Here’s a concise guide to AI‑agent observability platforms — what they do, the leading players, and how to choose one.

What “agent observability” covers

• Trace-level logging of agent activity: prompts, tool calls, external API requests, DB/FS access, and returned outputs.
• Correlation of semantic (LLM prompts/decisions) and system-level telemetry (latency, errors, CPU/GPU, network).
• Replayable traces and session replays to reproduce multi-step reasoning and tool use.
• Evaluation and automated “evals” to measure correctness, hallucination, grounding, and downstream user outcomes.
• Alerting, anomaly detection, and slice/drift analysis for agent behavior and data inputs.
• Privacy/PII controls, retention policies, and integrations with governance/audit tooling. (medium.com)

Notable platforms and what they focus on

• Langfuse — purpose-built LLM/agent tracing and analytics; popular for self-hosting and engineering control, with prompt/versioning and trace visualizations. Good for teams that want an ops-first, privacy-controlled stack. (Langfuse.com)
• Maxim AI — end-to-end platform that combines simulation, evaluation, and observability specifically for agentic apps (agent simulation, multi‑step trace analytics, evals + production monitoring). Good when you want unified experimentation → production flow. (getmaxim.ai)
• Weights & Biases (Weave + W&B) — long-running ML experiment tracking that has added agent/trace integrations (Weave) to capture MCP/agent interactions and correlate with model metrics and experiments. Good for teams that already use W&B for training/experiments. (wandb.ai)
• Arize (and Arize Phoenix) — ML observability expanded toward agentic systems: drift/slice analysis, evaluation, and production diagnostics for LLMs and agent workflows. Strong on data diagnostics and model-quality signals. (getmaxim.ai)
• Enterprise observability vendors (Datadog, Dynatrace, APM vendors) — provide infra/APM/tracing context (OpenTelemetry) and are extending to include LLM token metrics, request tracing, and correlated telemetry across microservices. Best when you need a single-pane-of-glass for whole stack observability. (medium.com)

Emerging & research approaches

• eBPF / system-level correlation (AgentSight-style): research projects propose correlating TLS/LLM intent with kernel/system events to bridge semantic and system views; useful for high-security or platform-level observability. (research/prototype). (arxiv.org)
• Standard toolkits & protocols: OpenTelemetry for tracing, MCP (multi-component protocol) or other agent message formats to make traces portable between tools. (medium.com)

How to choose (quick checklist)

1. Scope: Do you need agent-specific traces (prompt/tool calls/replays) or just model metrics + infra telemetry?
2. Data control: Must you self-host for compliance? Langfuse and self-host options are strong here. (Langfuse.com)
3. Unified lifecycle: Want sim/eval → production continuity? Look at platforms with built-in simulation and evals (Maxim, Arize). (getmaxim.ai)
4. Integrations: Check support for your model providers (OpenAI, Anthropic, Bedrock, local LLMs), vector DBs, and APM (Datadog/Grafana). (Langfuse.com)
5. Alerting & automation: Can it run automated evals, slice alerts, or trigger rollbacks/flagging? Essential for production agents. (medium.com)
6. Cost & retention: Token-level accounting and trace retention add up — verify pricing for high-volume agents. (medium.com)

Recommended patterns for agent observability

• Instrument the agent runtime to emit structured traces for each step (prompt in, model outputs, each tool call and response, side effects). Export via OpenTelemetry or the vendor SDK. (medium.com)
• Store both semantic artifacts (prompts, outputs, eval scores) and telemetry (latency, system metrics) together so you can slice by user cohort, prompt template, or tool call. (medium.com)
• Run automated evals and human-in-the-loop review on sampled traces to detect hallucination, safety violations, and regressions. (getmaxim.ai)
• Combine agent observability with FinOps: track token usage per flow/agent to optimize cost. (medium.com)

If you want

• A short vendor comparison (features, hosted vs self-hosted, best use case) — I can produce a 1‑page comparison table.
• Help selecting/instrumenting an SDK (OpenTelemetry vs vendor SDK) for your agent runtime — tell me your stack (LangChain/Strands/Autogen, cloud provider, self-hosted LLMs) and I’ll give sample instrumentation steps.

Sources and further reading

• Langfuse blog on agent frameworks and observability. (Langfuse.com)
• Maxim AI coverage and product pages (agent simulation + observability). (getmaxim.ai)
• Weights & Biases Weave agent tracing docs. (wandb.ai)
• Industry writeups on tracing and observability for LLM/agents. (medium.com)
• Research on system-level agent observability (AgentSight). (arxiv.org)

Would you like a tailored recommendation for your stack (examples: LangChain/Autogen/Strands + OpenAI/Anthropic/Bedrock + AWS/GCP)? I can map specific integration steps and sample code.