The 2026 Guide to Agent Observability Tools

The first sign of trouble usually isn’t a red alert, it’s a confused customer. By the time someone’s asking why their invoice tripled, your agent has already pulled the data, called the tools, walked through the task, and handed back an answer that nobody checked. Agents run on their own, that’s the whole point, but running on their own also means failing on their own. Agent observability is about being the one who finds out first.

Agent observability tools watch your AI agents in production and tell you what they’re really doing: the data they pull in, how fast and how expensively they run, the paths they take through a task, and whether their answers are accurate, relevant, and safe.

And teams have clearly gotten the memo. In Monte Carlo’s survey of enterprises building agents in production, 73% said they won’t ship an agent without monitoring and alerting in place. Which is a good instinct, because 53% also expect to significantly redesign agents they’ve already deployed. Agents aren’t something you launch and walk away from. They’re something you keep watching, keep fixing, and keep rebuilding, and you can’t do any of that without seeing what’s actually happening.

Which brings us to the tools. There are dozens of agent observability tools now, and they don’t all do the same thing. Some trace what happened, some score how good it was, some sit in front of your models like a toll booth, and some are big platforms with an AI button bolted on. Several got acquired in 2026, too, so today’s independent tool may belong to a platform vendor tomorrow. Here’s the field, grouped by what each one is built to do, and where the data underneath it all fits in.

How to think about choosing

Before you start comparing features, figure out which question you’re trying to answer. Agent observability tools cluster into five groups, and each one lines up with a different question:

• What did my agent just do? → Tracing Tools
• Was the output any good? → Evaluation Tools
• How do I route and keep an eye on all this traffic? → Gateways
• Can one platform handle my old-school ML and my GenAI? → Hybrid Platforms
• Can I just run this alongside the infra monitoring I already have? → Broad Platforms

The filters that matter

Once you know your question, a few filters will sort the agent observability tools that fit from the ones that don’t:

Tracing and Runtime Observability

These are the agent observability tools that nail one question: what did the agent just do? You wire up your app, either with the tool’s own SDK or through OpenTelemetry, and it captures a trace of every step. Each individual step, like an LLM call, a tool call, or a retrieval, gets recorded as a “span,” with the prompts, responses, latency, token counts, and cost stapled on. String the spans together and you get the trace: the full story of one run, start to finish. Most have since added prompt management and some evaluation, so the edges get fuzzy, but tracing is still the heart of the group.

Langfuse: The most widely adopted open-source option

Langfuse is often the first of the agent observability tools that teams reach for. The core is open source under MIT, and the numbers back up the popularity: around 20,000 GitHub stars, more than 26 million SDK installs a month, and over 6 million Docker pulls. It came out of Y Combinator’s W23 batch and is trusted by 19 of the Fortune 50 and 63 of the Fortune 500.

It doesn’t stop at tracing, either. The full stack lives in one place:

• Hierarchical tracing that makes multi-step agent runs easy to read.
• Prompt management with versioning — roll back a bad prompt without redeploying.
• LLM-as-a-judge evals against live traffic or saved datasets, experiments for replaying real inputs against a new model or prompt.
• A playground for testing.
• A native MCP server, letting your coding agent pull production traces on its own.

Under the hood it runs Postgres for the transactional side and ClickHouse for heavy analytical queries, a combo that keeps it snappy at trace volumes that would crush a single Postgres instance. Self-hosting is also very straightforward with only a few minutes of Docker.

One thing to weigh before you commit: ClickHouse acquired Langfuse on January 16, 2026, announced alongside ClickHouse’s $400M Series D at a $15B valuation. But the software is still MIT-licensed and self-hostable, so if the roadmap ever looks dire, you can keep running the version you have or switch to a fork.

Best for: the widest-adopted open-source option, a real path to self-hosting, and one tool covering tracing, prompts, and evals.

