AI agents extend far past chatbots to do things like plan out a task, involve external tools, search, generate code, and create complicated multi-step workflows. But an agent that accomplished a task doesn’t always do it well. It might look up the wrong answer, use the wrong tool, reveal a sensitive file, involve superfluous steps, or produce an artifactually but seemingly plausible answer.
Existing application monitoring methods can detect crashes, latency, and infrastructure failures, but such signals are insufficient to determine if an AI agent acted correctly, meaning that AI agent observability and evaluation tools are increasingly important to the development and operation of agentic AI systems.
Observability allows developers to discover what occurred during an agent’s run. Evaluation assesses if its performance and output have reached the quality standards set for it. The two allow teams to trace, diagnose failures, test enhancements, and track performance metrics.
As Braintrust’s Eval Manifesto explains, “The eval is as important as the model, harness, etc. It should be treated as part of the stack, not an afterthought.” The principle is increasingly relevant as businesses depend on AI agents for customer support, software development, research, financial operations, and internal automation.
Let’s see five popular platforms and five frameworks to look out for in 2026. Here’s a manageable comparison, not a definitive ranking: the best will vary depending on your team architecture, affordability, governance needs, and development process.
LangSmith: End-to-End Agent Development and Evaluation
LangSmith by LangChain: Tracing, debugging, managing datasets, experimenting, evaluating, and monitoring in production. It allows teams to scrutinize the execution of an agent, such as model calls, tool calls, intermediate steps, and final answers. Developers can use real production failures as evaluation samples, and see if a reworked prompt, model, or workflow is an improvement.
A significant 2026 development is LangSmith Turned Evaluators, introduced in August. These managed evaluators attach quality feedback to production traces, starting with a Perceived Error evaluator that identifies conversations where users appear to have received an incorrect or unhelpful experience. The first of these, a Perceived Error evaluator, adds a label to conversations that seem to have resulted in users receiving a wrong or unhelpful experience. LangChain reported reducing evaluation costs by up to 82% for this specialised evaluator in its benchmark, although actual savings depend on the workloads.
In September, LangSmith Engine v2 introduced proactive red-teaming, detection of inefficient execution paths, and trends in errors, latency, and cost, as well as automated testing of proposed prompt and code fixes. These capabilities move the platform beyond passive monitoring towards an evaluation driven improvement cycle.
Best Suited for: Teams that need a connected workflow for agent development, testing, production, tracing, and iterative improvement.
Langfuse: Open-Source Observability with Prompt & Skill Management
Langfuse is an open source LLM engineering platform that offers tracing, prompt management, datasets, evaluations, usage analysis, and cost visibility. With the ability to self-host, Langfuse might be appealing to organisations requiring more control over their infrastructure and data.
The updates in November 2026, are especially important for agent developers. Langfuse brought beta AI agent skill management, which enables teams to store and version control agent skills, collaborate on skills, and link versions to traces. This lets developers track down which skill was used when a workflow failed or returned an odd output.
More recent updates include a refreshed session view that makes messages across traces flow together; additional built in AI Gateway analytics; and more detailed API-key access controls. The public beta of the AI Gateway now enables you to track calls via the gateway, with usage costs divided by user, key, and model. 5 November marked the release of Python SDK version 5.2.0, which has a new observation-pagination utility and more resilient batch evaluation.
Best Suited for: Development teams seeking flexible, self-hostable observability with prompt, dataset, and agent-skill versioning.
Braintrust: Evaluation-First Artificial Intelligence Quality Engineering
Braintrust measures and enhances the quality of AI application using datasets, experiments, sourcing functions, evaluation runs, and observability in production. Teams can run side-by-side comparisons of model versions, prompts, and agent configurations on the best dataset prior to deploying changes.
Agent quality is something you can’t actually measure just by latency otr uptime. An agent that is quick may also be giving the wrong refund decision. If well crafted, and evaluation will verify conformance with policy, factual correctness, completeness, and whether or not the agent truly addressed the user’s request.
Braintrust enables a failure feedback loop in which production traces identify failure patterns; the patterns generate new eval cases; and follow-up experiments assess suggested improvements. This feedback loop is especially important for teams that release prompts/models often and want to catch regressions before they impact many users.
Who’s it for: AI product teams focused on systematic testing, regression tests and quantifiable improvement.
Datadog Agent Observability: AI Monitoring Within Enterprise Operations
Datadog Agent Observability links the specialized tracing and evaluation that is unique to AI with existing application monitoring, infrastructure telemetry, logs, and security processes.
This makes a difference when an agent going wrong is due to something outside the model, say a slow database, a non functioning API, or a retrieval system that’s on the fritz.
