AI Agent Observability: The 8 Best Tools for Production Agents

Your AI agents work fine in a demo. Then they hit production, and something breaks. An agent that used to complete a task correctly now stalls on a tool call, hallucinates an answer, or loops between two steps and burns through tokens for 10 minutes before timing out. No one on the team can say why because no one can see what the agent actually did.

That gap shows up because agentic AI doesn't behave like the code most observability tools were built to watch. Frameworks like LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK all produce non-deterministic systems where the same input can take a different path on different runs. This is something traditional monitoring, built around predictable request paths, doesn't handle. That means watching them requires tracking traces, tool calls, and model behavior instead of basic request-and-response codes. Without that level of visibility, agents remain black boxes where you can see something went wrong but not why. 

Fixing that visibility gap starts with identifying which operational problem you are trying to solve. Some teams need basic execution tracing: identifying which tool calls fired, where latency piled up, or why a handoff between two agents failed. Other teams already have trace visibility but can't tell if output quality is degrading, whether a model hallucinated, or if a prompt update made things worse. Below are eight platforms engineering teams are using to solve one or both gaps, along with guidance on how to choose the right fit for your stack.

The best AI agent observability tools for production teams

Every tool below does some mix of tracing what your agents did and judging whether what they did was correct. We evaluated each one against four criteria: trace and span depth across multi-agent workflows (not just single LLM calls), evaluation and quality scoring (including hallucination and bias detection), integration with the telemetry you already collect (APM, infrastructure, logs), and deployment model, including whether self-hosting or VPC deployment is available.

New Relic

New Relic built its AI monitoring on top of an observability platform most engineering teams already run for APM, infrastructure, and logs. Because of this, agent traces show up alongside telemetry instead of in a separate tool.

Key features:

• Automatically captures model name, token usage, tool calls, and message content for supported agent frameworks, including LangGraph, Strands, and AutoGen
• Accepts OpenTelemetry data mapped to the GenAI semantic conventions, allowing custom agents built outside the supported frameworks to show up in the same traces
• Employs an Agents Service Map that visualizes multi-agent handoffs and call relationships in real time, with a drill-down into the underlying traces
• Features distributed tracing across tool calls and MCP server calls, correlated with the APM, infrastructure, and log data already in New Relic to eliminate tool-hopping during root-cause analysis
• Accepts OpenTelemetry spans so teams can instrument checks like prompt injection detection within standard trace views

The trade-off: Framework coverage is still expanding — confirm your specific framework is on the current supported list before committing, and evaluation capabilities are lighter than dedicated platforms.

Best for: Teams that already run New Relic (or that want one platform instead of several) and need immediate agent visibility without standing up a second tool. 

Datadog

Datadog takes the same basic approach as New Relic by extending a general observability platform to cover agent tracing, then correlating it with APM, infrastructure, and real user monitoring data.

Key features:

• Traces prompts, retrieval steps, and tool calls end to end, tracking latency, token usage, retries, and errors at each step
• Supports OpenAI, Anthropic, Gemini, and Bedrock models, as well as frameworks like LangChain, CrewAI, Pydantic, and Strands Agents
• Features built-in and custom evaluators, dataset versioning pulled from production traces, and experiments comparing prompts and models against real traffic
• Offers billing per LLM span, with tool, agent, embedding, and retrieval spans included free

The trade-off: Datadog's broader platform has a reputation for being expensive and hard to forecast on top of agent tracing.

Best for: Teams already standardized on Datadog for APM and infrastructure that want agent tracing plus real evaluation without adding a vendor.

Langfuse

Langfuse functions as an open-source, framework-agnostic telemetry platform designed for teams that prioritize full data ownership. It focuses on tracing and execution monitoring first, providing a clean foundation to layer on evaluations and prompt management

Key features:

• Runs on an MIT-licensed core (with a small enterprise-edition module under separate terms) that's available as a hosted cloud service or self-hosted in your own VPC
• Traces every LLM call, tool call, and retrieval step in a hierarchy, with cost and latency attached
• Scores output using LLM-as-a-judge, heuristic evaluators, and human review
• Separates prompt management from your codebase with one-click rollbacks, integrating across 100+ model providers and frameworks

The trade-off: Self-hosting for VPC or compliance requirements means running infrastructure yourself, and Langfuse doesn't correlate with your broader APM or infrastructure telemetry.

