Best AI agent analytics tools (2026): see trends across every agent answer - Articles - Braintrust
Best AI agent analytics tools (2026): see trends across every agent answer
TL;DR: best AI agent analytics tools in 2026
Production agents can repeat refund, tool-use, or incomplete-answer failures across thousands of conversations before anyone can see which issues are growing. Engineering teams may debug individual traces and review sampled conversations while recurring user intents, negative sentiment, failed tool calls, and incomplete answers continue to reach users.
AI agent analytics reads production answers, classifies each answer, and groups similar behavior by task, sentiment, issue type, or product-specific dimensions. It helps engineering and product teams understand agent behavior across production traffic and open the source traces behind each recurring issue.
This guide compares five AI agent analytics tools across classification quality, filtering depth, trace review, evaluation handoff, and pricing at scale. Braintrust is the strongest choice because its Topics analytics layer classifies every answer across Task, Sentiment, and Issues, supports custom facets, and turns recurring production issues into datasets and scorers for release control.
What AI agent analytics means
AI agent analytics groups production answers by meaning, so product and engineering teams can review patterns across the full volume of conversations. It sits next to two layers most teams already run, observability and product analytics, and the table below shows where each layer helps and where it falls short for agent teams.
| Layer | What it shows | Where it helps | Where agent teams need more |
|---|---|---|---|
| Observability | The trace of one request across prompts, retrieval, tool calls, and the final answer | Debugging a specific conversation or failure | Trace review does not show how often the same issue appears across production traffic |
| Product analytics | Events selected in advance, such as clicks, conversions, or completed workflows | Measuring known user actions and funnel movement | Unexpected intents, sentiment changes, and response failures can stay hidden when no event exists for them |
| AI agent analytics | Classifications across production answers, grouped by meaning and behavior | Finding recurring patterns across large conversation volumes | The strongest analytics workflows connect those patterns to source traces, review queues, datasets, or scorers |
For example, a support agent team could see that refund requests account for a large share of traffic, that checkout conversations often involve negative sentiment, and that account-update failures stem from the same tool call. AI agent analytics gives the team both the aggregate pattern and the source conversations behind it, so production behavior can feed directly into review and evaluation work.
What to look for in an AI agent analytics platform
Use the criteria below to compare how each tool finds patterns in production answers, connects those patterns to source traces, and turns recurring issues into evaluation work.
- Automatic classification: The tool should classify agent answers automatically, without requiring engineers to define every category first.
- Multiple classification dimensions: A useful analytics layer should analyze the same answer through more than one lens, such as user task, sentiment, and response issue.
- Custom dimensions: Built-in classifications cover common patterns, but product-specific questions often need custom categories.
- Combined filtering: The tool should support filtering across multiple dimensions at once.
- Source trace review: Aggregated patterns are useful only when reviewers can access the underlying conversations.
- Evaluation workflow connection: A recurring production issue should be easy to turn into an evaluation dataset, scorer, or review queue.
- Pricing at production scale: Agent analytics should remain practical as conversation volume grows.
The 5 best AI agent analytics tools in 2026
1. Braintrust
Best for: Engineering and product groups that need automatic classification across production agent answers.
Braintrust is an AI observability and evaluation platform for teams shipping LLM applications and agents to production. Its agent analytics layer, Topics, reads production traces, classifies agent behavior, and groups similar answers into named patterns.
Automatic classification with Topics
Topics format each trace into readable text from messages, tool calls, nested spans, and the final response. Braintrust then extracts summaries across Task, Sentiment, and Issues.
Built-in and custom facets
Topics ships with three built-in facets for core agent analytics. Task identifies the user's goal, Sentiment captures the emotional tone of the interaction, and Issues flags agent behavior problems.
Trace collection, review, and collaboration
Braintrust supports trace collection and review for agent analytics. Native SDKs, OpenTelemetry support, and the Braintrust gateway help teams collect production traces from different AI stacks.
Pros
- Topics automatically classify production answers.
- Custom facets capture product-specific dimensions.
- Logs filtering and SQL support combined analysis across topics.