LangSmith: The default if you live inside LangChain

The tracing option built by the LangChain team, and if your app runs on LangChain or LangGraph, you can flip on tracing with only one environment variable. Every chain, tool call, and retriever step shows up correctly nested from the start, because the framework knows its own shape. It’s not locked to LangChain either, with OpenTelemetry support and SDKs for Python, TypeScript, Go, and Java.

It is best used in the loop between prod and dev. You spot a bad trace in production, click it into a dataset, tweak the prompt in the playground, rerun, and see whether you fixed it or just moved the problem. That flow is tight, and it’s why teams stay even after they outgrow the framework. In 2026 it added LangSmith Fleet for deploying and managing agents, a unified cost view, and an “Insights Agent” that sniffs out recurring failure patterns instead of making you hunt for them.

It’s commercial and closed source, though. The free Developer tier gives you 5,000 traces a month, which will disappear fast in production. Plus runs $39 per seat, and self-hosting is gated behind Enterprise.

Best for: LangChain and LangGraph shops who want zero-config tracing and a tight debug loop, and don’t mind a closed-source, per-seat vendor.

Laminar: Built for agents, and fast

Laminar is what you get when someone builds tracing starting from “agents” instead of “LLM calls.” It’s open source under Apache 2.0, written in Rust, and from Y Combinator’s S24 batch. The Rust part isn’t just a flex: it means ingestion keeps up without breaking a sweat, even when a single agent run throws off thousands of spans, so tracing never becomes the thing slowing your app down. And because it’s OpenTelemetry-native and deduplicates content, the company claims storage runs roughly 20 times cheaper than logging every prompt in full.

And the rest of the standout features revolve around easy debugging too:

• Browser-session recordings synced to your traces: watch what the agent saw next to what it did.
• Chat with your trace in plain English and ask “why did it call the refund tool twice?” instead of scrolling a thousand-span log.
• A step-level debugger that replays from the exact moment a run went bad — no re-running the thirty-minute job.
• “Signals,” where you describe a problem in natural language once and get pinged every time it recurs.

Maturity is the sticking point. It’s younger and smaller than Langfuse or LangSmith, with a thinner community and fewer enterprise-level features.

Best for: long, messy agents (especially browser agents), where trace-storage cost bites and you want debugging that feels agent-shaped rather than log-shaped.

Langtrace: The standards-first, vendor-neutral option

If lock-in keeps you up at night, Langtrace was built for your anxiety. It’s open source, with the app under AGPL-3.0 and the SDKs under Apache 2.0. That split matters: the SDK license is the one your code touches, so Apache 2.0 there means you can instrument a commercial app without being impacted by the AGPL copyleft.

The philosophy is standards-first, top to bottom. Langtrace leans into OpenTelemetry harder than almost any of the other agent observability tools here: its traces are plain OTel spans following the GenAI semantic conventions with nothing proprietary about them. So you can pipe them into Grafana, Datadog, Elastic, or Signoz today, then switch backends next year without changing a line of instrumentation. And this isn’t just marketing talk, either. The Langtrace team helped shape those very conventions and contributed the official OpenAI instrumentation upstream.

The rest is capable if unflashy: a Postgres-plus-ClickHouse stack, more than 30 integrations (LangChain, LlamaIndex, OpenAI, Anthropic, Pinecone, ChromaDB), live dashboards for cost, latency, and token usage, and self-hosting. What you won’t get is deep evaluation tooling or slick agent replay.

Best for: anyone who needs their telemetry to outlive their vendor, and who’d rather bolt evaluation on separately than accept a proprietary SDK.

AgentOps: The most framework-agnostic of the bunch

Most agent observability tools have a framework they secretly prefer. AgentOps doesn’t. It’s an open-source Python SDK with over 400 integrations, covering basically everything. You wire it up with a single decorator, and it runs inside your own infrastructure.

