GITNUXSOFTWARE ADVICE
Business FinanceTop 10 Best Agent Monitoring Software of 2026
AgentOps, Weave, and Datadog LLM Observability are ranked in a factual list of agent monitoring software for teams tracking performance and compliance.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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AgentOps is the best pick for agent teams that need trace-level monitoring with automated reporting and clear ownership when sessions fail, while Weave fits ML teams already using wandb who want agent performance tracking from experiment context.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
AgentOps
Run timeline correlation across model, tools, retries, and outcomes for trace-driven debugging.
Built for fits when agent teams need trace-level monitoring with automated reporting and clear ownership across failures..
Weave
Editor pickRun-linked evaluation artifacts that attach scoring results directly to the triggering agent trace timeline.
Built for fits when ML teams need agent performance tracking tied to existing wandb experiment context..
Datadog LLM Observability
Editor pickLLM evaluation results can be queried and alerted on using the same trace and log correlation context as runtime calls.
Built for fits when engineering teams need LLM agent telemetry correlated with traces and automated evaluations..
Related reading
Comparison Table
Agent monitoring software records agent and LLM execution traces, evaluates outputs against quality rules, and surfaces latency, errors, and cost signals. This ranked list targets teams comparing agent observability stacks by integration depth, data modeling, provisioning, RBAC, and audit log coverage across evaluation and production monitoring workflows.
AgentOps
vertical specialistAgentOps records, evaluates, and monitors AI agent sessions with traces and developer analytics.
Run timeline correlation across model, tools, retries, and outcomes for trace-driven debugging.
AgentOps is built around end-to-end agent activity monitoring, so it can connect conversation flow with intermediate steps like tool calls, retries, and completion states. It provides structured run data for performance tracking and quality management workflows that depend on consistent evaluation signals. Automation and API access enable pipelines for surfacing regressions, creating scorecard datasets, and routing findings to the right owners. This depth makes AgentOps a fit when monitoring needs to reflect the actual agent execution graph rather than only front-end transcripts.
A practical tradeoff is that monitoring fidelity depends on instrumenting the agent runtime consistently across services. Teams also need governance discipline to keep tracking configuration aligned with prompt changes and tool contracts. AgentOps works best when agent behavior issues must be reproduced from telemetry and traced back to specific decision points, not just summarized after the fact.
- +End-to-end run telemetry links model calls to tool steps
- +API and automation surface supports external reporting pipelines
- +Configurable tracking rules align monitoring with deployment workflow
- +Structured event timelines speed root-cause analysis
- –Consistent instrumentation is required across every agent entrypoint
- –Governance overhead increases when tracking rules evolve frequently
- –Complex agent graphs can create busy timelines without filtering
- –Some teams may need engineering help to refine signal quality
AI platform teams
Diagnose agent regressions from run telemetry
Faster rollback decisions
Quality management teams
Track outcome quality over time
Better QA calibration
Show 2 more scenarios
Customer support ops
Triage agent errors by root cause
Reduced escalations
Run outcomes and intermediate events narrow repeat issue clusters.
Governance and compliance leads
Audit agent behavior with trace records
Stronger accountability
Event trails provide structured evidence for review workflows.
Best for: Fits when agent teams need trace-level monitoring with automated reporting and clear ownership across failures.
More related reading
Weave
enterpriseWeave traces, evaluates, and monitors LLM applications and agent workflows.
Run-linked evaluation artifacts that attach scoring results directly to the triggering agent trace timeline.
Weave organizes agent monitoring around per-run timelines and structured events, so traces can be inspected alongside prompts, inputs, and downstream tool interactions. Evaluations and scoring artifacts can be linked back to runs, which helps when the goal is performance tracking across prompt or policy changes. The integration depth with the broader wandb ecosystem reduces the work of aligning agent metrics with existing experiment naming, tagging, and grouping.
A tradeoff is that Weave’s monitoring depth depends on trace quality, so missing tool-call or prompt instrumentation limits what can be analyzed later. This works best when engineering teams can standardize instrumentation for agents and then review failures through consistent trace views during iterative development.
