Best AI Agent Monitoring Tools in 2026: Reviews & Comparisons

While doing research for this post I realized there are really a few different categories of AI (agent) monitoring. Even what exactly we mean by “AI agent” needs to be specified because currently, due to these concepts still being relatively new, the terminology is still hazy and in flux. I plan on writing about all that in more detail in a separate post.

What are AI Agents?

Let’s define this first. For the purposes of this post we can think of them as applications that are “clients” of LLM models through providers of AI services, such as OpenAI, Anthropic, and so on. These AI agents are popping up like mushrooms after the rain and are becoming real production software. They are no longer just chatbots that answer questions, but systems that can call APIs, execute shell commands, read and modify files, query databases, browse the web, send messages, and delegate work to other agents.

That radically changes the monitoring problem and actually adds a whole new bucket of things one ought to monitor now.

When you run a normal application in production, you mostly want to know whether it is available, fast, and error-free. With AI agents running everywhere now, those questions still matter, but they’re no longer enough. An agent can be technically healthy while doing something completely wrong.

It can successfully call every API and still misunderstand the task. It can spend hundreds of dollars on model calls because it got stuck in a loop. It can select the wrong tool, expose information it shouldn’t, or take an action that nobody explicitly intended it to take.

And there is another problem that I think will become increasingly important: sometimes the organization may not even know that an AI agent is running on its infrastructure.

That is why I think “AI agent monitoring” is really becoming two related but distinct categories:

1. Monitoring the AI application you intentionally built, including traces, model calls, latency, cost, and quality.
2. Discovering and monitoring autonomous agents running on your infrastructure, including what they are doing and whether they create security or governance risks.

What is AI agent monitoring?

AI agent monitoring sits somewhere between several things we already know from software engineering, but it isn’t exactly the same as any one of them:

• Application performance monitoring (APM). Agents are still software, so you need to know whether they’re slow, failing, retrying excessively, or consuming unreasonable amounts of CPU, memory, or other resources. Traditional application monitoring covers part of this problem, but it usually can’t explain why an agent decided to take a particular action.
• Distributed tracing. A useful agent trace is more than a request flowing through microservices. An agent might call an LLM, decide to invoke a tool, inspect the result, call another model, delegate work to another agent, and repeat the process several times. You want to be able to reconstruct that chain and understand how one decision led to the next.
• LLM observability. This adds visibility into things traditional tracing doesn’t normally care about: prompts, model responses, token consumption, model selection, context size, and inference costs. This is essential when the model itself is part of the application’s decision-making process.
• Evaluation and testing. With traditional software, I can often write a deterministic test: given input X, expect output Y. Agents are different. They may take different paths and still produce acceptable results, or they may successfully complete every API call while producing a completely wrong answer. Monitoring should therefore include some way of measuring quality, not just whether the software crashed.
• Security monitoring. This is the area I think will become much more important as agents get more autonomy. Once an agent can execute commands, read files, access APIs, use credentials, browse the web, or communicate with other systems, you absolutely need visibility into what it is actually doing—not just what it was supposed to do, but what it did, which commands it called, while files it read, created, deleted or modified, which information is sent out and where, and so on.
• Infrastructure monitoring. Finally, there is a very practical problem that most AI observability tools don’t solve: how do I know which agents are running in the first place? An organization may have agents running on developer machines, servers, containers, CI/CD infrastructure, or Kubernetes clusters without a central inventory of them.

That combination is what makes AI agent monitoring different from simply adding another dashboard to an existing observability stack.

So for those two distinct categories I described earlier, there are also two fundamentally different questions we want monitoring to answer.

The first is:

Is my agent working correctly?

For that, we care about the agent’s execution path. What did it decide? Which model did it call? Which tools did it use? Where did it fail? How long did it take? How much did it cost? The first four bullets from the above list are all about this.

The second question is more uncomfortable:

What AI agents are actually running in my environment, and what are they doing?

That means being able to determine whether an agent is accessing files, executing shell commands, connecting to external systems, using credentials, or behaving in ways that violate security policies. That’s what the last two bullet points are about.

Why AI agent monitoring is important

The obvious reason for monitoring agents (in that first category mentioned above) is debugging. Agents are complicated systems. When something goes wrong, you need to understand what happened.

But I think the bigger reason will increasingly be security. That’s that second category.

We are giving software systems capabilities that we would traditionally give only to trusted applications or humans. Agents can access production systems, use credentials, execute commands, modify files, communicate with external services, and sometimes make decisions with relatively little human intervention.

