AI agent observability and monitoring

What AI agent observability actually does

Observability is the practice of instrumenting an agent so you can answer, after the fact, what it did and why. A trace captures every LLM call, tool invocation, retrieval, token count and latency in a span tree you can replay. Evals score the quality of those outputs against datasets or LLM-as-judge graders. This is real, useful engineering work: without it you are debugging a black box. The category is honest about its scope, it observes, traces, debugs and evaluates. It is the layer that makes agent behaviour measurable and comparable.

• Tracing: span-by-span record of LLM calls, tool calls, retrievals, token usage and latency.
• Evals: offline datasets plus online LLM-as-judge scoring of output quality.
• Cost and usage analytics: per-request, per-session and per-user spend breakdowns.
• Debugging: transcript views and trace replay to reconstruct exactly what happened.

The real tools, honestly

The 2026 landscape is mature and the tools are good. Pick the one that matches your workflow rather than chasing a single winner. These are the tools for AI agent observability debugging Langfuse Helicone OpenTelemetry agents reliability that most teams actually reach for, and the AI agent orchestration monitoring evaluation tools 2026 conversation usually starts here.

• Langfuse: self-hostable, strong on prompt versioning, evals and structured logging; v4 moved to OpenTelemetry-based tracing.
• Helicone: drop-in proxy, change one base URL and get traces, costs, errors and session-level workflows with no SDK changes.
• OpenTelemetry (OTel) GenAI semantic conventions: the emerging vendor-neutral standard for prompts, tool calls, token usage and agent steps; adopted by Datadog, Grafana and major frameworks.
• AgentOps: multi-framework agent debugging and session replay across orchestration stacks.
• Laminar: optimised for long-running agents, with transcript view and signals for hard-to-reproduce failures.
• MLflow: open-source tracing, prompt versioning, automated evaluation and trace replay in one platform.
• Framework-native tracing and evals: LangSmith (deep with LangGraph, works with most SDKs via @traceable), plus OpenAI Agents SDK tracing evals tools 2026 built into the SDK itself.

Where observability stops: it sees, it does not save

Here is the honest boundary. Observability tells you when and why an agent broke. It does not keep the agent alive. When a long-running agent crashes at 3am, the trace will faithfully record the out-of-memory error, the failed tool call, or the unhandled exception, and then nothing happens. The process is dead. No span restarts it, restores the working files it had open, or reloads its integrations. Monitoring is the smoke detector. It is essential, and it is not the fire department. For long-running agents that are supposed to run for hours or days, you need something that acts on the failure, not just records it.

Where Molted fits: the recovery layer

Molted is a managed runtime for long-running autonomous agents (OpenClaw today, Hermes on request). It is the layer that does the recovering. A daemon runs alongside your agent and survives the agent dying: crashes are caught in under 60 seconds and the agent is back under 90 seconds, automatically. On top of that sits 4-tier self-healing, a versioned S3-backed filesystem with point-in-time restore so a recovered agent picks up where it left off, a RAM semaphore for safe high density, and a post-mortem written on every failure. You also get a real-time view of crashes, recoveries and RAM pressure. Observability answers why; Molted handles what happens next.

• Daemon survives the agent process dying: crash caught under 60s, agent back under 90s.
• 4-tier self-healing instead of a dead process waiting for a human.
• Versioned, S3-backed filesystem with point-in-time restore so state is not lost on recovery.
• RAM semaphore for safe high density, plus a post-mortem on every failure.
• 1,000+ integrations via a managed integration layer, reloaded automatically after recovery.

Use both: see with observability, recover with a runtime

These layers are complementary, not competing, and you do not have to choose. Run Molted as the runtime so your agents stay alive, and keep sending traces to your observability tool of choice, Langfuse, Helicone, MLflow, AgentOps, Laminar, or anything that speaks OpenTelemetry. Use the observability tool to debug behaviour and grade output quality; let Molted handle the operational reliability, restarts, state and RAM, that no trace can do on its own. The same team operates molted.cloud for 300+ clients, so the recovery layer is proven at scale (molted.net is the canary surface).

• Observability layer: trace, debug, eval, measure output quality.
• Runtime layer (Molted): catch crashes, restore state, reload integrations, keep agents alive.
• Wire them together: Molted as runtime, your OpenTelemetry-compatible tool as the trace sink.