- +Trace timelines link tool calls, prompts, and outputs for fast failure isolation
- +API supports programmatic run logging for automation and batch evaluations
- +Evaluation artifacts stay connected to the exact run that produced them
- +Deep integration with wandb experiment workflows reduces duplicated telemetry
- –Meaningful insights require consistent instrumentation across agent tools and prompts
- –Agent monitoring views prioritize trace inspection over contact-center style dashboards
ML platform teams
Regression testing agent tool workflows
Faster iteration on prompt policies
Experimentation engineers
Compare agent behaviors across experiments
Clear performance deltas
Show 2 more scenarios
QA for agent systems
Debug failed tool calls
Reduced investigation time
Timeline inspection shows prompts, tool inputs, and outputs to pinpoint the failure boundary.
Research teams
Measure quality under varied prompts
More reproducible findings
Evaluation outputs link back to the exact run, enabling targeted analysis of prompt variants.
Best for: Fits when ML teams need agent performance tracking tied to existing wandb experiment context.
Datadog LLM Observability
enterpriseDatadog LLM Observability tracks AI application traces, agent workflows, latency, errors, and costs.
LLM evaluation results can be queried and alerted on using the same trace and log correlation context as runtime calls.
Datadog LLM Observability builds on the Datadog trace and log model, so LLM interactions become first class signals that can be filtered by service, environment, and deployment. It captures structured metadata for prompts, responses, and tool invocations, then ties those fields to the same correlation identifiers used for application telemetry. Automated evaluation jobs can run against captured prompts to detect quality drift and regressions over time. Governance is handled through Datadog access controls, so teams can restrict who sees evaluation results and trace data based on workspace permissions.
A key tradeoff is that full coverage depends on instrumentation paths that emit traces and logs from the LLM integration points. Without that wiring, agent step monitoring may show gaps even when infrastructure monitoring works. A strong usage situation is a platform engineering team debugging a multi service agent where prompt changes must be tied to backend errors and throughput changes in the same investigation.
- +Correlates LLM spans with app traces and logs for faster root cause
- +Evaluation runs use the same queryable telemetry to catch quality regressions
- +Structured metadata supports filtering by model, environment, and workflow step
- +Alerting works on observable LLM signals alongside infrastructure metrics
- –Requires consistent instrumentation at every LLM call site
- –Tool call and prompt fields rely on correct schema mapping from integrations
- –Cross system traces need shared identifiers across services to stay linked
- –Governance setup can be complex for multi team workspace layouts
Platform engineering teams
Debugging agent prompt regressions fast
Reduced time to root cause
SRE and reliability teams
Alerting on LLM behavior drift
Earlier quality and reliability detection
Show 2 more scenarios
Data and evaluation owners
Running repeatable quality checks
More stable rollout decisions
Execute evaluation jobs over recorded prompts to track improvements and regressions across releases.
Security and governance teams
Restricting visibility into LLM traces
Tighter access control for sensitive data
Apply workspace permissions so access to prompts, responses, and evaluation results follows RBAC controls.
Best for: Fits when engineering teams need LLM agent telemetry correlated with traces and automated evaluations.
Opik
open-sourceOpik is an open-source platform for tracing, evaluating, and monitoring LLM and agent applications.
Evaluation and calibration workflows that apply the same scoring artifacts across agents, then surface supervisor-ready results.
Opik from comet.com is agent monitoring software that focuses on conversation evaluation workflows, not just recording storage. It supports automation for collecting transcripts and running scoring with configurable evaluation artifacts.
Opik also provides governance-oriented reporting for supervisors who need consistent scorecards across agents and time windows. The product’s integration story centers on connecting monitoring output to external systems through an API and event-driven use cases.
- +Configurable evaluation forms that standardize agent quality scoring
- +API enables automation around ingestion, scoring, and results sync
- +Supervisor dashboards support calibration and trend review
- +Workflows support consistent re-scoring across evaluation cycles
- –Setup requires careful alignment between evaluation criteria and agent workflows
- –Reporting depth depends on disciplined configuration of scorecards
- –Less coverage for telephony-specific analytics compared to CTI-first tools
- –Advanced automation needs more engineering attention than template-based products
Best for: Fits when teams need consistent, scorecard-driven conversation evaluation with API automation for ongoing governance.