That creates a muuuuuuuch larger attack surface.

Recent security incidents involving major AI organizations and AI infrastructure, including incidents affecting organizations such as OpenAI and the broader open-source AI ecosystem around Hugging Face, are reminders that AI infrastructure is not somehow separate from normal security problems. It contains credentials, proprietary models, sensitive data, source code, APIs, and increasingly powerful workloads. Keep in mind that whatever you read about incidents like the one from OpenAI/Hugging Face is likely not the full scope of the incident. Not everything is shared. Especially when it may hurt those organizations. Too much interest, too much money is at stake.

At the same time, research into autonomous agents is showing behavior that should make engineers pay attention. In controlled environments, agents have found unexpected ways around constraints, coordinated with other agents, left persistent information for other agents, exploited loopholes in their environments, and, in some evaluations, attempted to hide or preserve behavior when their goals conflicted with being shut down or modified.

Again, I think the problem is much bigger than what is publicly being shared. This is not science fiction any more, this is reality today. We must not ignore what these early incidents  are showing us.

We are building systems with:

• more autonomy,
• access to more tools,
• more persistent memory,
• more access to infrastructure,
• more ability to communicate,
• and fewer humans directly involved in every decision.

It is entirely reasonable to expect that these systems will eventually do things their designers did not anticipate. And let’s actually be real: they are really already doing that today.

Eventually, some of those things will be malicious, not necessarily because the underlying model suddenly develops malicious intent, but because an agent can be manipulated, compromised, given a harmful objective, or simply find an unexpected path toward accomplishing the objective it was given. It doesn’t have quite the full reasoning we have. It doesn’t really have empathy. It doesn’t really discern the way (good) humans do.

Security engineers have spent decades learning that you don’t wait until something becomes a widespread incident before you start monitoring it. In other words, even if you are not running any AI agents in your infrastructure today, I’m of the opinion that you should be prepared and put in place measures and tools to catch agents when they do show up. This is very much like e.g., virus or malware monitoring on your own computer. You don’t install an antivirus after it’s already on your computer, after it has already stolen your credentials, read and sent out sensitive data, spread to the rest of the network, encrypted your files and started blackmailing you, etc. No, smart people have antivirus running well ahead of such incidents and catch issues before they happen.

That’s how I think companies should approach AI agents.

What should an AI agent monitoring tool actually do?

If I were evaluating these products, I wouldn’t start with a long feature checklist. I’d start with a few practical questions. So back to my 2-category starting point:

For an agent application that my team built, I want to know whether I can follow an execution from beginning to end. I want to see the model calls, tools, sub-agents, latency, failures, and cost. I also want some way of determining whether the agent’s output was actually good.

For infrastructure-level monitoring, my questions are different.

Can the system tell me which agents exist without requiring every developer to manually register them?

Can it show me what those agents are doing?

Can I see shell commands, file activity, network connections, tool execution, and access to sensitive information?

Can I identify unusual or risky behavior?

Can I create policies and alerts?

The first category is mostly about observability and engineering quality.

The second is increasingly about security and governance.

I don’t think those two categories will necessarily converge into one product. In fact, I suspect most companies will use different tools for each.

1. Sematext AI Agent Watch

Description

Sematext AI Agent Watch addresses a different problem from most of the products in this article.

LangSmith, Langfuse, Phoenix, and similar tools, which I review below, generally start from the assumption that you know about the agent. You built it, you instrumented it, and want to understand its execution.

But not all AI agents running on your infrastructure will be your agents – the agents that you have built and instrumented. There will be third party agents that somebody has installed on your servers for who knows what reason. Thus, AI Agent Watch starts with a more fundamental question:

What AI agents are actually running on my infrastructure?

It is designed to first discover agents running on monitored hosts and then observe what they are doing. That can include agent sessions, shell commands, tool execution, file activity, network connections, and potential exposure of sensitive information.

I think this is a super important distinction. This is that second category from the beginning of the article.

Additionally, an AI agent, including your own, can be perfectly instrumented at the application level and still be a security problem. Conversely, an agent might be running somewhere in the environment without anyone having instrumented it at all. But you should still want to know about it and its activity, right?

This type of monitoring is about discovering autonomous software and understanding its real-world behavior.