The system offers execution tracing, token, and latency analysis, evaluation datasets, custom evaluators, human review, and experiments on computing prompts, models, and agent configurations. Rather than trying to reason about each system in isolation, teams can create a model of the societal context in which an AI behaviour sits.
If your organization is currently a Datadog user, this direct integration eliminates the need to support two separate monitoring environments. Enables operations teams to understand the link between response quality, and infrastructure cost and availability.
Suitable for: Organisations looking to bring AI observability into their DevOps, site reliability engineering, and application monitoring stack.
Arize Phoenix: Open-Source Tracing and Evaluation
Arize Phoenix is an open-source tool for analysing LLM and agent execution, outputs, and RAG pipeline troubleshooting. The trace data for model calls, retrieval, and tool execution can be viewed for developers to determine what caused the wrong answer.
Phoenix supports relevance, correctness, and hallucinations risk evaluation pipelines. This is useful when an agent draws from the wrong docs, uses irrelevant context, or comes to a final answer that doesn’t follow from the evidence.
And it gives engineering teams the freedom to implement and consume observability on their own terms. When evaluating features, deployment options, and enterprise offerings, organisations need to distinguish between the Phoenix product and the larger Arize product portfolio.
Best for: Teams seeking transparent, developer-first tracing and measurement for RAG and other challenging LLM scenarios.
Galileo: Assessment and Guardrails for High-Stakes Pipelines
Galileo is on the side of AI quality, evaluation, and safety for production deploys. The Galileo platform allows teams to quantify the quality of outputs, and to diagnose issues that application-specific KPIs frequently miss, like hallucinations, retrieval errors, and dangerous outputs.
Seeing the final answer is not sufficient when evaluating agentic workflows. The system might arrive at the right answer via the wrong path, leverage an off-limits tool, or let something slip in the process. Quantity assurance and guardrails can enable teams to identify these patterns and enforce restrictions as necessary.
Galileo is especially for teams that require quantitative assessments during both the development and production of their solution and need tools and metrics that are customisable for the use case. Organisations must consider how the evaluators and deployment choices offered satisfy their regulatory and operational needs.
Who can use it: Teams working in the safety and quality of customer-facing or business-critical AI.
DeepEval and Confident AI: Evaluating Agents as Software
DeepEval is an open-source evaluation toolkit that allows developers to evaluate LLM applications based in metrics, test cases, and repeatable evaluation workflows. Confident AI’s platform manages and operationalizes the evaluation activity surrounding those tests.
This approach is commonplace for software engineers: specify desired behaviour, create tests, run tests, verify changes don’t break regressions. Agent-specific tests can evaluate tool selection, task fulfilment, reasoning traces if they exist, quality of retrieval, and adherence to set instructions.
DeepEval is designed to be used as part of a continuous integration/ continuous delivery pipeline, so that teams can identify regressions in quality as part of their everyday software testing. Automated metrics are not replacing human judgement in each domain. High impact use cases will benefit from programmatic metrics, expert human review, and targeted evaluation data.
Ideal for: Developers seeking code-driven AI testing, reusable evaluation cases, and automated checks within their development process.
Weights & Biases Weave: Experiments, Traces, and Model Comparisons
Weights & Biases Weave allows teams to track execution of LLM applications, observe outputs, compare experiments, and visualize application performance. As a member of the broader Weights & Biases ecosystem, Weave could provide a valuable resource to companies already tracking machine-learning experiments and model development.
Traces allow agent teams to inspect model calls and tool use, and evaluation datasets allow for consistent testing of how system changes affect performance. For instance, agent teams can determine if a new model reduces hallucinations without also increasing latency or tokens used.
Comparison of versions is important because a change in one metric can induce regressions in another. For instance, a model that writes more verbose responses might be slower and costlier. By examining traces and scores, developers can make such trade-offs before deployments.
Best for: Machine learning teams that want LLM tracing and evaluation along with broader experiment-tracking workflows.
AgentOps: Insight into Agent Operation
One of AgentOps’ focus is monitoring and debugging your AI agents. By centralising the most important information about your agent sessions and execution behaviour, you can adopt a different approach to agent observability and discover how your agent is navigating a task (tool calls, repeat actions, failed actions, etc).
One of AgentOps’ focus is monitoring and debugging your AI agents. By centralising the most important information about your agent sessions and execution behaviour, you can adopt a different approach to agent observability and discover how your agent is navigating a task (tool calls, repeat actions, failed actions, etc).