Arize Phoenix
enterpriseArize Phoenix monitors LLM and agent traces, evaluations, retrieval quality, and model behavior.
Phoenix uses evaluation pipelines that can score agent runs and attach the results back to the underlying trace context.
Arize Phoenix collects traces, agent tool calls, and message events from LLM-powered systems into one monitoring view. It focuses on evaluating conversations with configurable quality checks and provides grounded, trace-to-text drilldown for supervisors.
The agent monitoring workflow is supported by an evaluation layer that can score runs and segment failures by model, prompt, and route signals. Admin users get governance controls through organization-level access patterns and auditability of evaluation artifacts.
- +Trace-to-conversation drilldown links tool calls to the exact message sequence
- +Configurable evaluation runs produce repeatable quality scoring for agent outputs
- +Segmentation by prompt and routing signals speeds root-cause for regressions
- +API-first ingestion supports custom agent event formats and integrations
- –Evaluation configuration can require careful mapping of agent events to scoring logic
- –Advanced governance workflows need deliberate setup for multi-team environments
- –High-volume monitoring may require tuning ingestion and retention to manage throughput
- –Some workforce and contact-center reporting needs extra integration work
Best for: Fits when teams need trace-grounded agent monitoring with configurable evaluation scoring and fast failure segmentation.
Braintrust
enterpriseBraintrust provides tracing, evaluation, datasets, and production monitoring for AI agents.
Dataset-driven evaluation runs that reuse the same agent configuration against fixed test sets for controlled QA.
Braintrust is an agent monitoring and evaluation workspace that focuses on tracing and scoring AI agent runs end to end. It captures prompts, tool calls, and outputs in a way that supports supervisor-style review and repeatable quality checks.
The core differentiator is its experiment and dataset workflow for running the same agent configurations against fixed evaluation sets. Admin oversight is centered on project boundaries, role-based access, and audit visibility around monitoring artifacts.
- +Experiment and dataset workflow ties evaluation runs to specific agent versions
- +Trace history includes prompts, tool calls, and model outputs for review
- +Scorecards and evaluation templates support consistent QA across teams
- +API-first integration supports pushing traces and evaluations into monitoring
- –Requires disciplined event taxonomy to keep traces queryable at scale
- –Advanced review views depend on consistent metadata across agent runs
- –Governance is project-centric, so cross-project oversight needs extra process
- –Deep workflow coaching features are limited compared with contact-center suites
Best for: Fits when teams need repeatable agent evaluations with trace-level review and scorecards.
Galileo
enterpriseGalileo monitors generative AI and agent quality with evaluations, guardrails, and production analytics.
Evaluation configuration can be tied to custom automation triggers through Galileo’s API surface.
Galileo provides agent monitoring focused on turning unstructured agent interactions into structured coaching and governance signals. It supports conversation-level capture, scoring workflows, and supervisor review views so QA teams can track consistency across shifts.
Galileo also emphasizes extensibility through APIs and event-driven automation so monitoring logic can connect to existing systems. Admin controls concentrate around evaluation configuration, user roles, and review auditing for traceable change management.
- +Conversation scoring workflows link agent transcripts to actionable QA outcomes.
- +Automation and API hooks support custom monitoring rules and external sync.
- +Supervisor dashboards organize evaluations by agent, team, and time windows.
- +Evaluation change tracking supports audit-friendly governance of scoring logic.
- –Deeper setup is needed to map custom evaluation criteria to real operations.
- –Screen and desktop capture coverage depends on the agent channel and integration.
- –Alerting granularity is less flexible than tools built around bespoke routing.
- –Large transcript review can feel slower without disciplined sampling.
Best for: Fits when QA teams need consistent agent evaluations plus automation that feeds governance and reporting.
Lunary
SMBLunary provides monitoring, prompt management, evaluations, and analytics for LLM applications and agents.
