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The right AI agent observability tool is the one that captures your agent’s real execution tree, exposes useful AI fields, supports the evaluations your team runs, and fits your deployment and data requirements. No platform is universally best. Shortlist candidates by framework and language support, trace completeness, evaluation workflow, deployment model, retention and access controls, and the way they bill for ingestion or usage. Then run the same representative agent workflow through each candidate before committing.
This guide explains what to inspect, how to compare common options, and how to structure a proof of concept that reveals missing spans, unusable fields, operational work, and unexpected costs.
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What is AI agent observability, and why is it different from ordinary application monitoring?
AI agent observability records and explains an agent’s decisions and actions across a complete run. A useful trace links the user request to model calls, planning steps, retrieval, tool invocations, retries, handoffs, errors, latency, and token consumption. The result should be a navigable parent-child tree rather than a flat stream of log lines.
Traditional application monitoring can show that an API request was slow or returned an error. Agent observability must also show why the agent made a particular call, what prompt and model response preceded a tool invocation, which tool arguments were sent, what came back, and where the run diverged from the intended workflow. This context is essential when an agent loops, selects the wrong tool, produces an invalid argument, or appears successful while delivering a poor answer.
NVIDIA’s AI-Q documentation describes observability backends that trace “agent execution, LLM calls, tool invocations, and token usage.” Treat that as a capability to verify in your own instrumentation, not as a guarantee that every backend will display every field in the same way.
How should I evaluate trace completeness?
Start by proving that a platform preserves the execution hierarchy your framework emits. A trace that contains only a final model response cannot explain an agent failure, even if it is technically stored successfully.
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Send a workflow that includes a user request, at least two model calls, a retrieval step, a tool call, a retry, and a deliberate error. In the UI, confirm that the top-level run contains nested spans for each operation and that timestamps and status values are coherent. If your framework exposes parallel branches or handoffs, verify that those relationships remain visible after export.
Inspect the fields that make a trace actionable
- Model provider and model name, including the deployed version or identifier when your system supplies it.
- Prompt and completion content, with configurable masking for secrets and personal data.
- Token counts, estimated or reported cost fields, and latency for each model call.
- Tool name, arguments, return value, timeout, retry count, and final status.
- Retriever or vector-search query, selected documents, scores, and source identifiers when retrieval is part of the run.
- Error type, stack or structured error details, and the span in which the error originated.
- Request, session, user, tenant, and environment identifiers that let operators filter without exposing unnecessary content.
Do not assume that an OpenTelemetry transport automatically supplies these details. Instrumentation libraries can emit different schemas, and backends can map the same attribute differently. Use a real trace to check both the visual tree and the exported record.
Which AI-specific fields should the platform display?
AI fields are useful only when they are searchable, retained at the required granularity, and visible in the views your team actually uses. A backend may accept generic spans while hiding model or tool attributes from its analysis screens.
Verify names, types, and cardinality
Send traces from every model and tool combination in your production design. Confirm that model names are not collapsed into a generic “LLM” label, that token counts are numeric rather than embedded in text, and that tool arguments remain associated with the correct invocation. Check how the platform handles streaming responses, parallel tool calls, retries, and nested agents.
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Test filtering and export
Ask an engineer who did not build the test run to find all failures for one tool, all calls to one model, and all runs exceeding a latency threshold. If those queries require manual inspection, the data may be present but operationally weak. Export several traces and inspect the schema in a neutral parser; fields visible in a dashboard are not necessarily available through an API or data export.
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Separate content visibility from metadata visibility
Some teams need token and latency metrics while prohibiting storage of raw prompts or tool payloads. Determine whether the product can redact selected fields at ingestion, retain metadata while dropping content, and apply different policies by environment or tenant. Record exactly which fields are removed, hashed, truncated, or retained.
Do I need evaluations and experiments in the same product?
If your team is iterating on prompts, models, retrieval, or tools, trace search alone is rarely enough. An evaluation workflow lets you run a fixed dataset, compare versions, annotate outputs, and detect regressions before a change reaches production.
Capabilities to compare
- Dataset creation from production traces or uploaded examples.
- Versioned prompts, model settings, and tool configurations.
- Automated evaluators for correctness, relevance, safety, format, or tool-selection behavior.
- Human review queues with labels, comments, and agreement tracking.
- Side-by-side experiment comparison with links back to the underlying traces.
- Regression thresholds and integrations with continuous-integration or release workflows.
Phoenix documents evaluation, datasets, experiments, tracing, and prompt workflows. Braintrust is a candidate to investigate when experiments and evaluation are central to the development loop. These descriptions identify workflows to examine; they are not independent performance benchmarks.
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Prepare a small, representative set that includes ordinary requests, ambiguous requests, long contexts, tool failures, and known bad outputs. Run it through every candidate with identical prompts and model settings. Compare whether each platform stores the input, output, evaluator result, and trace link together, and note how much manual work is required to explain a score.
How important are framework and language integrations?
Integration fit determines how much instrumentation code you must maintain and whether the resulting traces contain the fields you care about. General support for OpenTelemetry or “agent tracing” does not prove that your chosen SDK, framework, or language emits a complete hierarchy.
