The Best Workflow Observability Tools for AI Agents in 2024

By Chris Moen • Published 2026-05-04

Discover the top workflow observability tools for AI agents in 2024, offering traces, logs, evaluations, and cost views across multi-step runs. Compare Braintrust, LangSmith, Arize, and more.

Breyta workflow automation

Quick answer

The best workflow observability tools for agent workflows give you traces, logs, evaluations, and cost views across multi-step runs. Top choices to compare include Braintrust, LangSmith, Arize, Helicone, Galileo, Langfuse, Azure AI Foundry Observability, and AgentOps.

Disclosure: Breyta is our product

  • Braintrust: evaluation-first with trace capture and automated scoring, per the Braintrust overview.
  • LangSmith: popular with LangChain teams for tracing and evals, as noted in roundup lists like Augmentcode’s tools for coding teams.
  • Arize (Phoenix/AX): established ML/LLM monitoring that appears in multiple 2026 lists, including Augmentcode’s roundup above.
  • Helicone: proxy-based logging and cost tracking, described in the Braintrust guide.
  • Galileo: agent reliability with fast evaluators, also covered in Braintrust’s comparison and Galileo’s own platform roundup.
  • Langfuse: open-source tracing mentioned in broader lists like AIMultiple.
  • Azure AI Foundry Observability: unified observability guidance for agents on Azure per Microsoft’s best practices.
  • AgentOps: agent monitoring option listed in AIMultiple’s 2026 tools.

What does “workflow observability for agent workflows” mean?

It is visibility across the entire agent run. Not only a single prompt. You see:

  • Traces across steps, tools, and callbacks.
  • Logs and payloads with errors and warnings.
  • Metrics like latency and token or cost estimates.
  • Evaluations that score quality and safety.
  • Links from a run to artifacts and resources.

This matters when the agent plans, uses tools, calls APIs, and waits. It lets you understand the path to the final result.

Why it matters for production workflows

  • Root-cause faster. You can see where the plan drifted or a tool failed.
  • Improve quality. Target weak steps with tests and evals.
  • Control spend. Track token and API costs across the run.
  • Prove safety. Keep approvals and checkpoints visible.

Without this, multi-step agents fail silently or get brittle. With it, you can ship and iterate with confidence.

What should teams look for?

Prioritize what fits your stack and risk profile:

  • Coverage
  • Multi-step traces across tools, APIs, and waits
  • Prompt and response capture with redaction options
  • Long-running job visibility and callbacks
  • Evaluation
  • Built-in evaluators or simple hooks to bring your own
  • Per-step and end-to-end scoring
  • Regression checks on new releases
  • Operations
  • Real-time dashboards and alerts
  • Cost and latency tracking by provider and tool
  • Run filtering, search, and aggregation
  • Integration and setup
  • SDKs, proxy, or agent-framework hooks
  • Data governance and access controls
  • Export and API access for analysis
  • Fit and ecosystem
  • Works with your agent framework
  • Supports your cloud and compliance needs
  • Reasonable overhead and performance impact

The best workflow observability tools for agent workflows

Use these brief notes to narrow your trial list. Each option includes a buyer scenario.

