb, SigNoz and other observability backends — can be used as OTEL backends for LLM traces or for low‑cost/high‑cardinality debugging. SigNoz (and similar OSS) plus OpenTelemetry are common building blocks. (honeycomb.io, signoz.io)

Key features to compare

• Tracing model: session → trace → spans, ability to inspect intermediate reasoning steps and tool calls. (arize.com)
• Evaluation & closed‑loop: LLM-as-judge evals, human labeling, batch vs online evaluation and auto‑retraining hooks. (arize.com, langchain.com)
• Guardrails & safety: content policies, prompt‑injection detection, automated blocking or rerouting. (docs.whylabs.ai, fiddler.ai)
• Open standards & integrations: OpenTelemetry / OpenInference / OpenLLMTelemetry support (vendor‑neutral instrumentation is increasingly important). (blog.langchain.com, arize.com, docs.whylabs.ai)
• Data residency / self‑host options: important for PII, HIPAA, regulated industries. Many vendors offer VPC/self‑hosted deployment. (langchain.com, fiddler.ai)
• Cost & scale: token logging and full-text traces can be large — look for sampling, redact/PII strategies, and cost controls. (datadoghq.com)

Telemetry schema (what to capture — minimal recommended attributes)

• identifiers: session_id, trace_id, span_id, user_id (hashed/pseudonymized)
• LLM inputs/outputs: prompt text (or redacted), system messages, model name & version, tokens_in/out, token costs
• Retrieval & tools: retrieval query, retrieved doc ids/snippets (or hashed), tool_name, tool_args, tool_response, success/fail flags
• Observability metrics: latency, error_code, CPU/memory for hosted model, infra spans (DB, API)
• Quality signals: auto‑eval scores, human label, satisfaction, hallucination flag, safety policy violations
• Metadata: timestamp, environment (dev/stage/prod), deployment tag, experiment id

Implementation pattern (practical)

1. Instrument with OpenTelemetry or the vendor SDK (LangSmith/Arize/WhyLabs all support OTEL or vendor SDKs). Start by sending traces for every user session and tool call. (blog.langchain.com, arize.com)
2. Redact or hash PII at ingest and keep raw text in a separate, access‑controlled store only when necessary for debugging (and with audit). (Privacy best practice; vendors support VPC/self‑host). (fiddler.ai, langchain.com)
3. Define SLOs/monitors: latency, token‑cost per session, tool‑call correctness, eval pass rate; set alerting & automated rollback rules. (aws.amazon.com, datadoghq.com)
4. Deploy sampling + full‑trace capture for failed sessions: sample healthy traffic but capture full traces for errors or threshold breaches to control volume/cost.
5. Close the loop: use production traces to create evaluation datasets and automated retraining or prompt fixes. (arize.com)

Risks & compliance (short)

• Sensitive data leakage (store/review prompts carefully). Use redaction, VPC/self‑host, RBAC, and audit logs. (fiddler.ai)
• Over‑logging costs & SLO noise — use sampling and meaningful aggregated metrics. (datadoghq.com)

How to pick (simple rubric)

• If you already use LangChain: trial LangSmith first (tight integration + OTEL). (langchain.com)
• If you need enterprise evaluation + built‑in model‑ops: evaluate Arize. (arize.com)
• If you need multi‑agent/multi‑span hierarchical debugging and guardrails: try Fiddler and WhyLabs (guardrails). (fiddler.ai, docs.whylabs.ai)
• If you want to integrate LLM traces into existing APM/infra: Datadog or Honeycomb as they tie neatly to infra/APM telemetry. (datadoghq.com, honeycomb.io)
• If you prefer OSS or want to avoid vendor lock‑in: instrument with OpenTelemetry/OpenInference and use SigNoz or self‑hosted collectors as a first step. (signoz.io, arize.com)

Quick next steps (30–90 day pilot)

• Week 0–2: pick 1–2 pilot traces (critical agent flows), decide sampling and PII rules.
• Week 2–6: add OpenTelemetry or vendor SDK, send traces to your chosen backend, capture tool calls & retrievals. (blog.langchain.com, arize.com)
• Week 6–12: create 3–5 monitors/evals (hallucination, tool misuse, latency), run incident drills, iterate on prompts/agents. (arize.com)

If you want, I can:

• produce a one‑page comparison table of the vendors above (features, pricing model, self‑host support), or
• generate an OpenTelemetry trace schema + example instrumentation snippet for your agent stack (LangChain, custom agent, or browser‑based).

Which of those would be most useful?