Teams utilizing Agent Ops should ensure that they review their integrations, trace coverage, assessment support, and deployment needs with their selected agent framework. The instrumentation quality and the rich context captured during execution will significantly influence the utility of any monitoring platform.
Use case: Tool-using agents Developers who are building tool-using agents need to see the content, execution flows, and behaviour of sessions.
Promptfoo: Evaluation, Security Testing, and Red Teaming
Promptfoo allows you to make a comprehensive set of tests for any LLM application, compare models and prompts, and perform adversarial security tests. Qualities such as quality, safety, and performance can all be measured via assertions and metrics. Promptfoo can be integrated into your CI/CD pipeline. Its red-teaming features can be used to help write adversarial tests.
This matters for AI agents. Because they can use tools and plan over multiple steps, these issues become more dangerous than just producing bad text. Agents can be tricked by prompt injections, leak private info, misuse connected tools, or act contrary to system guidelines.
Promptfoo extends production observability by testing for known failure modes ahead of release. Vulnerabilities found can be turned into repeatable tests by teams and re-ran after any modifications to models, prompts, tools, or guardrails. Mostly a test and evaluate decision, and not a replacement for production tracing.
Ideal for: Engineering and security teams who require repeatable assessments, adversarial testing, and AI red-teaming as part of their development process.
Industry Experts’ Opinions on AI Observability
When we think about the value of observability, it isn’t just about product features. It’s increasingly becoming a fundamental engineering practice for teams working on AI agents.
James Newton-King of Microsoft wrote in an OpenTelemetry article on generative AI observability: “without observability, you are guessing.” The bottom line: without knowing how many calls to a model were made, which tools were used, how many tokens were used, and how long each operation took, it can e difficult to pinpoint what really went wrong.
Kevin Royer, Staff ML/AI Engineer at Vanta, described the value of LangSmith’s Turned Evaluators: “This out-of-the-box evaluator gave us a safety net to catch failure modes from day one, while we built out our own business-specific evaluators.” His remark underlines the benefit of a blend of pre-designed assessments and those customised to meet an organisation’s specific needs.
The Eval Manifesto from Braintrust states that observability and evaluation should be integrated and not divided. These different perspectives collectively lead to a key principle: teams have to understand agent behaviour, measure its effectiveness, and use that knowledge to improve the system iteratively.
Upcoming Advancements: The Road to AI Agents Observability
The subsequent stage of AI agent observability will most probably be centred on automatic evaluation, proactive failure detection, stronger security testing, and more standardised telemetry. Some of these capabilities are already emerging, while others remain active areas of development.
Production traffic is also more feasible to evaluate continuously. Rather than only sampling the output of a system before release, teams can evaluate living interactions and surface issues like confusion, policy violations, and quality drift. Managed evaluators, such as LangSmith’s Tuned Evaluators, illustrate how specialised evaluation models may reduce the cost of monitoring quality at scale.
Automation of debugging and fixes- another new trend—system can analyse traces, pinpoint repeated failures, suggest code changes or prompt changes, and verify them using evaluation sets. The LangSmith Engine v2, for example, already offers proactive automatic bug discovery and automatic confirmation of the suggested fix- but the solution should be checked by a human for sensitive changes.
OpenTelemetry GenAI conventions are also important for interoperability. They help represent model calls, token usage, and agent operations in a common telemetry format that can be consumed by compatible tools. As they are still evolving, check the current state of GenAI-specific conventions, don’t assume all conventions or attributes are final.
Finally, security testing will probably be embedded into the agent lifecycle. Once agents get into browsers, code repositories, enterprise databases, and business applications, organisations will have to test for prompt injection, tool permissions, data leakage, and unsafe sequences of actions- not simply whether the text generated makes sense.
Conclusion
The most effective AI Agent Observability and Evaluation tools go beyond logging and token counting. These tools provide insight into agent behaviour, determine whether it is accomplishing what it was meant to accomplish, and reduce failures throughout the process.
LangSmith and Braintrust excel in integrated evaluation workflows; Langfuse and Arize Phoenix provide flexible observability; Datadog is right for teams that want to bring enterprise monitoring into their workflow; and DeepEval, Weave, Galileo, AgentOps, and Promptfoo are each focused on some permutation of testing, evaluation, and execution observability.
No individual platform is best for every organization. Prior to selecting, teams should trial authentic agent tasks, benchmark trace depth, evaluate evaluation consistency, examine data-governance policies, and compare costs at meaningful volume. First and foremost, they should define success criteria and leverage production results to enhance evaluation datasets.
As agentic AI becomes integral to critical business workflows, continuous assessment and observability will be critical requirements for creating trustworthy, safe, and responsible AI.