Best for: Teams that want a dedicated, open source tracing and evaluation layer they can self-host, independent of whatever platform they use for the rest of their stack.

LangSmith

LangSmith is the native observability platform built by the team behind LangChain and LangGraph. Because of that shared origin, it offers the deepest, lowest-friction integration for teams already using the LangChain framework. 

Key features:

• Delivers native framework support for LangChain alongside OpenTelemetry ingestion for custom setups 
• Tracks costs, online evaluations, tool execution, and alerting
• Surfaces automatic pattern detection through trace clustering
• Uses SmithDB, a purpose-built trace database, to deliver fast queries across millions of traces, with self-hosting options available for enterprise tiers

The trade-off: Confirm self-hosting details with your rep before assuming it's included at your tier. Also, like other eval-first tools, it doesn't correlate with APM or infrastructure telemetry.

Best for: Teams building specifically on LangChain or LangGraph that want the tracing and evaluation tool built by the same team as the framework.

Arize (Phoenix / AX)

Arize comes from traditional ML observability, monitoring embeddings and model drift before the current wave of LLM agents, and that heritage shows in its evaluation tooling.

Key features:

• Ships as Phoenix, a source-available version under the Elastic License 2.0 (more restrictive than MIT or Apache terms), and Arize AX, the commercial platform
• Uses OpenInference and OpenTelemetry semantic conventions
• Organizes three functions: Observe (tracing), Evaluate (span, trace, and session-level scoring), and Learn (testing prompt or harness changes before you ship them)
• Adds an AI agent in Arize AX that automates parts of debugging and a GenAI-optimized datastore integrating with BigQuery, Databricks, and Snowflake

The trade-off: Phoenix's license terms mean it isn't unrestricted open source, so check whether Elastic License 2.0 works for your deployment plans. The breadth across Observe, Evaluate, and Learn also means a longer ramp-up if all you need is basic tracing.

Best for: Teams already using Arize for traditional ML monitoring that want the same platform extended to LLM agents or teams that want a mature evaluation framework built on open standards.

Braintrust

Braintrust built its platform around an evaluation-first workflow, making output quality and regression testing the core focus. Rather than treating production tracing as a separate task, it connects live traces directly into test datasets to help engineering teams catch model failures before release. 

Key features:

• Traces production runs in real time, capturing tool execution, latency, and cost across agent workflows
• Establishes baseline quality criteria prior to deployment to run comparative experiments across prompts and models
• Converts production traces into evaluation datasets with one click, catching regressions against real user failures rather than synthetic tests 

The trade-off: Braintrust provides less depth when correlating agent behavior with your infrastructure and APM telemetry than a full-stack observability platform offers.

Best for: Teams whose main problem is proving and improving output quality over time, especially regression-testing prompts and models against real production failures.

Comet (Opik)

Comet extends its machine learning experiment tracking background into agentic workflows through Opik. It combines open-source tracing with automated prompt optimization, targeting teams that want end-to-end evaluation without relying on closed commercial suites. 

Key features:

• Traces agent behavior across development, testing, and production
• Scores output with LLM-as-a-judge across 30+ metrics, including answer relevance, context precision, task completion, and hallucination detection
• Uses a Prompt Optimizer with six algorithms that tune tool calling, orchestration, and model parameters automatically
• Distributes under an Apache 2.0 open-source license with a self-hostable core, alongside a free cloud tier and an enterprise tier for larger teams 

The trade-off: Comet has less name recognition specifically for agent observability than Langfuse or LangSmith, and like other eval-first tools, it lacks native correlation with APM or infrastructure telemetry.

Best for: Teams already using Comet for ML experiment tracking that want the same platform for LLM evaluation or teams that want genuinely open source tracing plus a built-in prompt optimizer.

Confident AI

Confident AI builds on top of DeepEval, an open-source testing framework designed to bring software unit-testing patterns to LLM outputs. It targets engineering teams that want to embed output evaluation and security checks directly into their CI/CD pipelines before code hits production.