Its agent-native roots show in what it tracks, which is the specific ways agents fail:

• Session replay with “time-travel” — rewind a run and step through the decisions in order.
• Tool-use and reasoning-loop tracking, which reveals not just that the agent called a tool, but how it talked itself into it.
• Infinite-loop detection to stop agents from re-reading the same document forty times.
• Prompt-injection detection and PII redaction, for a bit of safety built into the tracing layer.

It is Python-only, and the evaluation side is lighter than the eval-first tools below.

Best for: Python shops juggling multiple agent frameworks who want one instrumentation layer, cheap setup, and some basic guardrails.

Evaluation-first Tools

These agent observability tools are less about watching live traffic and more about answering “is the output any good?” Instead of just recording what happened, these tools score quality against curated datasets in development and against real traffic in production. Most of them also do tracing and monitoring now, but evaluation is the core product. The extra things to check here: whether non-engineers can review and score outputs, how cleanly evals gate CI/CD, and whether production failures loop back into your test datasets.

Braintrust: The well-funded, eval-first heavyweight

Braintrust is the one you’ll hear about from AI-native startups shopping for agent observability tools. It’s commercial and closed source, founded in 2023 by Ankur Goyal (who previously founded Impira, acquired by Figma), and it pulled in an $80M Series B in February 2026, led by ICONIQ at an $800M valuation, with a16z and Greylock returning. The customer list is a who’s-who of companies shipping AI: Notion, Replit, Cloudflare, Ramp, and Dropbox.

Evaluation here is meant to feel like writing a unit test: define what “good” looks like, run it against a dataset, and get a score you can compare across prompt, model, and code changes. It ships dozens of built-in scorers, extendable by describing what you want in plain language.

Around that sit a few pieces worth a mention:

• An AI proxy that logs requests automatically, making tracing almost free.
• “Brainstore,” a database built from scratch for the job, which the company claims queries complex AI traces roughly 80% faster. It’s why the UI stays responsive even across millions of spans.
• Sturdy CI/CD gating: a prompt change that tanks your factuality score fails the build before it ever ships.
• A free tier that’s hard to argue with — 1M spans and 10,000 eval runs a month.

It’s closed source, with no self-host in the lower tiers, so your prompts live on someone else’s infrastructure.

Best for: product-led teams who want evaluation to feel like testing, and who value polish and speed over open source and self-hosting.

Confident AI / DeepEval: An open-source framework with a platform on top

This is really two products. DeepEval is the open-source piece. It is basically Pytest but for LLMs. You write test cases, assert on them, and then run them in CI. If you’ve written a Python test, the mental model transfers instantly. It’s local-first, has over 250 contributors, and ships more than 50 metrics covering G-Eval, hallucination, RAG scores (faithfulness, contextual precision and recall), agentic behavior, tool-use correctness, safety, and multimodal. The metric breadth is the selling point, including agent-specific checks like whether the agent picked the right tool.

Confident AI is the commercial layer that turns this framework into a platform with tracing, production monitoring, dataset management, human annotation, and no-code workflows so PMs and QA can review outputs without opening a terminal.

This is the usual open-core bargain. The free framework is excellent, but the collaboration and annotation features that make it a team tool live in the commercial platform.

Best for: engineering-led orgs that want evaluation as code, the deepest metric library on this list, and an upgrade path when non-engineers need in.

Maxim: End-to-end, with simulation as the hook

Maxim is a commercial platform spanning experimentation, evaluation, and observability, with a no-code UI aimed at product teams. It was founded in 2023 by Vaibhavi Gangwar (ex-Google) and Akshay Deo (ex-Postman and Slack), with a $3M seed led by Elevation Capital and angels including the founders of Postman, Razorpay, and Groww.

Maxim lets you generate hundreds of scenarios and personas and turn them loose on your agent before it meets a real customer. Most agent observability tools score against a fixed dataset; this one manufactures the traffic.