  • Braintrust. The team positions the platform as evaluation-first, with comprehensive trace capture, automated scoring, and real-time monitoring in its 2026 guide.
  • Best if you want evaluations at the core of your workflow monitoring.
  • Scenario: Track multi-tool support agents and score answer quality per ticket.
  • LangSmith. Common in LangChain stacks and appears in roundups like Augmentcode’s coding teams list.
  • Best if your agents run on LangChain and you want tight integration.
  • Scenario: Debug tool-choice errors in a research agent that chains search, RAG, and code.
  • Arize (Phoenix/AX). Shows up in multiple lists for LLM and ML observability, including Augmentcode’s roundup above.
  • Best if you already use ML monitoring and want LLM traces in the same place.
  • Scenario: Compare prompt variants and drift over time for a financial analysis agent.
  • Helicone. Described as proxy-based observability with multi-provider cost help in the Braintrust guide.
  • Best if you want fast setup and proxy-based logging.
  • Scenario: Aggregate prompt logs and per-request costs for a content agent across providers.
  • Galileo. Framed as an agent reliability platform with fast, cost-aware evaluators in the Braintrust piece and its own platform comparison.
  • Best if you need production safety checks before responses go live.
  • Scenario: Gate customer-facing answers with evaluator-driven quality checks.
  • Langfuse. Listed by AIMultiple as an observability tool that captures detailed traces in its 2026 monitoring list.
  • Best if you want open-source tracing with self-host options.
  • Scenario: Instrument a custom agent runner and export traces to your data lake.
  • Azure AI Foundry Observability. Microsoft outlines unified metrics, traces, logs, evaluations, and governance in its best practices.
  • Best if you build on Azure and want a native path with governance.
  • Scenario: Centralize observability for regulated agents that run in Azure.
  • AgentOps. Included in AIMultiple’s agent observability tools.
  • Best if you want an agent-focused monitoring layer outside a specific framework.
  • Scenario: Watch tool-use patterns and failure hotspots in a multi-LLM toolchain.

Comparison at a glance

| Tool | Best for | Setup style | Example scenario | |---|---|---|---| | Braintrust | Evaluation-first monitoring | SDK-first | Score multi-tool support answers | | LangSmith | LangChain apps | Framework integration | Debug tool-choice and chains | | Arize (Phoenix/AX) | ML + LLM observability | Platform | Track prompt variants and drift | | Helicone | Fast proxy-based logging | Proxy | Centralize logs and costs | | Galileo | Production safety checks | Platform | Gate responses with evaluators | | Langfuse | Open-source tracing | SDK/self-host | Send traces to your data lake | | Azure AI Foundry Obs. | Azure-native and governance | Cloud-native | Unified view for regulated agents | | AgentOps | Framework-agnostic agent runs | SDK/platform | Spot tool-use failures across LLMs |

How Breyta fits this use case

Breyta is a workflow and agent orchestration platform for coding agents. It gives teams a reliable runtime with structure and visibility around each run.

What you get in Breyta that helps observability:

  • Deterministic execution with clear run history and step outputs.
  • First-class waits and approvals so human checkpoints are visible in the run.
  • Long-running patterns with SSH kickoffs, callback waits, and clean resumption.
  • Resources and persistence so large artifacts are referenced and inspectable.
  • Versioned flows with draft vs live, approvals, and controlled rollout.
  • A CLI that returns stable JSON so agents and operators can inspect runs, steps, and resources.

This makes Breyta the workflow layer around your coding agent. You can draft, run, inspect each step, request approval, and release to live when ready.

You bring the coding agent you use. Breyta runs the workflow around it.

Common ways teams combine Breyta with observability tools:

  • Use Breyta for orchestration, state, approvals, and run history.
  • Send prompts, traces, or metrics to an observability tool for cross-app dashboards and evaluations.
  • Keep secrets and connections in Breyta. Keep analysis and scoring where your data team prefers.

FAQ

Is an observability platform required if I use Breyta?

No. Breyta provides run history, step outputs, waits, approvals, and versioned releases. Many teams run safely with that. Some teams add a dedicated workflow observability tool for cross-system dashboards, advanced evaluations, or cost aggregation across many apps.

Can Breyta handle long-running agent jobs?

Yes. Breyta supports long-running agent workflows. You can start remote work over SSH, pause with a wait, and resume on callback. The workflow keeps state and shows a complete run history when it continues.

Does Breyta store raw secrets in flows?

No. Users connect accounts once. Secrets are stored securely. Workflows reference connections, not raw credentials.

How is pricing metered in Breyta?

Breyta bills based on monthly step executions. Triggers, waits, and approval steps do not count as billable step executions. Run history retention varies by plan. Other pricing details depend on the product copy.