AI agent observability means tracing what your agents do (every tool call, model inference, sub-agent handoff, and action taken inside a connected system) across the full execution path. 

It is not application performance monitoring, which watches infrastructure and response times.

And it is not LLM tracing alone, which records model inputs and outputs and stops at the tool-call boundary.

Agent observability requires going past the API boundary (which app is being accessed) to see everything an agent does while inside an app or tool like Linear, Salesforce, or PostHog.

KPMG's Q4 2024 survey found 51% of respondents exploring AI agents and 37% piloting them, for a combined 88% (KPMG, Q4 2024). A 2026 LangChain survey found that 89% of organizations had implemented some form of agent observability, but only 62% had detailed tracing for individual steps and tool calls (LangChain, 2026). 

AI agents are off to a flying start within organizations attempting to capitalize on the capabilities of this new technology. 

But often, when something goes wrong at scale (and when working with AI agents it is a matter of when, not if it does!) there is no trace, no audit record, and most often, no way to say who authorized the action. 

TL;DR

• Choose Phoenix or Langfuse for self-hostable tracing and evaluation; add Braintrust when production traces must feed CI/CD evaluations.
• Choose Fiddler for combined ML and agent monitoring; choose Zenity for discovery, security posture, and inline response.
• Add Willow when agents need employee-bound identity, action-level permissions, runtime enforcement, and authority-linked audit records.

Why AI Agent Observability Matters in Production

In PwC's 2025 survey, 79% of surveyed executives said their companies were already adopting AI agents (PwC, 2025). 

An AI agent in production is not the same problem as an AI model in production. A model produces an output.

An agent takes an action. Often, a lot of actions. Autonomously. And unpredictably. 

The mistake some make is thinking of AI agent monitoring in the same manner as model monitoring. 

The model layer covers latency, cost per token, input and output logs. 

That is necessary, and adds a valuable layer of visibility. 

But the model layer stops being sufficient the moment an agent has read or write access to a production database, app, or tool. 

Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, driven in part by inadequate risk controls (Gartner, June 2025). And getting controls for AI agents right is no simple task. 

Agents act inside tools, and multi-step workflows branch into execution trees. Prompts alone cannot guarantee that an agent will stay within the intended path, especially when prompt injection or poisoned content influences a later step. One important way to reduce the blast radius is to enforce least-privilege, action-level permissions alongside approvals, sandboxing, network controls, and monitoring. 

Those controls need permissions infrastructure. A practical solution is to connect agent permissions to the IdP and access policies the organization already maintains, such as Okta or Entra ID. 

What to Look For in an AI Agent Observability Platform

The criteria that separate a real agent observability platform from a repurposed APM or LLM-tracing tool come down to five questions. 

(1) Can it trace a full multi-step run as a single linked tree? 

A span is one recorded step in a workflow. The root span represents the complete run, while child spans represent the model calls, tool calls, and handoffs inside it. 

For example, a support agent might read a Zendesk ticket, check Salesforce, query a billing system, and issue a refund. One linked trace should nest those four steps so an incorrect refund can be traced back to a faulty entitlement returned earlier in the workflow.

(2) Does it see inside the tool, or only that the tool was called? 

Most platforms stop at the MCP (Model Context Protocol, the open standard that lets agents connect to external tools) gateway. That tells you which app or tool was connected to. 

Action-level visibility means capturing which record was read or written, which query ran, which file changed. In short, it captures the bulk of actions AI agents complete, those that happen past the gateway and inside the tool itself.

(3) Can it name the employee behind each action? 

This asks whether each action traces back to the human authority under which it executed, using the IdP (the directory that manages employee access through systems such as Okta, Entra ID, or JumpCloud).

A service-account log may show that salesforce-bot changed an opportunity. An authority-linked record should additionally show that Priya's agent requested the change, which permission allowed it, and whether the action stayed within her approved scope.

(4) Does it read your existing IdP and CI/CD, or bolt on a second system? 

If a platform copies users and permissions into a separate credential store, a role change may update in the IdP but not in that specific copy. A transferred employee could retain agent access that the current role no longer permits. This is credential drift. 

The same split applies to CI/CD. A platform that hooks into the pipeline you already run gates every agent change on tests that fire automatically, so a prompt or model tweak that drops quality or safety scores gets caught before it ships. One that makes you stand up a separate evaluation system leaves those checks outside your release process, where they get skipped under deadline and stop matching what actually reaches production.

(5) Can it export an audit trail an auditor can follow? 

SOC 2, SOX, and the EU AI Act want evidence that specific actions occurred. The export needs structured fields, such as action type, tool, data touched, identity, timestamp, and outcome. 

These tools work at different layers and often run side by side, so the table above compares capabilities rather than ranking rivals. We checked each against the vendor's public documentation as of July 2026. N/D* means we could not find public confirmation of a capability, not proof that it is missing.