Map your actual stack
Write down the agent framework, language runtime, model providers, retrieval layer, tool protocol, queue or workflow engine, and deployment environment. For each candidate, verify an official integration or a documented instrumentation path for that exact combination. Test asynchronous execution, background jobs, streaming, and cross-service calls if your production system uses them.
Measure instrumentation effort
Count the code changes, wrapper functions, environment variables, and custom attributes needed for a useful trace. Identify whether upgrades to the framework or model SDK are likely to require changes in your instrumentation. A platform that looks feature-rich but requires extensive bespoke wrappers can create long-term maintenance cost.
Inspect unsupported operations
During the test run, deliberately exercise a custom tool, a timeout, a retry, and a handoff between agents. Mark every operation that appears as an unconnected span, a generic log, or no event at all. Missing coverage is often more consequential than a missing dashboard feature.
Which deployment and data controls should I compare?
Deployment choice affects operational ownership, network design, incident response, and contractual review. Hosted, self-hosted, and hybrid labels are only starting points; confirm the actual data path and administrative controls.
Hosted services
Ask where telemetry is processed and stored, which regions are available, how long data is retained by default, and whether backups follow a different schedule. Confirm single sign-on, role-based access, audit logs, tenant isolation, export and deletion procedures, and support escalation. Obtain current contractual terms rather than relying on a marketing page.
Self-hosted systems
Self-hosting can provide control over network location and storage, but your team then owns upgrades, scaling, backups, alerting, secrets, vulnerability response, and on-call coverage. Validate the required database, object storage, queue, and compute components, as well as upgrade and migration procedures. A self-hosting option by itself does not establish suitability for regulated or sensitive data.
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Hybrid and collector-based paths
An OpenTelemetry Collector can sit between applications and one or more backends, providing routing, sampling, and redaction. NVIDIA lists the collector as a production infrastructure and enterprise-redaction option. Treat it as a telemetry component, not a one-for-one replacement for a platform that provides datasets, evaluators, or experiment management.
Redaction and residency tests
Send synthetic secrets and personal-data markers through the entire pipeline. Confirm whether redaction occurs before network transmission, at the collector, or only in the backend UI. Test a deletion request, an access-log review, and a region restriction while documenting what remains in caches, exports, backups, and third-party processors.
What does each shortlisted option fit best?
The following are candidates for investigation, not rankings. Product names, integrations, deployment choices, retention, and commercial terms can change, so verify each item against current documentation and your own workflow.
| Candidate | Investigate when | Questions to verify |
|---|---|---|
| LangSmith | Your team builds with LangChain or LangGraph and wants tracing and evaluation in one workflow. | Which framework versions and languages are supported? What deployment options, retention settings, and plan limits apply to your trace volume? |
| Langfuse | You are considering an open-source workflow or self-hosting. | What infrastructure must you operate? Does your exact telemetry path preserve tool and model fields, and how are upgrades handled? |
| Arize Phoenix | You want an open-source observability path using OpenTelemetry and OpenInference instrumentation. | Which features are available in the open-source deployment, and which operational tasks, integrations, or support requirements apply? |
| Arize AX | You are evaluating a commercial offering associated with the Phoenix ecosystem. | Compare it separately from Phoenix for deployment, support, access controls, retention, and commercial terms. |
| Braintrust | Evaluation and experiment management are the center of your development loop. | Can your datasets, evaluators, annotations, and trace links support the release process you use? |
| Datadog Agent Observability | Your organization already operates application and infrastructure monitoring in Datadog. | Which agent instrumentation is supported, how are AI fields represented beside existing APM data, and what usage units apply? |
| OpenTelemetry Collector | You need a controllable transport, routing, or redaction layer for production telemetry. | Which backend will provide AI-specific views and evaluations, and who will operate the collector fleet? |
| Weights & Biases Weave | You want to investigate an observability backend listed among AI-Q-supported options. | Does its current framework coverage and evaluation workflow match your agent, and how are retention and exports handled? |
| Verbose logging | You need a low-dependency diagnostic path for development or a narrow incident. | How will you correlate events, protect sensitive content, control volume, and obtain evaluation or long-term analysis later? |
How should I compare costs when prices and units differ?
Do not compare headline allowances until every candidate is expressed in the same operational units. Current pricing was not established here, and software terms change; request a quote or calculate from each provider’s published billing definition at the time you buy.
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Build a usage model
- Runs per day, average and peak spans per run, and bytes per span.
- Model-call volume, input and output tokens, and the proportion of traces with full content.
- Retention days for hot search, archived data, and evaluation datasets.
- Number of engineers, reviewers, administrators, and service accounts.
- Evaluation executions, human annotations, experiment variants, and API requests.
- Self-hosted infrastructure, support, backups, and on-call time where applicable.
Ask whether sampling, replay, exports, and failed requests count toward usage. Confirm whether retention is measured by event, byte, trace, seat, evaluation run, or another unit. Model a normal month, a launch spike, and a debugging incident in which sampling is temporarily reduced.