Cons
- Topics work best after a project has enough logged traces.
- Custom facets require a Pro or Enterprise plan.
Pricing: Free includes 1 GB processed data, 10k scores, and unlimited users. Pro is $249/month and includes more features.
2. Galileo
Best for: Teams that need evaluation and runtime guardrails for customer-facing AI agents.
Galileo is an AI observability and evaluation system for GenAI applications that focuses on evaluating traces and monitoring production behavior.
Pros
- Supports production trace evaluation.
Cons
- Trend exploration is operator-driven.
Pricing: Free tier with 5,000 traces/month; paid plan starts at $100/month.
3. HoneyHive
Best for: Smaller teams that want tracing and evaluation in one agent-focused system.
HoneyHive provides tracing, trajectories, experiments, and dashboards for teams.
Pros
- OpenTelemetry-native tracing.
Cons
- Smaller team and integration ecosystem.
Pricing: Free tier available with a single workspace, up to 5 users.
4. Datadog LLM Observability
Best for: Teams already using Datadog who want LLM traces and error tracking.
Datadog LLM Observability extends Datadog monitoring to LLM applications.
Pros
- Tracks latency, token usage, retries, and errors.
Cons
- Does not center on the automatic classification.
Pricing: Free tier with 40K LLM spans per month; LLM Observability Pro starts at $160/month.
5. Langfuse
Best for: Cost-sensitive teams wanting open-source LLM observability.
Langfuse provides an open-source system for LLM tracing and evaluations.
Pros
- Open source with a self-hosting option.
Cons
- No automatic answer classification.
Pricing: Free self-hosting and a free cloud plan with limits; paid plan starts at $29 per month.
Quick comparison: best AI agent analytics tools (2026)
| Capability | Braintrust | Galileo | HoneyHive | Datadog LLM Observability | Langfuse |
|---|---|---|---|---|---|
| Automatic answer classification | Topics classify every answer | No per-answer classification | Tag-driven classification | No per-answer classification | Requires custom classification logic |
| Multi-facet analysis | Built-in Task, Sentiment, and Issues | Metric-led analysis | Limited user-defined dimensions | Depend on tags and dashboards | Depends on user-defined tags |
| Custom dimensions | Custom facets for domain-specific signals | Custom logic available | Custom tags and annotations | Custom tags and facets | Custom tags, scores, and SQL views |
| Combined filtering | Filter Logs by topic, facet, scores | Filter inside evaluation views | Filter sessions, traces, annotations | Filter via tags and dashboards | Filter via UI and SQL |
| Source-trace review | Source traces stay connected | Distributed tracing | Trajectory traces | Full request traces | Trace and session review |
| Evaluation handoff | Topics feed datasets, scorers, experiments | Eval workflows | Online evaluation workflows | Not primary focus | Datasets and experiments |
| OpenTelemetry support | OTEL-based tracing supported | OTEL integrations available | OpenTelemetry-native | OTEL ingestion | OpenTelemetry-native |
| Self-hosting | Enterprise self-hosting and hybrid deployment | Enterprise deployment options | Managed offering | SaaS only | Free self-hosting available |
| Free plan | 1 GB processed data, 10k scores | 5,000 traces/month | Single workspace, up to 5 users | 40K LLM spans/month | Free cloud plan with 50K units |
| Starting paid price | $249/month (Pro) | $100/month | Custom | From $160/month for 100K spans | $29/month |
Choosing the right AI agent analytics tool
The right tool depends on which gap your team needs to close first, but Braintrust is the best option for most teams evaluating AI agent analytics as defined in this guide.
Choose Braintrust if your team needs to catch recurring problems in production before the next release.
Choose Datadog LLM Observability if your AI workload already runs inside Datadog and your main priority is keeping LLM traces beside infrastructure metrics.
Choose Langfuse if you are self-hosted, cost-sensitive, and willing to build your own analytics workflow.
Choose Galileo if runtime protection is your first requirement and your procurement process favors enterprise plans.
Choose HoneyHive if you run an agent-heavy workload on a smaller team.