The 20 Best AI Agent Observability Tools for Production in 2026

AI agents fail differently from traditional software. A broken REST call throws an exception and halts. An agent calling the wrong tool with plausible-looking parameters might complete successfully, log no errors, and propagate a bad decision through three more steps before anything looks wrong. By the time a user notices, the trace is cold and the context window is gone.

This is the problem that's driving a new category of tooling. Traditional application performance monitoring was built around latency, error rates, and throughput. Those metrics matter for agents too, but they tell you almost nothing about whether the agent made the right call, picked the right tool, respected policy constraints, or hallucinated a result that downstream logic treated as ground truth. Observing an agent means capturing spans at the model-trace level, not just the HTTP level.

The market has split into two camps: purpose-built platforms designed from scratch for model traces and evaluation, and established observability vendors extending their platforms upmarket to cover AI workloads. Both are legitimate choices depending on where your stack already lives. StartupHub.ai data shows more than 100 companies now active in the AI agent observability and MLOps space, with dedicated model monitoring tools accounting for a growing share of new entrants since mid-2025. The 20 tools below cover the full range, from traces and evals to infrastructure visibility and policy governance.

1. LangSmith

The closest thing to a standard for AI application debugging, built by the team that made LangChain ubiquitous.

LangSmith captures execution traces at the span level, so you can see exactly which tool the agent called, what input it passed, and what output it got back. Its evaluation framework lets teams run regression tests against golden datasets before shipping changes, which matters more than runtime alerts when you're trying to prevent the wrong tool call from ever reaching production.

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2. Arize AI

A purpose-built AI observability and model evaluation platform for teams running agents in production at scale.

Arize gives AI engineers a single pane of glass to monitor model behavior, track drift, and run structured evaluations on agent outputs. Its Phoenix open-source tracing library has become a common instrumentation layer for teams that want vendor-neutral traces with a managed evaluation backend behind them.

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3. Braintrust Data

An AI-native observability platform focused on tracking the quality of model outputs rather than just their latency or cost.

Braintrust frames observability as a quality problem, not an infrastructure problem. Its platform monitors AI models and products in production, tracking whether outputs meet the criteria teams actually care about. The focus on quality scoring rather than operational metrics puts it in a different category from tools that primarily surface token counts and response times.

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4. Coralogix

A full-stack observability platform that added AI workload visibility without asking teams to adopt a separate tool.

Coralogix unifies logs, metrics, traces, security, and AI observability into one streaming analytics system. For teams already running their infrastructure observability through Coralogix, the AI agent layer arrives as an extension rather than a new vendor, with the same query interface and the same alert routing.

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5. Fiddler Labs

An AI Control Plane that covers the full agentic lifecycle: test, observe, protect, and govern, in one product.

Fiddler treats the observability problem as inseparable from the security and governance problem. Its platform lets enterprise teams validate agent behavior before deployment, monitor it at runtime, and enforce policy constraints on what agents can do. That combination of eval, monitoring, and guardrails in one product reduces the number of vendors a team needs to manage.

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6. Comet ML (Opik)

The Opik platform connects model trace data to automatic code repair, closing the loop between observability and remediation.

Comet ML reoriented around its Opik platform, which goes further than most monitoring tools by using trace data to automatically suggest or apply fixes to agent code. The connection between "here is what went wrong" and "here is the corrected implementation" shortens the feedback loop for teams iterating on agent behavior.

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7. Onyx Security

A secure AI control plane for enterprises that need to discover, monitor, and govern agents spreading across SaaS, cloud, and endpoints.

Onyx Security addresses a problem that most observability tools ignore: you can't monitor an agent you don't know exists. Its platform discovers AI agents deployed across the organization, maps their access and tool permissions, and enforces governance policies across the full estate. The emphasis on shadow agents makes it relevant for enterprises where business units deploy tools faster than central IT can track them.

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8. Solo.io

Enterprise agentic AI orchestration with model operations built in, aimed at organizations running agents across complex multi-model architectures.

Solo.io combines agentic AI orchestration with model observability, RAG infrastructure, and inference optimization in one platform. For teams managing agents that call multiple models or mix retrieval with generation, Solo.io's unified visibility across the orchestration layer fills a gap that single-model monitoring tools leave open.

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9. Datadog

The platform where most infrastructure teams already live, now with native model observability baked into the same dashboards they use for everything else.

Datadog's model observability product brings model traces, prompt monitoring, and cost tracking into the same SaaS platform teams use for infrastructure and APM. For organizations that don't want a separate vendor for AI monitoring, Datadog's breadth is the argument: one agent, one query language, one alert system covering the full stack.

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10. New Relic

A data observability company that extended its software stack monitoring to cover AI model applications, debugging, and AI-specific performance signals.

New Relic added AI monitoring capabilities through its New Relic AI platform, allowing engineers to track model performance alongside the rest of their software observability. Its strength is the density of integration with existing infrastructure: teams already streaming telemetry to New Relic can add AI workload visibility without re-instrumenting their codebase.

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11. Grafana Labs

The open-source observability platform explicitly positioning its roadmap around the needs of teams building in the agentic era.

Grafana Labs has been explicit that its product direction is oriented around agentic workloads, extending the Grafana stack to unify telemetry signals from AI agent pipelines alongside traditional logs and metrics. For teams committed to open-source tooling at the observability layer, Grafana's ecosystem breadth and self-host option are significant advantages.

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12. Monte Carlo

A data observability platform repositioned as an intelligent orchestration system for monitoring AI systems running in production.

Monte Carlo started in data reliability but has moved toward AI system orchestration, giving teams tools to monitor, troubleshoot, and improve the data pipelines that feed their agents. For teams where bad agent behavior traces back to bad input data rather than bad model outputs, Monte Carlo's focus on the data layer is the right entry point.