Key features

• Automatic AI agent discovery
• Host-level monitoring
• Agent session tracking
• Shell command visibility
• Tool execution monitoring
• File read and write monitoring
• Network connection monitoring
• Sensitive-data detection
• Trusted and untrusted agent tracking
• Risk scoring
• Alert rules and reporting

Pros

• Solves a problem most LLM observability tools don’t address
• Can discover agents without application-level instrumentation
• Strong infrastructure and security perspective
• Useful for detecting risky behavior
• Focuses on real actions rather than only prompts and responses

Cons

• Not a replacement for detailed LLM tracing
• Not primarily an evaluation platform
• Most useful where you control or monitor the underlying infrastructure

Pricing

Sematext AI Agent Watch pricing is tied to the monitored infrastructure and Sematext plan. It starts at $1.68/host/month.

My Opinion

What I like: I think this addresses a problem that will become increasingly important: you can’t govern or secure agents if you don’t know they exist.

Traditional AI observability starts after someone deliberately instruments an agent. Infrastructure-level discovery starts earlier.

It asks:

What autonomous software is running, where, and what is it actually doing?

I think that will eventually become a standard security question.

What I dislike: I wouldn’t use it alone for agent quality, prompt debugging, or detailed model-level evaluation. For those problems, I would pair it with Langfuse, LangSmith, or Phoenix.

2. LangSmith

Description

LangSmith is one of the most complete AI application observability platforms. It covers tracing, evaluation, prompt management, datasets, experiments, production monitoring, and feedback collection.

Its biggest strength is that it makes complicated agent executions relatively easy to inspect. If an agent calls multiple models, invokes tools, uses retrievers, and delegates work to sub-agents, LangSmith gives you a structured view of that execution instead of forcing you to reconstruct it from logs.

It is particularly compelling for teams using LangChain or LangGraph, although it is no longer limited to those frameworks.

I see LangSmith as a platform rather than just a tracing tool. That’s useful if you want one place for much of your AI engineering workflow. The trade-off is that you’re adopting a fairly large proprietary platform, and I would think about that before making it the center of everything.

Key features

• Agent and LLM tracing
• Tool and sub-agent visibility
• Production monitoring
• Offline and online evaluation
• Datasets and experiments
• Prompt management
• Token and cost tracking
• User feedback
• Dashboards and alerting

Pros

• One of the best tracing experiences in the category
• Excellent LangChain and LangGraph integration
• Strong combination of observability and evaluation
• Mature production workflow
• Good cost visibility

Cons

• Proprietary platform
• Strongest integration is naturally within the LangChain ecosystem
• Self-hosting is not the default experience
• Pricing is complicated: costs can grow with both usage and team size

Pricing

LangSmith offers a free developer tier and paid plans that combine seats and usage. Pricing changes fairly regularly, so I would check the current pricing page before making a decision, particularly for production-scale workloads.

My Opinion

What I like: The tracing UI is genuinely useful. When debugging a complicated agent, being able to visually follow the execution tree saves a lot of time.

What I dislike: I don’t love the idea of tightly coupling my entire AI development workflow to one proprietary platform. If interoperability and deployment flexibility are important to me, I’d look closely at Langfuse or Phoenix as well.

3. Langfuse

Description

Langfuse is probably my default recommendation for a general-purpose engineering team that wants AI observability without committing too heavily to a single proprietary ecosystem. Keep in mind that when I refer to “AI observability” here I mean that first category described further above.

It provides the things I expect from an AI observability platform: traces, nested agent activity, sessions, token and cost tracking, prompts, datasets, experiments, and evaluations. The important difference is that Langfuse is open source and can be self-hosted.

That makes it particularly interesting for companies that have data residency requirements or simply don’t want every prompt and model response to live in someone else’s SaaS platform.

I also like its OpenTelemetry direction. I think standardization matters in this space, especially because AI frameworks are changing so quickly.

The downside is that flexibility comes with more choices. Langfuse doesn’t force one particular way of building your AI engineering workflow, which I see as an advantage, but some teams may prefer something more opinionated.

Key features

• LLM and agent tracing
• Agent graphs
• Sessions
• Token and cost tracking
• Prompt management
• Datasets and experiments
• Online and offline evaluations
• OpenTelemetry support
• Python and JavaScript SDKs
• Self-hosting

Pros

• Open-source core
• Strong self-hosting story
• Framework-agnostic
• OpenTelemetry support
• Good combination of tracing and evaluation
• Reasonable entry pricing

Cons

• Self-hosting means operational responsibility
• Requires more architectural decisions than highly opinionated platforms
• Evaluation still requires engineering work and good datasets
• Pricing units require some understanding at scale

Pricing

Langfuse has a free cloud tier, paid cloud plans, and a self-hosted open-source option. This is one of the things that’s good about it: small teams can get started cheaply, while larger organizations have the option to keep more control over their deployment.