Around it sit the pieces that close the loop: a “Data Engine” that turns production failures into eval datasets automatically, its own LLM gateway, and SDKs in Python, TypeScript, Java, and Go — broader language coverage than most of this group. The enterprise checklist is covered too, with SOC 2, ISO, and HIPAA compliance plus in-VPC deployment.

Size is the thing to sit with. It’s a smaller company than Braintrust or Arize and fully commercial, with no open-source core to fall back on.

Best for: conversational and customer-facing agents you want to break in simulation before launch, with PMs and QA in the tool alongside engineers.

HoneyHive: evaluation built for the enterprise and the regulated

HoneyHive is the tool that shows up when the compliance team is in the room. It’s commercial but OpenTelemetry-native, a rare but welcome combination, since your instrumentation is standard OTel and can be pointed elsewhere.

It runs on “Evaluation-Driven Development”: decide what good looks like before you build, and measure every change against that bar. In practice, that means:

• More than 50 pre-built metrics out of the box.
• The same evaluators running online and offline, so development and production share one definition of “good.”
• A human “Review Mode” where domain experts grade outputs directly.
• A Prompt Studio that stores prompts as YAML, with no proxy required.
• CI through GitHub Actions, so evals gate merges like any other test.

The security posture is where HoneyHive pulls ahead. It has SOC 2 Type II, GDPR, and HIPAA, single-tenant and VPC deployment, and a “virtual data plane” that keeps each tenant’s data isolated rather than logically partitioned in a shared pool.

All of which is built for organizations that have a compliance function, and the pricing reflects that. A two-person startup will find it heavier than it needs.

Best for: banks, healthcare, and other regulated enterprises that need audit-grade evaluation and hard data isolation, without a proprietary SDK.

Comet Opik: open source, with optimization and guardrails built in

Opik is refreshing for a simple reason: it’s Apache 2.0 licensed and the full feature set ships in the open-source core, not some crippled community edition. In a category where “open source” usually means “open source until you need SSO,” that deserves applause.

You get tracing plus more than 30 eval metrics and LLM-as-a-judge, putting it in the same league as the commercial tools on coverage. But it does also have two unique features:

1. The Agent Optimizer doesn’t just grade your prompt, it fixes it. Most tools tell you your prompt scores 0.72 and leave you to figure out why. Opik ships several prompt-tuning algorithms (evolutionary search, few-shot selection, meta-prompt optimization, and so on) that tune the prompt for you and hand back a version that scores better.
2. Cost Intelligence tracks what your coding agents are spending. That’s a very 2026 problem, given that one Claude Code or Cursor session can eat more tokens in an afternoon than your chatbot does in a week.

This is rounded out with guardrails for runtime safety checks, online evaluation rules that score production traffic as it flows, multimodal logging, and OQL, a query language for interrogating traces instead of scrolling them. You can self-host with Docker or Kubernetes, or use the roomy cloud tier. It’s the newest of the eval-first bunch, but it arrived with enterprise features in place, thanks to Comet’s long history in ML tooling.

Best for: commercial-grade evaluation without a commercial license, and for anybody who’d like their tool to help fix the prompt, not just grade it.

Gateways and Proxies

These sit between your app and the models, and since everything flows through them, they become agent observability tools almost for free. Their real job is routing, reliability, and cost, giving one unified API across providers with automatic failover, load balancing, caching, virtual keys, budgets, and guardrails. Just know that most gateways give you only request-level logging, not the deep span trees of a dedicated tracer, plus the additional latency will be felt by all your users.

Portkey: The gateway that became a full control plane

Portkey started as a router and grew into an AI control plane. It routes to more than 1,600 models, and in March 2026 it did something most gateways haven’t: it open-sourced the entire gateway under MIT. There’s also an open-source MCP Gateway alongside it, which matters as agents start reaching for MCP tools the way they used to reach for plain APIs.

Performance is the other reason people pick it. The overhead is sub-millisecond, and it’s pushing over a trillion tokens a day.