Timeline-based run trace that links each tool call and intermediate state to the final agent result.
Lunary monitors autonomous agent activity with an execution trace that ties tool calls, inputs, outputs, and outcomes into a single timeline. The core workflow centers on capturing runs, inspecting failures, and comparing behavior across versions of prompts and agent code.
It also provides alerting hooks and integration options that fit monitoring and QA pipelines for production agents. Lunary is positioned for teams that need operational visibility beyond raw logs when agents run multi-step tasks.
- +Trace view connects tool calls to each agent run outcome
- +Run comparisons help pinpoint regressions across prompt changes
- +API supports programmatic ingestion of run events
- +Alerting flags failed runs for faster triage
- –Deep debugging depends on consistent instrumentation in agent code
- –RBAC and governance controls are not as granular as enterprise SIEM
- –High-volume tracing can create throughput and retention overhead
- –Limited built-in contact-center specific dashboards for CX metrics
Best for: Fits when teams need execution traces and run diffs for production agents.
Helicone
API-firstHelicone offers gateway-based logging, tracing, analytics, cost controls, and alerts for AI applications.
Agent run evaluation hooks that attach scoring outputs directly to individual traces for regression tracking.
Helicone monitors AI agent interactions by capturing prompts, tool calls, model outputs, and evaluation signals into a searchable workflow timeline. It focuses on operational observability for agent runs, including traces, structured metadata, and configurable retention of run artifacts.
Helicone also adds QA instrumentation so teams can compute scorecards from conversations and track regressions across agent versions. Its agent-centric integration surface supports programmatic submission of telemetry and consistent tagging for cross-system analytics.
- +Captures agent run telemetry from prompts through tool calls to final outputs
- +Supports evaluation-driven scorecards tied to specific agent runs
- +Enables structured tagging so dashboards slice by workflow and environment
- +Provides an extensibility path for custom instrumentation and metadata
- –Depth of agent coverage depends on instrumentation placed around tool orchestration
- –Governance controls for multi-team separation are not as granular as audit-first setups
Best for: Fits when teams need traceable agent run monitoring with evaluation scorecards and repeatable tagging across environments.
Portkey
API-firstPortkey provides an AI gateway with observability, routing, guardrails, and reliability controls.
Run traces that link prompt context, intermediate tool calls, and final outputs for agent-level debugging.
Portkey (portkey.ai) is agent monitoring software focused on observing LLM agent runs across prompts, tool calls, and model responses. It provides run-level traces that make it easier to spot failures like tool errors, JSON formatting issues, and retry loops.
Portkey also supports automated evaluation hooks so teams can score agent outputs against rubric-style criteria during test runs. Admins can use configuration controls to manage what gets captured in traces and which workloads produce telemetry.
- +Run-level traces connect agent prompts, tool calls, and model outputs
- +Automated evaluation hooks support rubric scoring during controlled runs
- +Granular trace capture controls reduce noise in long agent sessions
- +Extensible instrumentation fits custom agents with varied toolchains
- –Tool and trace data completeness depends on consistent instrumentation
- –Operational troubleshooting can be harder when agents generate large tool graphs
- –Some governance controls require careful mapping of captured fields to policies
- –Threading multi-agent workflows into one view can take extra configuration
Best for: Fits when teams need agent run traces plus evaluation automation for quality review workflows.
Conclusion
After evaluating 10 business finance, AgentOps stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right agent monitoring software
Agent monitoring software records agent execution and ties it to outcomes so teams can trace failures across model calls and tool steps. This guide covers AgentOps, Weave, Datadog LLM Observability, Opik, Arize Phoenix, Braintrust, Galileo, Lunary, Helicone, and Portkey based on how each tool links traces, evaluations, and automation.
The practical differences show up in trace correlation depth and the automation surface for evaluation runs. AgentOps emphasizes timeline correlation across model calls, tools, retries, and outcomes, while Weave attaches scoring artifacts directly to the triggering agent trace timeline.