My Opinion

What I like: This is probably where I would start for many teams. I like the combination of open source, OpenTelemetry, self-hosting, tracing, and evaluation.

What I dislike: It’s less of a “just follow this workflow and everything works” product. If my team wanted maximum convenience and was already committed to LangGraph, LangSmith might be easier.

4. Arize Phoenix and Arize AX

Description

Keep in mind that Dynatrace acquired Arize in August 2026. This means things may change in the future, but for now, this is how I see things:

Phoenix takes a more engineering-oriented approach to AI observability.

The open-source Phoenix project provides tracing and evaluation capabilities, while Arize’s commercial platform adds managed production monitoring and additional enterprise features.

What I find interesting about Phoenix is its emphasis on OpenTelemetry and open infrastructure. That makes it attractive if I don’t want my instrumentation strategy tied to a specific AI framework.

Phoenix is particularly useful for inspecting complex LLM and retrieval workflows and for building evaluation pipelines around them. The managed Arize platform makes more sense when you want those capabilities without operating everything yourself.

The main trade-off is that Phoenix feels more like an engineering platform than a polished, highly opinionated SaaS workflow.

That’s a good thing if your team wants flexibility. It’s less attractive if you just want to install an SDK and immediately have everything configured for you.

Key features

• OpenTelemetry-based tracing
• LLM and agent observability
• Retrieval and RAG inspection
• Evaluations
• Custom metrics
• Production monitoring through Arize
• Open-source deployment

Pros

• Strong OpenTelemetry support
• Open-source option
• Good fit for engineering-heavy teams
• Strong evaluation capabilities
• No immediate vendor lock-in

Cons

• More engineering effort than some SaaS alternatives
• Phoenix and Arize AX can be confusing as separate products
• Less turnkey for teams without observability experience
• Some uncertainty around the future of both Phoenix and Arize XA after acquisition by Dynatrace, known for being expensive and targeting large enterprises with deep pockets.

Pricing

Phoenix is available as open source. Arize also offers managed plans with usage-based pricing and enterprise options.

My Opinion

What I like: I like the architectural direction. OpenTelemetry and open-source instrumentation give me more confidence that I won’t have to completely redo my observability stack when the next AI framework becomes popular.

What I dislike: It’s not the product I would choose for a team that wants the simplest possible onboarding experience.

5. Braintrust

Description

Braintrust is the tool in this list I would look at first if my biggest concern was not debugging, but preventing AI quality regressions.

Tracing is available, but the center of gravity is evaluation. Braintrust is built around datasets, experiments, scorers, production evaluations, and measuring whether AI systems are actually improving.

That philosophy makes sense to me. My background is in text search where similar frameworks exist for pretty much the same reasons – evaluating the quality of results, tuning, feedback, etc.

A beautiful trace can tell me exactly what happened. It still doesn’t tell me whether the agent did a good job.

Braintrust makes evaluation part of the normal engineering workflow. I can define test cases, compare versions, create scoring functions, and evaluate production behavior.

The trade-off is that this approach requires investment. You need meaningful datasets and useful evaluators. The tool can’t invent a good definition of quality for your application.

Key features

• Agent tracing
• Datasets and experiments
• Custom scorers
• Automated evaluation
• LLM-as-a-judge
• Production quality monitoring
• Token and cost tracking

Pros

• Excellent evaluation workflow
• Strong support for regression testing
• Good production quality monitoring
• Useful experiment comparison

Cons

• More expensive than lightweight tracing tools
• Requires serious investment in evaluation design
• Proprietary platform
• Can be more than a small team needs

Pricing

Braintrust offers a free entry tier and paid plans for larger-scale evaluation and production use. Pricing is worth checking carefully because the platform makes the most sense when you are running a meaningful evaluation workflow rather than just collecting traces.

My Opinion

What I like: I agree with the underlying philosophy. AI quality needs to be tested continuously, not treated as something developers inspect manually when a customer complains.

What I dislike: If I only need tracing and debugging, I wouldn’t start here.

