Best Tools for Agent Failure Detection

You already know what agent failure detection is.

The harder part is deciding which platform actually fits your stack. Some tools focus on tracing individual runs, others on evaluations and regression testing, while others give you visibility across the agents running throughout your organization.

This guide compares the leading options so you can quickly see where each one fits, and where it doesn’t.

Quick comparison: best tools for agent failure detection

The important distinction is what you want to see.

If you’re debugging one agent execution, tracing and evaluation platforms may be enough. If you’re running agents across multiple teams, frameworks, clouds, and projects, you may need a broader control layer.

That’s where Lyzr OpenController fits.

1. Lyzr OpenController

Most agent observability platforms answer:

What happened during this agent run?

OpenController takes a step back and asks:

What agents are running across the organization, who owns them, what are they using, and what are they costing?

That becomes increasingly important as organizations move from a handful of experiments to dozens or hundreds of agents.

OpenController provides a control layer across your existing AI stack. You don’t need to replace your models, agent frameworks, cloud infrastructure, CI/CD, identity systems, or observability platforms.

Instead, it gives teams a consolidated view of agent activity, model and infrastructure costs, and spend across developers, projects, and agents.

What OpenController is good at

• Agent inventory: Know what agents exist across your organization.
• Agent governance: Understand ownership, access, and usage.
• Agent sprawl: Identify agents being created and run outside established controls.
• Cost visibility: Track model and infrastructure spend across agents, projects, and developers.
• Multi-cloud visibility: Get visibility across different environments instead of managing each one separately.
• Existing-stack compatibility: Keep your current models, frameworks, infrastructure, and observability tools.

Choose OpenController if

• You have multiple teams building and deploying agents.
• You are starting to lose track of how many agents exist.
• Different teams use different agent frameworks or infrastructure.
• You need visibility into model and infrastructure costs.
• You want governance without replacing your existing AI stack.
• You need a single view across agents, developers, and projects.

Where OpenController is different

OpenController isn’t trying to replace the platforms below.

You can still use LangSmith, Langfuse, Phoenix, Braintrust, or Datadog for tracing, evaluation, and debugging.

OpenController sits above that stack to give you visibility and control across the agents themselves.

Observability helps you investigate a run. OpenController helps you understand and control the fleet.

Learn more about Lyzr OpenController.

2. LangSmith

LangSmith is closely integrated with LangChain and LangGraph, making it a natural option for teams already building their agents within that ecosystem.

Its focus is on tracing, debugging, evaluation, and experimentation around individual AI application runs.

What it’s good at

• Tracing LangChain and LangGraph applications
• Debugging individual agent runs
• Evaluating agent outputs
• Creating datasets from application traces
• Testing prompt and workflow changes

Choose LangSmith if

• Your agents already use LangChain or LangGraph.
• Your developers want framework-native debugging.
• You want tracing and evaluations in the same workflow.
• Your main requirement is understanding individual agent executions.

Where it differs from OpenController

LangSmith is primarily concerned with the application and its execution.

OpenController is concerned with the broader agent fleet, what exists, who owns it, how it is being used, and what it costs.

If you’re managing a growing collection of agents rather than only debugging individual runs, that’s an important distinction.

3. Langfuse

Langfuse is an open-source observability and evaluation platform designed for teams that want flexibility across their AI stack.

It provides tracing, evaluations, prompt management, datasets, cost tracking, and production monitoring. It can capture LLM calls, retrieval, tool usage, and other parts of an agent workflow.

What it’s good at

• Framework-agnostic AI observability
• Production tracing
• LLM and agent evaluations
• Cost and token monitoring
• Self-hosted deployments
• Connecting production traces with datasets and experiments

Choose Langfuse if

• You don’t want observability tied to one agent framework.
• Self-hosting is important.
• You want open-source flexibility.
• You need tracing and evaluation in the same platform.
• Your team wants more control over telemetry.

Where it differs from OpenController

Langfuse gives you detailed visibility into AI application behavior.

OpenController adds visibility at the organization level, helping teams understand the agents being built and operated across projects, developers, and environments.