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13. Temporal

An open-source durable execution engine that makes agent workflows observable and recoverable without requiring teams to bolt on a separate monitoring layer.

Temporal builds observability into the execution model itself. Every workflow step is recorded, retryable, and inspectable through the Temporal UI, which means you can see exactly where an agent workflow diverged from expected behavior without needing external tracing. For teams writing agents as durable workflows, the observability is a property of the runtime rather than an afterthought.

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14. Grafana Cloud

The managed version of Grafana's full-stack observability platform, built on open standards and designed to unify every telemetry signal in one place.

Grafana Cloud unifies metrics, logs, traces, and dashboards in a fully managed environment, giving teams the open-source Grafana ecosystem without the operational overhead of self-hosting. Its composable approach lets teams bring in OpenTelemetry traces from agent pipelines alongside existing Prometheus metrics without vendor lock-in.

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15. Logz.io

A cloud-native observability platform that combines open-source log analytics, metrics, and distributed tracing with AI-assisted alert routing.

Logz.io integrates and extends ELK, Prometheus, and Jaeger, offering the open-source tooling most teams already know with a managed backend and AI-assisted analysis on top. For teams instrumenting agent tool calls with distributed tracing, Logz.io's Jaeger integration provides span-level visibility into multi-step agent workflows without building custom tracing infrastructure.

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16. Groundcover

A Kubernetes observability platform using eBPF to give engineers and agents deep visibility without requiring code instrumentation.

Groundcover instruments at the kernel level using eBPF, which means teams can get full-stack observability for containerized AI workloads without modifying their application code. Its platform explicitly positions itself as a tool "for engineers and agents," covering the infrastructure layer underneath the model stack without the overhead of traditional APM agents.

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17. AppDynamics

An enterprise APM platform with deep application transaction tracing, now positioned under the Cisco Splunk portfolio for broader security integration.

AppDynamics provides application performance management with granular transaction tracing and business impact scoring. As part of the Cisco/Splunk portfolio, it now integrates more tightly with security tooling, which matters for organizations that want to correlate agent behavior anomalies with broader threat signals rather than treating observability and security as separate disciplines.

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18. MagicMirror

Real-time visibility and control over enterprise GenAI usage, focused on understanding how employees and agents actually use AI tools day to day.

MagicMirror provides local GenAI observability that sits close to the user and agent layer, tracking usage patterns, preventing data risk, and giving organizations the governance information they need to run responsible AI programs. Its emphasis on understanding actual AI activity, rather than just infrastructure metrics, addresses the gap between what companies think their agents are doing and what they're actually doing.

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19. Checkly

A code-first synthetic monitoring platform that unifies testing, monitoring, and observability for teams that want reliability verification built into their deployment pipeline.

Checkly lets developers write monitoring checks as code and run them as part of CI/CD, treating uptime and synthetic tests as first-class artifacts rather than a post-deployment concern. For AI agent workflows where the failure mode is often a silent wrong output rather than a hard error, Checkly's synthetic monitoring approach, running expected-behavior checks on a schedule, provides a complementary layer to trace-based observability.

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20. LogicMonitor

A hybrid observability platform using predictive AI to give infrastructure teams unified visibility across on-premise and cloud environments.

LogicMonitor delivers observability across hybrid IT environments, using predictive analytics to surface issues before they affect users. Its breadth of integration, covering cloud, on-premise, and network infrastructure, makes it relevant for enterprises running AI agent workloads across mixed environments where a pure cloud-native tool would leave significant gaps.

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What This List Reveals About the Space

The clearest pattern across these 20 tools is the gap between what you need to monitor an agent and what traditional APM was built to do. Tools like LangSmith, Arize AI, and Braintrust Data exist because latency and error rate dashboards tell you almost nothing about whether an agent made a good decision. Span-level traces at the model layer, evaluation frameworks, and output quality scoring are genuinely new capabilities, not repurposed versions of something that existed before.

The established observability platforms, Datadog, New Relic, Grafana, and AppDynamics, are catching up through product extensions rather than rebuilding from scratch. That gives them distribution advantages and makes adoption easier for teams with existing contracts, but purpose-built tools still tend to have more depth at the model-specific layer. The segment most underserved right now is policy-level governance: knowing not just what your agent called, but whether that call was consistent with the intent it was authorized for and the policies it should have respected. Onyx Security and Fiddler Labs are moving toward that problem, and it's where the next round of differentiation in this category will play out.

Frequently Asked Questions

What is AI agent observability?

AI agent observability means capturing the inputs, tool calls, intermediate reasoning steps, and outputs of an AI agent in enough detail to understand why it behaved the way it did. Unlike traditional APM, which focuses on latency and error rates, agent observability requires span-level traces at the model layer, structured logging of tool invocations, and often an evaluation layer that scores output quality against expected behavior.

How do model operations tools differ from traditional MLOps?

Traditional MLOps focuses on training pipelines, model versioning, and batch inference at scale. Model operations tooling shifts attention to the runtime behavior of foundation models in interactive and agentic contexts, covering prompt management, trace-level debugging of multi-step agent workflows, evaluation against human or automated reference sets, and cost management across token usage. The tooling overlaps but the failure modes and operational rhythms are distinct.

Which tools work best for real-time AI agent monitoring?

For real-time trace visibility, LangSmith and Arize AI both provide low-latency span capture during agent execution. Datadog and Coralogix offer real-time streaming for teams that want AI traces alongside infrastructure telemetry. Checkly adds a synthetic monitoring layer that verifies expected agent behavior on a schedule. The right choice depends on whether your team needs deeper model-specific analysis or unified visibility across the full infrastructure stack.