Key features:

• Adds team collaboration, dataset management, tracing, and real-time monitoring on top of local and CI testing in DeepEval
• Scores output for faithfulness, relevancy, and coherence, including support for G-Eval
• Automatically converts production traces into eval datasets, and categorizes failure modes and edge cases
• Runs red-teaming aligned with the OWASP Top 10 for Agentic Applications, useful for stress-testing agents against prompt injection before they reach users

The trade-off: The framing is testing and CI-first rather than an always-on dashboard, and it's less useful if your actual gap is visibility into agent behavior rather than output quality.

Best for: Teams that need to catch hallucinations, bias, and other failure modes before code ships, with the option to run the same checks continuously once agents are live.

What to look for in an AI agent observability tool

Choosing between these tools comes down to how they solve real production problems. Here's why those problems matter and how to test for them.

Multi-agent trace and hand-off visibility

Multi-agent systems create a failure mode single-call tracing wasn't built for. When two or three agents pass a task back and forth and call tools or APIs along the way, the number of possible paths through the system multiplies fast. 

A trace has to capture that whole chain, not just isolated spans. Test this by tracing a real multi-agent workflow end to end and checking whether you can find where a handoff broke down in a couple of clicks, not by digging through logs.

Evaluation and quality scoring depth

Tracing tells you what an agent did. It doesn't tell you whether the answer was right. Because agents are non-deterministic, the same input can take a different path and land on a different, possibly wrong, answer on two separate runs. 

Testing before launch isn't enough; you need ongoing evaluation of live traffic, and LLM-as-a-judge is what makes that practical at scale, but only when it's set up well. Galileo's 2026 State of AI Evaluation report found that untuned LLM judges agree with human experts only 66-68% of the time, a gap that closes significantly with techniques like multi-judge consensus, calibration, and fine-tuning. That's the real case for a dedicated evaluation layer over a DIY judge prompt: calibrated automated scoring paired with human review is what actually catches hallucinations, bias, and other regressions before a bad prompt update reaches most users.

Integration with your existing stack

Agent tracing that lives in its own disconnected tool only tells half the story. If an agent is slow, is that latency coming from the model, a tool call to an internal API, a vector database lookup, or the infrastructure it runs on? Without that correlation, root-cause analysis turns into manually cross-referencing timestamps across tools during an incident. 

Deployment model matters here too: if agent and prompt data must stay inside a VPC for compliance reasons, confirm the tool supports self-hosting before you evaluate anything else about it. Fragmented tooling is exactly what operational leaders say they want to reduce, with PagerDuty’s State of AI-First Operations survey showing that 51% of respondents feel consolidating multiple tools into a single platform improves operational resilience.

Extending your stack vs. adopting a dedicated eval platform

Teams that already run full-stack observability and just need to see what their agents are doing are usually better served by extending that platform rather than adding a disconnected tool. Teams doing heavy prompt and output quality evaluation at scale, such as testing against edge cases and running red-team checks for prompt injection, often benefit from an AI-native eval platform alongside their existing observability tool and whatever guardrails they've built.

New Relic's approach fits the first case: agent tracing correlated with the APM, infrastructure, and log data your team is already collecting, added to a platform you already run. It isn't a replacement for a dedicated evaluation platform, but for teams whose problem is "we can't see what our agents are doing," it closes that gap without a new system to deploy and pay for separately. One team used this approach to take an AI travel agent from a working demo to a production-ready service, setting SLAs and alerts once the agent handled real traffic.

Matching the tool to the gap you actually have

Before comparing vendors feature by feature, answer one question honestly: What can't you do today?

If nobody on your team can explain what an agent did during an incident, that's a visibility gap. The fix is extending your existing observability platform to cover agents as one more telemetry source next to your metrics and logs, not standing up a new system to babysit. If you can already see every trace but still can't tell whether the agent's answers are getting worse over time, that's an evaluation gap, and a dedicated AI-native platform built around scoring, datasets, and online evaluations will get you there faster. Most teams end up needing some of both. Start with whichever gap is costing you the most right now.

New Relic extends the observability platform many engineering teams already run to cover LangChain- and LangGraph-based agents, correlating traces with the APM, infrastructure, and log data you already collect. See how New Relic's AI monitoring works against your own agents, or request a demo to walk through it with your own stack.