Agent monitoring software that tracks agent execution traces, quality scoring, and governance-ready evaluations
Agent monitoring software captures agent runs and records the chain from prompt context through intermediate tool calls to the final output so teams can audit behavior and debug failures. Many systems also add evaluation pipelines that score runs and attach results back to the trace context for regression tracking and operator workflows.
AgentOps focuses on run telemetry that connects model calls to tool steps and uses an API and automation surface for external reporting pipelines. Datadog LLM Observability adds LLM evaluation results that use the same trace and log correlation context as runtime calls, but it depends on correct schema mapping from integrations.
Agent execution trace correlation and evaluation automation
Agent monitoring software should connect the chain of events from prompt context to intermediate tool calls and the final output so failures can be traced to the specific step that caused them. The strongest tools also attach evaluation results back onto the run timeline so teams can separate production regressions from isolated prompt or tool issues.
Trace-driven debugging across model calls and tool steps
AgentOps correlates model, tools, retries, and outcomes into a single timeline so trace inspection shows exactly which step changed behavior. Lunary provides a timeline-based run trace that links each tool call and intermediate state to the final agent result.
Evaluation artifacts attached to the triggering trace timeline
Weave links scoring results directly to the triggering agent trace timeline so evaluation and failure isolation happen in the same view. Helicone attaches scoring outputs directly to individual traces to support regression tracking across runs.
LLM evaluation runs that query against the same runtime telemetry
Datadog LLM Observability runs LLM evaluations using the same trace and log correlation context as runtime calls so quality regressions can be caught with telemetry queries and alerts. Datadog LLM Observability also correlates LLM spans with app traces and logs for faster root cause.
Scorecard workflows with calibration and governance-ready outputs
Opik uses evaluation and calibration workflows that apply the same scoring artifacts across agents and then surface supervisor-ready results. Opik also offers configurable evaluation forms to standardize agent quality scoring for ongoing governance.
Trace-grounded evaluation pipelines with repeatable scoring
Arize Phoenix uses evaluation pipelines that score agent runs and attach results back to the underlying trace context for repeatable quality scoring. Braintrust supports dataset-driven evaluation runs that reuse the same agent configuration against fixed test sets for controlled QA.
Choose based on trace coupling, evaluation governance, and automation surface
The main fork is how tightly the platform couples runtime traces to evaluation runs, because weak coupling forces teams to reconcile traces and scores outside the monitoring view. The second fork is whether automation and external reporting depend on a tool-first API surface or on a dataset and scorecard workflow that teams standardize before scaling governance.
Map the monitoring workflow to the platform’s trace-to-evaluation coupling
AgentOps ties model calls, tool steps, retries, and outcomes into trace-driven debugging so engineering teams can pinpoint step-level failures. Weave and Helicone attach scoring outputs directly to the triggering trace so QA and engineering can review regressions without switching contexts.
Verify that evaluation can be executed and queried in the same telemetry context
Datadog LLM Observability uses the same trace and log correlation context for LLM evaluation results so evaluations can be queried and alerted on like runtime telemetry. Arize Phoenix and Portkey attach evaluation hooks to trace context for controlled scoring workflows.
Decide whether governance depends on scorecard standardization or dataset-controlled QA
Opik emphasizes configurable evaluation forms plus calibration workflows so supervisor-ready outputs reflect standardized scorecards. Braintrust emphasizes dataset-driven evaluation runs against fixed test sets so the same agent version can be tested under controlled conditions.
Assess the automation and integration surface for operational reporting
AgentOps and Weave both provide an API and automation surface for external reporting pipelines, which supports batch evaluations and programmatic logging. Galileo offers evaluation configuration tied to custom automation triggers so governance and reporting can be driven by custom monitoring rules.
Check whether coverage gaps exist for the channels and capture types used by agents
Galileo’s screen and desktop capture coverage depends on the agent channel and integration, which matters when capture-based QA is required. Tools that focus on trace graphs can still support evaluation, but capture coverage may not match contact-center style requirements.