On top of the routing itself, here’s what you get:

• More than 40 metrics with per-model, per-user, and per-virtual-key cost attribution. When spend triples, you know exactly whose feature did it.
• Over 50 guardrails, including jailbreak detection and PII redaction, enforced right at the gateway before anything reaches the model or your user.
• Semantic caching that’s smart enough to know “what’s your refund policy” and “how do refunds work” are the same question.
• SSO and SCIM for the enterprise checklist.

Every gateway hits the same ceiling, and Portkey is no exception: it sees requests, not reasoning. It knows your agent made nine model calls. It doesn’t know they were nine steps from one plan.

Best for: orgs running many models across many teams who want routing, cost governance, and guardrails in one enforceable layer.

LiteLLM: The most widely used open-source gateway

LiteLLM is the closest thing this category has to a default. It’s MIT-licensed, YC-backed, and has over 53,000 stars with more than 1,600 contributors. Netflix, Lemonade, and Rocket Money all run on it.

It comes in two flavors: a Python SDK you call from code, and a proxy server your whole org points at. Either way, the promise is one OpenAI-compatible API across 100-plus providers, so switching from GPT to Claude to a self-hosted Llama is only a config change. LiteLLM also brings a strong operational kit: virtual keys, spend tracking and budget limits, fallbacks and load balancing, safety guardrails, and an admin UI.

Something to keep in mind with LiteLLM is that it mostly runs through callbacks. It was built to feed tools like Langfuse, Helicone, and OpenTelemetry rather than housing your data. Don’t pick it expecting deep agent tracing, as you’ll have picked half a stack; plan on pairing it.

Best for: anyone who wants provider-agnostic routing, real cost controls, and no lock-in, but must be paired with a dedicated tracing tool for visibility.

Hybrid ML and LLM Platforms

Most of these started in classic ML monitoring with drift, embeddings, model performance, explainability, and added LLM and agent features as the world shifted. That heritage is the thing to weigh. They’re a natural fit if your machine learning runs through one of them today, and the big question is whether you want a single platform spanning classic ML and GenAI, or one of the dedicated, AI-native agent observability tools that digs deeper on the LLM side.

Arize (and Phoenix): ML-monitoring roots, a strong open-source core

Arize is a commercial AI observability platform that raised a $70M Series C in February 2025, with customers like Uber, DoorDash, Reddit, and Booking. Arize says the platform chews through roughly a trillion spans a month, so whatever you’re about to throw at it, it’s seen worse.

It comes in two pieces, and the split favors you more than most. AX is the enterprise product. Phoenix is the open-source core: Elastic License 2.0, around 10,000 stars, OpenTelemetry-native, and fully self-hostable with no feature gates. Phoenix runs on OpenInference, Arize’s semantic conventions for AI telemetry, which have been adopted well beyond Arize itself, so even the “proprietary” bit is a de facto standard.

The ML heritage surfaces in the analysis tools. Embeddings and cluster analysis let you project thousands of traces into embedding space and look at them as clusters. For example, maybe you run a failures cluster and spot a dense blob of unhappy outputs that all turn out to be questions about a product line your retriever never indexed. That’s a class of bug you can’t find by scrolling a trace list. There is also span replay for re-running just a step instead of everything, RAG metrics covering the retrieval side rather than only generation, and open-source eval models so your judges aren’t a proprietary API call.

That same ML DNA cuts both ways. It’s a strength if you also run classic models, and baggage if you don’t. If your world is purely agents, some of this surface area is a tax on capabilities you’ll never open.

Best for: running traditional ML and GenAI on one platform, and RAG-heavy applications where embedding-space analysis earns its keep.

Weights & Biases Weave: The natural pick if you live in W&B

Weave is the GenAI side of Weights & Biases (W&B). If your training, experiment tracking, and model registry run through W&B today, Weave puts your fine-tuning experiments and production agent traces in the same console, under the same projects and permissions. That organizational simplicity counts for more than most feature comparisons admit.

