Agent Trajectory Monitors: Ensuring AI agents follow the right path

When running AI agents in production, it’s crucial to have visibility into all elements of the agent lifecycle, including data/inputs, performance, behavior, and outputs. But what exactly do we mean by “behavior”? 

Put simply, it’s knowing whether agents are working in the way you expect. Are the paths they are taking from one step to the next in the correct sequence? Are they potentially skipping a step in a workflow? Are they executing loops that dramatically increase resource usage? And how do these behavior issues impact their overall performance, results, and ultimate outputs? 

Getting insight into patterns and decision-making is particularly difficult because agent behavior is non-deterministic. Understanding when something has gone wrong is also tricky because, when a behavioral issue is at the root, it doesn’t necessarily produce an error.  

The agent still returns a response and your system might look healthy. But something subtle, and potentially dangerous, has changed when agent behavior deviates from the expectation. This is why tracking it is so crucial to maintaining reliable, trustworthy agents in production that scale. 

And that’s why Monte Carlo has released one of our most exciting Agent Monitors, Trajectory Monitors. These monitors allow teams to verify that agents are executing workflows in the expected order, frequency, and structure, providing a new layer of observability for complex LLM systems.

The hidden risk in multi-step AI workflows

As AI systems become more sophisticated, agents increasingly resemble distributed systems composed of models, tools, and data services.

This introduces a new reliability challenge: understanding and tracing agent behaviors, decision-making, and paths taken to execute an objective. 

Observing agent behavior is a departure from what we’re used to when it comes to monitoring data, applications, and infrastructure, where we rely on fixed systems to gauge whether an endpoint returned an error or latency increased to an unacceptable threshold. 

We aren’t used to answering – or even asking – questions like:

• Did an agent skip the permission check before writing data?
• Is the retrieval step still happening before generation?
• Did a recent prompt change cause the agent to call the same tool five times in a loop?
  

These failures are subtle, but important, because they can impact not only outputs, but can drive up AI costs and even create security or compliance risks. 

Without observability into the trajectory of the agent’s execution, teams often discover these issues only after users complain, costs spike, or a security incident emerges. 

Agent Trajectory Monitors close that visibility gap.

What are Agent Trajectory Monitors?

Agent Trajectory Monitors are part of Monte Carlo’s suite of agent-specific monitors, which help data and AI teams get visibility into the full spectrum of the agentic stack – from data/inputs to performance, to behavior, and ultimately to outputs. 

Trajectory Monitors allow teams to define expected execution patterns for AI agents and automatically alert when those patterns are violated. Rather than monitoring outputs or performance metrics, these monitors focus on how the agent actually executes its workflow.

They analyze agent traces and detect when workflows, tasks, or spans behave unexpectedly, such as appearing in the wrong order, occurring too frequently, or missing entirely.

In practice, this means teams can monitor questions like:

• Did the check_permissions task run before delete_data?
• Did the web_search tool run more than five times?
• Did the retrieve_context step occur before the LLM response generation?

These monitors function similarly to Validation Monitors, but are purpose-built for agent traces and execution paths.

With that complexity comes new failure modes.

How Agent Trajectory Monitors work

Agent Trajectory Monitors operate directly on agent traces, which capture the sequence of workflows, tasks, and spans executed during an agent interaction.

Teams define alert conditions that describe the execution patterns they want to enforce.

These conditions can evaluate:

• Order — whether a step occurs before or after another
• Frequency — whether a task occurs more than a specified number of times
• Co-occurrence — whether two steps appear together or separately

Supported rules include conditions like:

• a span occurs before another span
• a task occurs without another task
• a workflow occurs more than X times
• a span does not occur before another span

These conditions allow teams to define expected agent trajectories and receive alerts whenever traces violate those expectations.

Alerts include:

• the number of traces that violated the condition
• a list of invalid traces
  historical graphs showing prior breaches

This makes it easy to identify when agent behavior changes and quickly investigate why.

Deploying trusted agents at scale

AI agents are quickly becoming a core layer of modern software. But to run them in production, teams need visibility into how each element within the system behaves – from inputs to performance to behavior to outputs. 

Agent Trajectory Monitors provide that visibility into behavior, decision-making, and paths taken. 

Along with Monte Carlo’s other agent monitors, including Evaluation, Validation, Metric, and Pre-Production Monitors, teams can achieve true end-to-end observability into their entire agentic stack. This ensures that they’re building AI systems that are not just powerful—but predictable, efficient, and safe to operate at scale.

Trajectory Monitors are just one feature within Monte Carlo’s comprehensive Agent Observability platform. Learn more about Agent Observability here.

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