6. AgentOps

Description

AgentOps is more explicitly focused on the practical problem of understanding what an agent did. This is indeed useful. It sounds simple, but it’s not.

When an agent behaves unexpectedly, reproducing the problem can be difficult because LLM-based systems aren’t fully deterministic. AgentOps focuses heavily on tracing agent activity, tool calls, model usage, and the ability to inspect or replay sessions.

I particularly like the idea of replaying an agent’s execution. Traditional debugging tools have taught developers to expect breakpoints, stack traces, and reproducible failures. Agent systems don’t always provide that.

AgentOps tries to close that gap.

Compared with evaluation-first tools like Braintrust, I see AgentOps as more focused on debugging and operational visibility.

Key features

• Agent tracing
• Tool-call visibility
• Multi-agent monitoring
• Session tracking
• Replay and debugging
• Token and cost monitoring
• Logs and errors
• Framework integrations

Pros

• Strong focus on practical agent debugging
• Useful replay capabilities
• Good multi-agent visibility
• Broad ecosystem support
• Straightforward cost tracking

Cons

• Evaluation capabilities are not its strongest differentiator
• Proprietary platform
• Usage pricing needs to be monitored
• Doesn’t solve infrastructure-level agent discovery

Pricing

AgentOps provides a free tier and paid plans based on usage and organizational requirements.

My Opinion

What I like: I like the focus on debugging. When an agent does something weird, my first instinct is to ask what sequence of events led to that behavior.

What I dislike: For serious quality engineering, I would probably combine this type of tooling with a stronger evaluation platform.

7. W&B Weave

Description

Weave makes the most sense in organizations that are already using Weights & Biases for ML experimentation. It extends the W&B ecosystem into LLM applications and agents by providing tracing, evaluations, monitoring, experiments, feedback, and production analysis.

The advantage of this approach is continuity. A team that already uses W&B for models and experiments doesn’t have to introduce a completely separate platform for AI application monitoring.

The idea of monitoring production traffic using automated scoring and evaluators also makes sense. That is closer to how I think mature AI applications should operate: continuously checking whether behavior is degrading rather than waiting for users to notice.

The disadvantage is that if you are not already using W&B, the ecosystem advantage is much smaller.

Key features

• End-to-end tracing
• Token and cost monitoring
• Evaluations
• Experiment tracking
• Production monitoring
• Human feedback
• Automated scoring
• Safety and quality monitoring

Pros

• Strong evaluation and experimentation capabilities
• Good fit for existing W&B users
• Connects development and production workflows
• Supports multimodal AI workloads

Cons

• Most compelling inside the W&B ecosystem
• Broader platform than some teams need
• Not focused on infrastructure-level agent discovery

Pricing

Weave pricing depends on the broader W&B platform and organizational usage. I would check the current pricing directly before evaluating it.

My Opinion

What I like: If my organization already used W&B, I would absolutely evaluate Weave before adding another specialized observability vendor.

What I dislike: For a conventional software engineering team with no existing ML platform investment, it may feel like more ecosystem than necessary.

 

Comparison table

Tool	Best for	Tracing	Evals	Cost monitoring	Self-hosting	Agent discovery	Security/activity monitoring
Sematext AI Agent Watch	Discovering and monitoring agents on infrastructure	Activity-level	Limited today	Planned/currently evolving	SaaS agent-based	Yes	Excellent
LangSmith	LangChain/LangGraph teams	Excellent	Excellent	Yes	Enterprise	No	Limited
Langfuse	Open-source, framework-agnostic teams	Excellent	Excellent	Yes	Yes	No	Limited
Arize Phoenix / AX	OpenTelemetry and evaluation-heavy teams	Excellent	Excellent	Yes	Phoenix: Yes	No	Limited
Braintrust	Evaluation-first workflows	Good	Excellent	Yes	Enterprise options	No	Limited
AgentOps	Debugging agent execution	Excellent	Moderate	Excellent	Enterprise	No	Some audit capabilities
W&B Weave	Teams already using W&B/ML tooling	Excellent	Excellent	Yes	Limited/enterprise-dependent	No	Quality/safety scoring

How I would choose between these tools

If I were building an agent application today, I would start by deciding which problem I actually need to solve, which of those two categories of AI monitoring I am primarily after.