It’s commercial, with a proprietary SDK built around the @weave.op decorator. Slap it on a function and it gets traced, inputs and outputs and all, which is very low friction to start, but also a real lock-in point, since that decorator isn’t OpenTelemetry so this instrumentation won’t travel. What it does well is fit the model of an agent: sessions, turns, steps, tools, and sub-agents are native concepts, which means an orchestrator delegating to three sub-agents shows up as a clean hierarchy. With that also comes evals and scorers for comparing prompt and model versions, guardrails for toxicity, harmful bias, PII, and hallucination, a playground, and W&B Inference for running open models directly.

Strip W&B out of the picture, though, and most of the advantage evaporates. You’re then weighing a proprietary SDK tool against OTel-native competitors. Weave is a strong reason to stay; it’s a weak reason to switch.

Best for: existing W&B customers who want fine-tuning and agent observability in one place, and who’ll trade OTel portability for ease of use.

MLflow: The open-source ML standard, extended to GenAI

If you’ve done any machine learning in the last five years, you’ve used MLflow. It’s Apache 2.0, a Linux Foundation project that came out of Databricks, with around 20,000 stars and more than 20 million downloads a month. It’s the de facto open-source ML lifecycle tool and MLflow 3 pushed it into the GenAI world.

Being a Linux Foundation project matters here. Unlike a company’s open-core loss-leader, it carries no acquisition risk and no possibility of a license rug-pull.

MLflow Tracing is fully OpenTelemetry-compatible and supports the GenAI semantic conventions natively, so you’re not adopting a proprietary format. It captures inputs, outputs, latency, and cost across LLM and agent runs, and adds trace replay for diffing a captured trace against a new model or prompt, prompt versioning tracked with the same rigor MLflow applies to model versions, and automated evals hooked into CI/CD. The real superpower is that your GenAI work lands in the same registry as your classic models, so you have one lineage story, one artifact store, one place your compliance team knows how to audit. Self-host with full auditability, or use the managed version through Databricks.

Against the AI-native tools, MLflow’s GenAI surface is newer and more utilitarian. What you get in exchange is a rock-solid, vendor-neutral foundation that isn’t going anywhere.

Best for: existing MLflow users, and any org that wants a neutral, foundation-governed home for both models and agents.

Pydantic Logfire: Full-stack observability that’s great at LLMs

Logfire comes from the Pydantic team, which tells you a lot before you read a single feature: this is a tool built by people with strong opinions about developer ergonomics. The licensing is split sensibly. SDKs are MIT across Python, TypeScript, and Rust, while the hosted backend is closed source, with self-hosting on the Enterprise tier.

It’s a full-stack observability tool that happens to be unusually good at LLMs. Database queries, HTTP requests, background jobs, and model calls all land in one place. When your agent is slow, you can see whether the model was slow or whether the Postgres query feeding the prompt took four seconds, without switching tools. That correlation is something the LLM-only tools structurally can’t give you.

Three things stand out:

• The LLM Panel renders GenAI spans as chat-style conversations with cost attached, instead of just some JSON blob of messages.
• Everything is queryable with PostgreSQL-flavored SQL — no bespoke DSL to learn and forget.
• An MCP server lets your coding agent query production telemetry directly, meaning your assistant can read the failing traces before it proposes a fix.

It’s Python-first, ties in deeply with the Pydantic AI agent framework, and the free tier is unusually large. But the evaluation story is thinner than the eval-first tools. Logfire will show you what happened; it’s less opinionated about whether it was any good.

Best for: Python codebases (especially Pydantic AI users) that want agent traces next to their app and database telemetry, and developers who’d rather write SQL than learn another query language.

Fiddler AI: Observability plus security, with in-house scoring models

Fiddler is a commercial AI observability and security “control plane,” with many Fortune 500 and government users and deep roots in ML monitoring and explainability, now extended to LLMs and agents.