You may use both: Langfuse for detailed traces and evaluations, OpenController for broader agent governance and visibility.

4. Arize Phoenix

Arize Phoenix is built for teams that want detailed engineering-level visibility into AI application execution.

It supports tracing across LLM calls, retrieval, tool calls, and other application steps, with a strong focus on debugging and evaluation.

What it’s good at

• Detailed agent traces
• Root-cause debugging
• RAG and retrieval analysis
• Tool-call analysis
• Evaluation and annotations
• OpenTelemetry-based observability
• Self-hosted deployments

Choose Phoenix if

• Your engineers need detailed traces.
• RAG quality is important.
• Tool-call failures are common.
• You already use OpenTelemetry.
• You want control over evaluation logic.

Where it differs from OpenController

Phoenix helps answer:

“Which step in this execution caused the problem?”

OpenController helps answer:

“Which agents are operating across the organization, and how are they being used?”

They’re solving different levels of the problem.

5. Braintrust

Braintrust takes a more evaluation-focused approach.

The platform is designed around measuring AI application quality, testing changes, and catching regressions as teams modify prompts, models, and workflows.

What it’s good at

• AI evaluations
• Regression testing
• Experimentation
• Comparing model or prompt changes
• Production-to-evaluation workflows
• Quality measurement

Choose Braintrust if

• Evaluation is already part of your development process.
• You frequently change prompts or models.
• Regression detection is important.
• You want evaluation results to influence release decisions.

Where it differs from OpenController

Braintrust focuses heavily on whether the application is producing the expected result.

OpenController focuses on visibility and control across the agents operating within the organization.

So if your problem is model or prompt quality, start with evaluation. If your problem is agent sprawl, ownership, usage, and cost, look at the control layer too.

6. Datadog LLM Observability

If your organization already uses Datadog, its LLM Observability offering is worth evaluating before introducing another standalone monitoring platform.

The main advantage is that AI workloads can be monitored alongside the services, APIs, infrastructure, and applications they depend on.

What it’s good at

• Enterprise application monitoring
• AI and LLM observability
• Correlating AI behavior with application telemetry
• Production monitoring
• Existing Datadog environments
• Centralized observability

Choose Datadog if

• Datadog is already part of your stack.
• Your AI applications depend heavily on traditional services.
• You want AI monitoring alongside infrastructure monitoring.
• Your team doesn’t want another standalone observability platform.

Where it differs from OpenController

Datadog is primarily an observability platform.

OpenController adds a layer focused specifically on AI agents and their governance.

That means the two can complement each other rather than being direct replacements.

Which tool should you choose?

The easiest way to narrow the list is to start with what you’re trying to solve.

But there’s another useful distinction.

If you have a few agents

Your biggest concern is probably understanding whether individual runs work correctly.

Tracing, evaluation, and debugging should be your first consideration.

If you have agents across multiple teams

The problem starts changing.

You now need to know:

• What agents exist?
• Who owns them?
• Where are they running?
• Which models are they using?
• What are they costing?
• Are teams creating agents outside your approved processes?

That’s where OpenController becomes relevant.

If you have agents across multiple clouds and frameworks

A single-framework observability platform may not give you the organizational visibility you need.

You may still use individual observability tools for debugging, but a control layer can provide the broader view across your stack.

A simple evaluation checklist

Instead of building a complicated scoring model, give each requirement a simple Yes / No / Maybe.

Then eliminate platforms that don’t cover the requirements you’ve marked Yes.

This makes the decision much easier than comparing every feature across every vendor.

The key question to ask before choosing

Don’t ask:

“Which agent observability tool has the most features?”

Ask:

“What do I need to know when something goes wrong?”

If the answer is:

“I need to inspect the execution and find the failed step.”

Look closely at LangSmith, Langfuse, Phoenix, Braintrust, and Datadog.

If the answer is:

“I need to know what’s happening across all the agents my organization is running.”

Add OpenController to the shortlist.

And if you need both, they don’t have to be mutually exclusive.

You can keep your existing observability and evaluation stack while using OpenController as the control layer across your agents.

Trace the run. Evaluate the behavior. Control the fleet.

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