If the problem is debugging agent behavior, I would look first at LangSmith, Langfuse, Phoenix, or AgentOps. (Category 1 below)

If the problem is measuring quality and preventing regressions, Braintrust, LangSmith, Langfuse, Phoenix, and Weave are stronger candidates. (Category 1 below)

If OpenTelemetry, open source, and deployment control matter most, I would look closely at Langfuse and Phoenix. (Category 1 below)

If my organization already runs heavily on LangChain/LangGraph, LangSmith would be the obvious first evaluation.

And if the question is:

What AI agents are running on my servers, containers, and infrastructure—and what are they actually doing?

Then I need something different from application-level tracing. That’s where infrastructure-level monitoring and agent discovery tools become relevant. (Category 2 below)

Category 1: AI application observability

For tracing, debugging, costs, and evaluations:

• Langfuse is my default general-purpose recommendation.
• LangSmith is the obvious choice for many LangChain/LangGraph teams.
• Phoenix is attractive if OpenTelemetry and open source matter.
• Braintrust is strongest when evaluations are central to your engineering process.
• AgentOps is particularly good for agent execution debugging.
• W&B Weave makes the most sense for organizations already invested in the W&B ecosystem.

Category 2: AI agent infrastructure and security monitoring

This is a different problem. Here I want to know:

Which agents are running on my servers, what are they doing, and are they creating risk?

For that problem, traditional LLM observability products generally aren’t enough because they typically depend on application instrumentation and focus on prompts, model calls, and traces.

That is where a tool such as Sematext AI Agent Watch fits.

My preferred architecture for a serious production environment would probably be two layers:

       AI Agent Application
               │
┌──────────────┴──────────────┐
│                             │
▼                             ▼
AI Observability              Infrastructure
Langfuse / LangSmith          AI Agent Watch
Phoenix / Braintrust          │
│                             │
▼                             ▼
Traces, Evals, Cost           Discovery, Files,
Quality, Latency              Commands, Network,
                              PII, Risk, Alerts

 

Feature comparison

Here is another way to compare tools reviewed in this article. You can clearly see the two categories of solutions addressing different angles of AI monitoring.

Feature	Sematext	LangSmith	Langfuse	Arize	Braintrust	AgentOps	W&B Weave
Agent tracing	★★	★★★★★	★★★★★	★★★★★	★★★★	★★★★★	★★★★★
Tool-call tracing	★★★★★	★★★★★	★★★★★	★★★★★	★★★★	★★★★★	★★★★
LLM cost tracking	★★★★★	★★★★★	★★★★★	★★★★	★★★★★	★★★★★	★★★★★
Offline evaluation	★	★★★★★	★★★★★	★★★★★	★★★★★	★★★	★★★★★
Online evaluation	★	★★★★★	★★★★★	★★★★★	★★★★★	★★★	★★★★★
Self-hosting	SaaS agent	★★	★★★★★	★★★★★	★★	★★	Varies
OpenTelemetry	N/A	★★★	★★★★★	★★★★★	★★★	★★★	★★★
Agent discovery	★★★★★	✖	✖	✖	✖	✖	✖
File activity monitoring	★★★★★	✖	✖	✖	✖	✖	✖
Network activity monitoring	★★★★★	✖	✖	✖	✖	✖	✖
Security risk detection	★★★★★	★★	★★	★★	★★	★★★	★★★

 

My final opinion

I don’t think there is a single best AI agent monitoring tool because the category is splitting into two layers.

The first layer is AI application observability.

This is where tools such as Langfuse, LangSmith, Phoenix, Braintrust, AgentOps, and Weave operate. They help me understand execution, latency, costs, prompts, tools, and quality.

The second layer is AI agent discovery, governance, and infrastructure monitoring.

This layer answers a different question.

Not:

“Why did my application agent make this decision?”

But:

“What autonomous agents are running in my environment, what capabilities do they have, and what are they actually doing?”

I think the first layer is already becoming a standard part of AI application engineering.

The second layer is earlier in its evolution. It’s only emerging. But I suspect it won’t stay that way for long. As agents become more autonomous and get access to more systems, companies are going to need an inventory of those agents. They will need to know which agents can execute commands, access files, use credentials, and communicate externally.

And eventually, monitoring alone won’t be enough. Organizations will want AI policies, AI agent governance, and enforcement around what agents are allowed to do.

So if I were designing a production AI observability today, I would probably use two layers:

An AI observability platform to understand whether the agent works correctly.

An infrastructure-level agent monitoring platform to understand whether the agent is doing something it shouldn’t.

Those are different problems and both (are going to) matter.

 

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