The Fiddler Trust Service solves a problem most teams don’t notice until it’s too late. Everyone else’s LLM-as-a-judge evaluation calls another LLM over the network, at real cost to latency. Fine for offline scoring; useless for guardrails. You can’t add 800ms and a per-check API bill to every response. Fiddler’s answer is “Centor Models”, small models made for the job that run inside your own environment, returning safety, faithfulness, and PII checks quickly with no “trust tax” from calling out to a third party. That means you can score every production response rather than sampling only 2%.

It’s why Fiddler can offer runtime guardrails that step in, pause, or reroute before a bad output reaches a customer, instead of just logging a warning. Most agent observability tools tell you what went wrong. Fiddler can stop it from going wrong.

The rest of the feature set is the ML heritage paying off:

• Hierarchical agentic observability, from the application level all the way down to individual spans.
• Embedding-drift monitoring for the slow, silent failures, where nothing errors out but your inputs quietly stop resembling what you built for.
• Multimodal support across tabular, text, and image data.
• Air-gapped and VPC deployment, for keeping everything inside your own walls.

This is enterprise software, priced and sold like enterprise software.

Best for: regulated, high-stakes deployments that need to block bad outputs, not just observe them, with scoring inside their own perimeter.

Broad Observability Platforms with Agent Modules

Now for the big general-purpose players, which have all bolted agent observability tools onto their existing suites. If your company runs everything through one of these today, the AI module is an easy organizational choice. Your LLM and agent traces land right next to your infrastructure and application metrics, in tooling your SRE team knows cold. The caveat runs through the whole group: they’re strong on operational telemetry like latency, errors, cost, and infra correlation, but weaker than the AI-native tools on output-quality evaluation. That’s why many teams pair one with a dedicated eval tool.

Datadog: The APM giant, now with an agent module

If Datadog is your incumbent, this is a pretty easy choice. You get LLM Observability plus AI Agent Monitoring, with LLM Experiments and an AI Agents Console in preview.

Datadog’s edge isn’t any single feature, it’s that it tracks everything. When your agent starts timing out, the question is rarely “was the model slow?” It’s “was the model slow, or did the vector DB fall over, or did a downstream API start rate-limiting us?” Datadog has the answer, because it’s been watching all of it the entire time. Newer additions include “Bits AI,” an assistant that scans your logs and docs and proposes fixes. It supports the OpenTelemetry GenAI conventions, though the fullest features require Datadog’s proprietary agent.

Two things to know going in:

1. First, the pricing is per-host plus per-log which gets pricey quickly at scale.
2. Second, actual quality evaluation is an add-on, not a first-class feature.

Best for: Datadog shops who want agent telemetry correlated with infrastructure, and who accept they’ll need something else for output quality.

New Relic: Full-stack, with friendlier pricing

New Relic covers most of the same ground as Datadog, and the biggest reason to prefer it is the bill. It uses ingest-based pricing per gigabyte, with 100GB a month free and unlimited hosts and containers. That’s important because agents are chatty: a single long run can produce thousands of spans stuffed with full prompts.

The agent story for New Relic has moved fast. Agentic AI Monitoring launched in November 2025, and a February 2026 update added the pieces that matter for multi-agent systems:

• Service maps of agent interactions showing which agent called which subagent and which tool.
• Agent performance views breaking down where time and tokens went.
• Multi-agent trace drill-down, letting an orchestrator delegating to sub-agents read as one coherent story.
• Cloud Cost Intelligence for displaying model and cloud spend together.

It carries the same quality gap as the rest of this group. New Relic is an operations platform that speaks agent, not a quality platform.

Best for: full-stack observability with agent support and predictable, volume-based pricing, especially where per-host billing is driving your costs.