Plan instrumentation discipline for consistent event taxonomy at scale
AgentOps and Datadog LLM Observability depend on consistent instrumentation across every agent entrypoint or LLM call site, which impacts throughput of trace generation and evaluation fidelity. Lunary and Braintrust also depend on consistent metadata so run diffs and dataset comparisons stay queryable.
Who agent monitoring tools fit best
Agent monitoring software fits teams that need repeatable investigation paths from an agent run to the exact step that produced an outcome. The right tool depends on whether monitoring is driven by engineering trace telemetry or QA scorecard and calibration workflows.
Agent platform and engineering teams running tool-using agents in production
AgentOps and Lunary support execution traces that connect tool calls and intermediate states to final outcomes, which accelerates step-level debugging when tool orchestration fails.
ML teams already using wandb experiment runs and evaluation artifacts
Weave ties agent scoring results to the triggering agent trace timeline and aligns run logging with existing wandb experiment context, which reduces duplication between experiment tracking and production evaluation.
Engineering organizations using trace and log observability pipelines for alerting
Datadog LLM Observability correlates LLM spans with app traces and logs and uses the same context for evaluation results, which supports alerting on quality regressions through established telemetry workflows.
QA teams standardizing evaluation criteria across agents
Opik provides configurable evaluation forms plus calibration workflows so supervisors review standardized scorecard outputs across agent programs.
Teams running repeatable evaluations against fixed test sets
Braintrust centers dataset-driven evaluation runs that reuse the same agent configuration against fixed test sets, which makes controlled QA repeatable across releases.
Common agent monitoring mistakes and how to avoid them
Most failures come from instrumentation inconsistency or from evaluation setup that does not match how agent events actually occur in production. Other mistakes come from assuming governance depth will appear automatically after evaluation runs start generating scores.
Assuming trace-level monitoring works without consistent instrumentation at every agent entrypoint
AgentOps and Datadog LLM Observability both require consistent instrumentation across agent entrypoints or LLM call sites, or tool call and prompt fields will be incomplete and evaluation attribution becomes unreliable.
Building scorecards that do not align with real agent workflows and event sequences
Opik requires careful alignment between evaluation criteria and agent workflows so calibration produces meaningful supervisor-ready results instead of mismatched scoring.
Treating trace views as a substitute for multi-team governance controls
Lunary and Helicone provide traceable evaluation and tagging, but governance controls for multi-team separation are not as granular as audit-first enterprise setups, which can cause review access problems.
Overloading production troubleshooting with very large tool graphs without a clear review path
Portkey run-level traces connect prompts, tool calls, and model outputs, but troubleshooting can get harder when agents generate large tool graphs because operators must filter more events to find the root cause.
Confusing evaluation trace attachments with queryability for alerts
Datadog LLM Observability supports query and alert workflows by using the same trace and log correlation context for evaluation results, while other tools may attach evaluation to traces without exposing the same telemetry query model for alerting.
How We Selected and Ranked These Tools
We evaluated how each platform correlates agent execution traces with evaluation outputs and how quickly teams can isolate failures from model calls to tool steps. Features accounted for 40% of the scoring because AgentOps earns top placement by linking timeline correlation across model, tools, retries, and outcomes for trace-driven debugging.
Ease and value each accounted for 30% because consistent instrumentation effort affects runtime trace fidelity and evaluation turnaround for teams operating agent programs. AgentOps was ranked highest because its API and automation surface supports external reporting pipelines built on trace-linked run telemetry.
Frequently Asked Questions About agent monitoring software
How does trace correlation differ between AgentOps and Datadog LLM Observability?
Which tools connect monitoring outputs to external systems via API and events?
How does Weave’s workflow for evaluation artifacts differ from Arize Phoenix?
When teams need dataset-driven repeatability for agent QA, which tool fits best: Braintrust or Lunary?
What breaks if an agent monitoring setup cannot capture intermediate tool states?
How do admin controls and auditability show up across Braintrust and Arize Phoenix?
Which tool is built for conversation evaluation workflows rather than storing recordings: Opik or Helicone?
How does scheduling and orchestration metadata relate to schedule adherence use cases in these tools?
Where does extensibility via configuration and events tend to matter most: Galileo or Opik?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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