Calculate the cost of missing data
A lower invoice is not a saving if engineers cannot find the failing tool call or reproduce a regression. Include the labor required to maintain custom instrumentation, operate collectors, review alerts, and migrate data when a plan or schema changes.
What is the most reliable proof-of-concept process?
Use one realistic workflow and identical data across the shortlist. The goal is to expose differences in trace structure, field visibility, evaluation workflow, operating effort, and billing—not to produce a synthetic feature checklist.
- Select a representative agent. Include the real model provider, retrieval, tools, retries, asynchronous work, and at least one known failure mode.
- Define acceptance criteria. Write down required spans, AI fields, filters, exports, evaluator outputs, redaction behavior, retention, and access roles before testing.
- Instrument through the intended production path. Use the SDK, collector, gateway, or framework integration you would actually deploy. Record code and configuration changes.
- Run success and failure cases. Trigger a timeout, malformed tool argument, model error, retry, and partial downstream outage. Check whether the parent run remains understandable.
- Inspect the hierarchy and fields. Follow each run from the user request to the final answer. Verify model, token, latency, tool, retrieval, and error attributes in both UI and export.
- Test search and access. Have another engineer locate failures by tool, model, tenant, and latency. Test role restrictions, audit records, and deletion or retention controls.
- Run a fixed evaluation set. Compare outputs, evaluator scores, annotations, experiment versions, and links back to traces. Note manual steps and unsupported cases.
- Apply redaction and residency checks. Use synthetic secrets and personal-data markers, then confirm where each value is removed or retained.
- Record operational burden. Document deployment components, upgrades, alerts, backups, incident response, and instrumentation maintenance.
- Model three usage scenarios. Estimate ordinary traffic, peak traffic, and a high-volume debugging period using each vendor’s current billing units.
- Review gaps with stakeholders. Include application engineers, platform operators, security, privacy, and the people who will run evaluations. A technically complete trace may still fail an access or workflow requirement.
What failure modes should I look for before signing?
Most disappointing implementations fail at the boundary between instrumentation and day-to-day use. Look for these symptoms during the proof of concept.
The trace is flat or stops at the model call
Check whether tool and framework instrumentation is installed, whether asynchronous context is propagated, and whether spans are flushed before the process exits. Add explicit parent identifiers around queue boundaries and verify retries do not create unrelated root runs.
AI fields exist in raw data but not in the UI
Inspect the backend’s schema mapping and indexing rules. You may need supported semantic attributes, a version-specific integration, or custom dashboards. If critical fields cannot be filtered or exported, treat that as a functional gap rather than a cosmetic issue.
Prompts or tool payloads are overexposed
Move redaction earlier in the pipeline, reduce content capture for sensitive operations, and use separate policies for development and production. Confirm that exports, backups, and error messages do not reintroduce the original values.
Evaluation scores cannot explain a bad result
Ensure each score is linked to the exact prompt, model settings, retrieved context, tool results, and trace version that produced it. Without that lineage, a numeric score cannot reliably guide a fix.
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Check span duplication, verbose payload capture, retries, export jobs, and evaluation loops. Apply sampling only after deciding which traces must remain complete for audits, incident analysis, or regression investigation.
How do I make the final selection?
Score candidates against explicit requirements instead of adding up marketing checkboxes. A practical decision sheet can assign pass, partial, or fail to each item below, with a written explanation and evidence from the proof of concept.
- Complete parent-child hierarchy for your framework and language.
- Visible, searchable, and exportable model, token, tool, retrieval, latency, and error fields.
- Evaluation datasets, experiments, human review, and regression workflow at the required maturity.
- Instrumentation effort that your team can maintain through SDK and framework upgrades.
- Deployment, residency, retention, redaction, identity, and audit controls that meet policy.
- Operational ownership that fits your platform team’s capacity.
- Predictable billing units under normal, peak, and debugging traffic.
- Migration and data-export options if the product or your requirements change.
Choose the candidate that clears every non-negotiable requirement and has the fewest unresolved risks for your specific workflow. Keep the acceptance traces, schema notes, and cost model with the decision so a future change can be evaluated against the same evidence.
Frequently Asked Questions
Does OpenTelemetry by itself provide AI agent observability?
OpenTelemetry provides a transport and instrumentation model, but it does not guarantee that a backend will capture or display model names, token counts, tool arguments, or evaluation data. You still need framework-specific instrumentation and a backend that maps those attributes into usable views and exports.
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Verbose logs can be effective for a short development investigation or a small service, especially when introducing a new tool. They become difficult to correlate, secure, search, retain, and evaluate as runs become concurrent and multi-step, so define the point at which a structured trace is required.
How much data should an agent trace retain?
Retain enough metadata to reconstruct timing, relationships, model and tool choices, and errors for the period in which incidents and regressions are investigated. Store full prompts, completions, and tool payloads only when the business need and data policy justify them; otherwise use redaction, sampling, hashing, or metadata-only retention.
What should security review before an agent observability rollout?
Security should review data classification, secret and personal-data handling, tenant isolation, identity and role design, network paths, encryption, audit logs, deletion behavior, backups, and subprocessors. The review should cover collectors, exports, evaluation datasets, and support access, not only the main dashboard.
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