Dynatrace: Enterprise automation, causation-based root cause

Dynatrace is aimed at large, regulated enterprises, and it’s the most automated of the agent observability tools in this group. The architecture rests on four pillars:

1. Grail, a data lakehouse that stores telemetry without making you pre-decide which fields you’ll query later.
2. OneAgent, which auto-instruments hosts and containers on install.
3. Smartscape, a live topology map that updates itself.
4. Davis AI, the real differentiator, which does causation-based root-cause analysis rather than the usual correlation-based.

That last distinction is the pitch. Most tools notice three metrics spiked at once and hand you these three suspects. Davis uses the dependency graph from Smartscape to work out which spike caused the others, and hands you one final answer.

On the AI side, the coverage is broad:

• All the fundamentals: agent execution paths, tool calls, RAG, guardrail metrics, A/B testing, and cost and latency.
• Integrations with the OpenAI Agents SDK, LangGraph, CrewAI, Bedrock AgentCore, and MCP.
• And, unusually, monitoring for vector databases like Milvus, Weaviate, and Qdrant, which is where a lot of RAG failures actually originate.

Expect a high entry price, no meaningful free tier, and no open source. You’re buying automation and paying for it.

Best for: large enterprises who want auto-instrumentation and a single confident root cause rather than a list of suspects.

Cisco (Splunk + Galileo): Network, security, and AI telemetry under one roof

This one is a consolidation story, best told in order. Splunk, which Cisco bought in 2024 for around $28B, brings heavyweight log ingestion. Splunk’s superpower has always been eating absurd volumes of unstructured data and letting you query it later, and that pairs well with AI telemetry, since agent traces are also enormous, semi-structured, and useless until you ask the right question.

What Splunk didn’t have was an opinion about quality. So on April 9, 2026, Cisco announced its intent to acquire Galileo, an AI-native evaluation, observability, and guardrails startup founded in 2021. Galileo brought Luna-2 evaluation models, 20-plus metrics covering hallucination, relevance, safety, and agent behavior, and real-time guardrails.

That deal has since closed, and Galileo is being folded into Splunk’s AI Agent Monitoring across the entire agent lifecycle. Cisco can now sell one solution for network, security, and AI-agent telemetry, which makes for a very short procurement conversation at an enterprise running Cisco networking and Splunk SIEM.

Beware the obvious risk with an acquisition this fresh: integration is still a promise, not yet a product.

Best for: large enterprises deep in Cisco and Splunk who want AI telemetry from a vendor on the existing contract, and who can tolerate a post-acquisition timeline.

The Gap Every One of These Tools Leaves Open, and How to Close It

Step back and a pattern jumps out. Every one of the agent observability tools above watches only a slice: the trace, the eval, the gateway, the dashboard. And nearly all of them stop at the agent.

Which means that when a stale table or a broken pipeline poisons an answer, your trace will look perfectly healthy because the agent did exactly what it was told with the numbers it was handed. By the time you’re reading the span tree, the damage happened upstream and left no fingerprints. Almost none of these agent observability tools watch the data feeding the agent and the agent’s behavior in the same place. That’s the gap, and it’s the one Monte Carlo, agent trust platform, is built to close.

Instead of splitting reliability across five tools that don’t talk to each other, Monte Carlo gives unified visibility across the four pillars that determine whether an agent can operate reliably in production:

• Context — the data and context the agent retrieves through RAG and tool calls, monitored alongside the pipelines that feed it.
• Performance — cost, latency, token usage, and error rates across the full workflow.
• Behavior — whether the agent’s workflow executed as intended, step by step.
• Outputs — whether the answers come back accurate, relevant, and safe.

On top of that, end-to-end lineage ties every output back to the source data that produced it, so when something breaks you find the real root cause instead of guessing, and all of that telemetry lives in your own warehouse or lakehouse, not someone else’s cloud. Monte Carlo is the only vendor tying observability across both the data and AI stacks in a single platform. If that’s the gap you’ve been feeling, drop your email below and we’ll show you what it looks like on your own agents.

Our promise: we will show you the product.