Keep in mind here that IdP + CI/CD fit means the platform reads your identity provider for its own single sign-on and role-based access and hooks into your CI/CD pipeline. 

Langfuse, for example, offers enterprise SSO and RBAC, with SCIM and audit logs on its Enterprise tier. 

That is separate from delegated human-authority attribution, which asks whether each production tool action is recorded against the employee authority under which it executed, including the effective permission scope at that moment. 

The Top 6 AI Agent Observability Platforms for 2026

The six products occupy different but complementary layers, from open-source tracing and evaluation, to enterprise monitoring and security, and identity, access, and runtime governance. 

1. Arize Phoenix: Open-Source LLM Tracing and Evaluation

Arize Phoenix is self-hostable with native support for OpenAI Agents SDK, LangGraph, and CrewAI (among other frameworks).

Tracing and quality assessment live in one place. Multi-step traces record each part of a run, and automated evaluation can use another LLM to grade those recorded steps against defined criteria. 

The core project is source-available under the Elastic License 2.0. It's not a permissive OSI license like MIT, but you can still self-host it, modify it, and run it inside your own commercial product for free (Phoenix license documentation). 

What the Elastic License forbids is taking Phoenix and offering it to third parties as a hosted or managed service. You cannot resell Phoenix itself, but your org can run it in production for its own purposes.

Phoenix documents user authentication and identity-provider integration, but as of mid-2026 the identity it records is the login or service account, not the employee whose permissions an agent action ran under (Phoenix authentication documentation).

2. Langfuse: Open-Source LLM Engineering Platform

Langfuse covers similar ground to Phoenix. Its core observability, evaluation, prompt-management, and related APIs are MIT-licensed and free to self-host. However, enterprise features such as SCIM, audit logs, and data-retention controls require a commercial license (Langfuse licensing). 

Where it pulls ahead of Phoenix is in prompt engineering. 

Prompt versions are tied to the traces they produced and the eval scores those traces earned. Practically, it means you can measure the quality impact of a single prompt change across a live deployment.

Strong CI/CD teams like it as an evaluation gate before an agent change ships.

Langfuse is SOC 2 Type II and ISO 27001 certified and captures trace user metadata and Enterprise audit logs, but that attribution stops at the session level. As of mid-2026 it has no documented way to tie a downstream tool action to the authorizing employee's live permissions.

3. Braintrust: LLM Evaluation and Tracing

Braintrust connects pre-production evaluation with production traces and failures. 

Its shared data model lets a production trace be replayed as an eval input, and an eval failure be traced back to the run that triggered it. 

That makes it the pick for teams whose dev-time eval scores and production behavior keep diverging. 

It also supports a bring-your-own-cloud deployment, where Braintrust's data plane runs inside the customer's AWS, Azure, or Google Cloud environment. That gives teams with data-residency requirements control over where sensitive trace and evaluation data is stored.

Braintrust can tag traces with application-provided user metadata, but a trace still shows what happened without recording who was accountable for it. As of mid-2026 it has no documented way to bind an action to the employee who authorized it. 

Braintrust is SOC 2 Type II compliant and supports HIPAA requirements, with business-associate agreements available (Braintrust security documentation). Those controls secure the platform itself. Tying an action to the employee who authorized it is a separate job, and not one Braintrust does.

4. Fiddler AI: Enterprise ML and AI Observability

Fiddler AI approaches agent observability from the enterprise-risk side.

It monitors predictive ML models and agentic AI under one governance dashboard, tracing an agent's full run over native OpenTelemetry, from the top-level request down to each step. 

On top of that sit real-time guardrails for faithfulness, hallucination, toxicity, jailbreaks, and PII or PHI detection. Fiddler documents sub-100ms guardrail latency and deployment inside the customer's environment (Fiddler Guardrails documentation).

Fiddler maps audit evidence to frameworks such as NAIC for insurance and SR 11-7 for bank model risk. It also documents air-gapped deployment options, enabling banks, insurers, and government teams to run it inside networks that do not touch the public internet. 

Fiddler is a strong fit for teams that want predictive ML and agentic AI monitoring in one enterprise environment.

Fiddler documents agent traces, decision context, and governance evidence, but its visibility stays at the model and behavioral layer. As of mid-2026 it does not reach inside a connected tool to show which record moved, or bind that action to an employee identity (Fiddler monitoring documentation).

5. Zenity: Enterprise Agent Security and Observability

Shadow AI discovery is a central Zenity capability, and the company raised a $38M Series B to build it out.

Zenity discovers and inventories agents across SaaS copilots (Microsoft 365 Copilot, Salesforce Agentforce, and ChatGPT Enterprise) and homegrown frameworks built on AWS Bedrock or Google Vertex AI. 

It then maps agent behavior to attack paths. 

For many enterprises the first observability problem is simply knowing which agents exist. Zenity covers that well. Its research found upwards of 79,000 apps built per organization on copilots and low-code platforms, with 62% of them carrying security vulnerabilities. 

Its AI detection-and-response layer blocks unsafe agent actions inline, before they land. Most monitoring tools only flag a risky action after it has already run. 

Zenity documents agent ownership, permission mapping, and step-level monitoring, and can see that a Copilot agent requested a file. However, as of mid-2026 it stops short of tying that action to the employee whose authority it ran under (Zenity AI Observability).

6. Willow: Identity and Access Layer for AI Agents

Willow is not a pure-play observability tool. It is the agent identity and access platform, and it is on this list to show the layer the other five leave open: identity, access scope, runtime enforcement, and authority-linked audit records. 

Observability records the action. Willow records both the action and the authority behind it.

With Willow, every agent action is bound to a real employee identity from your existing IdP (Okta, Entra ID, JumpCloud), scoped to app-aware permissions at the action level, and written to an immutable audit trail.

The audit trail shows whose identity the agent inherited, whether that person's live permissions covered the action, and whether it stayed in scope. 

At Wix, Willow now governs roughly 5,000 weekly active users across about 600 connected tools and MCPs, and more than 300,000 governed tool calls a week (Wix case study).

Willow also discovers agents, MCP servers, and AI tools already running in the organization, including unapproved ones. Endpoint- and browser-level detection helps teams identify shadow AI and either bring it under existing governance controls or block it.  

Willow's governed MCP gateway serves each agent only the tools and actions its identity and task permit (Willow Tools and Skills). An agent cleared to use Jira for one workflow does not automatically receive the tool's full range. The policy is enforced at runtime instead of only being detected after the action.

A one-click kill switch can revoke an agent or tool across the organization when something looks wrong (Willow product overview). 

Separately, every audit event exports to Splunk, Loki, or Grafana, whatever SIEM you already run (Willow July 2026 product digest).

How to Choose the Right Platform for Your Production Stack

Where you start depends on where your agents already are.

If you are still building or evaluating models, start with open-source tracing. Arize Phoenix and Langfuse both self-host, speak OpenTelemetry (the open standard for distributed tracing), and show each step of a run.

Add Braintrust when you need a CI/CD evaluation loop that turns production traces into reusable test cases. Its bring-your-own-cloud deployment is relevant when the data plane must remain inside the customer's cloud environment.

Once agents are live under real enterprise risk, though, you need to move up to an enterprise monitoring layer. 

Fiddler fits teams watching predictive ML and agentic AI on one compliance dashboard, or anyone who needs an air-gapped install.

Zenity fits teams whose main exposure is SaaS copilot sprawl and who need agent discovery, shadow AI detection, and attack-path analysis first.

Once agents act on production systems, observability alone leaves a gap. It records what happened, but not who was allowed to do it. Willow fills that gap. It ties each agent action to a real employee's authority, enforces scoped permissions as the action runs, and records who authorized it.

The Identity Attribution Gap Most Platforms Leave Open

Varonis found that 99% of the environments it analyzed had sensitive data exposed in ways AI could surface. Separately, 98% contained unverified applications, including shadow AI (Varonis, 2025). In Proofpoint's U.S. survey, 80% of CISOs reported concern about potential customer-data loss through public GenAI platforms (Proofpoint, 2025). 

That is the exposure Willow exists to close: agents acting without enforced identity, scope, and runtime controls.

When an incident involves an agent action, the observability log says "the action happened." The identity layer says "Sarah's agent, scoped to read-only access in Project Alpha, took an action that exceeded her authorization; here is the audit-trail entry, the scope at the time, and the timestamp."

The two layers are additive. The observability platform provides the operational record, and the identity layer provides permissioning, efficient access management, and audit logs linked to human identities. Together they provide a foundation for scaling agentic AI rollouts while maintaining control and visibility. 

Further Reading

• Willow Platform, Identity and Access Layer for AI Agents: https://withwillow.ai/platform/identity-access 
• Willow Wix Case Study: https://withwillow.ai/blog/wix-case-study 
• Arize Phoenix: https://arize.com/phoenix/ 
• Langfuse Documentation: https://langfuse.com/docs/observability/overview 
• Braintrust Platform: https://www.braintrust.dev 
• Fiddler AI Agentic Observability: https://www.fiddler.ai/agentic-observability 
• Zenity AI Observability: https://zenity.io/platform/ai-observability 
• Varonis 2025 State of Data Security Report: https://www.varonis.com/blog/state-of-data-security-report 
• KPMG AI Quarterly Pulse Survey, Q4 2024: https://kpmg.com/kpmg-us/content/dam/kpmg/corporate-communications/pdf/2025/q4-pulse-deck.pdf 
• PwC AI Agent Survey, 2025: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agent-survey.html 
• Gartner press release, June 25, 2025: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027