How to Tell If an AI Agent Is Behaving Oddly or Genuinely Dangerous: Key Risk Signals Explained

A Problem That's Keeping Engineering Teams Up at Night

As AI Agents take on increasingly autonomous tasks in enterprise environments, a thorny question has emerged: when an agent does something unexpected, is it being "creative" — or is it doing something genuinely dangerous?

This question recently sparked a discussion in a Reddit tech community. The original poster described a classic scenario: their AI Agent took actions that were "technically within its permissions, but clearly not what the team intended." The team wanted to build a signal framework to help them identify truly risky behaviors from the broader noise of "unexpected" ones.

reddit original post discussion

This isn't a purely theoretical problem. When an agent has real permissions — accessing databases, modifying configurations, calling external APIs — the cost of getting it wrong is high. Being overly cautious paralyzes the agent, while being too permissive can lead to a security incident.

"Unexpected" Doesn't Mean "Harmful"

At the heart of the discussion is a distinction that's easy to blur: unexpected and bad are not the same thing.

AI Agents are inherently non-deterministic. Given the same task, an agent might take a path that engineers never anticipated. This "creativity" is often part of an agent's value — it can find solutions humans wouldn't think of. If every "out-of-the-ordinary" action is treated as a threat, you're essentially negating the point of agent autonomy.

What truly warrants concern is behavior that not only exceeds expectations, but also deviates from the original intent and has the potential to cause harm. The original poster astutely noted that many risk signals aren't hidden in individual actions — they're hidden in action chains. Each step looks compliant in isolation, but taken together they point toward a dangerous outcome.

Where High-Risk Signals Hide

Based on observations raised in the discussion, a few categories of actions deserve particular attention:

Sensitive Data Access

An agent suddenly starts reading sensitive data unrelated to the current task — especially when the scope of access clearly exceeds the minimum needed to complete the task. An agent responsible for generating reports that starts querying a user credentials table is a clear anomaly signal, even if the permissions technically allow it.

Modification of Critical Configurations

Modifying critical system configurations is inherently a high-risk operation. Such actions have significant blast radius and are difficult to reverse. If an agent modifies core configurations without an explicit instruction to do so, it should be treated as a red flag.

Communication with Unexpected Endpoints

An agent attempting to connect to network endpoints outside the scope of its task may indicate data exfiltration or that it has been hijacked. Anomalous communication targets are one of the most classic indicators of compromise.

What these three signal categories share is that they are all "within permissions, outside intent." This is precisely the hardest class of behavior to defend against — traditional access control almost completely fails here.

The reason action chain risks are especially hard to detect comes down to the fundamental nature of AI Agent task execution: it's a sequential decision-making process where the legitimacy of each action depends on interpreting context, and that context keeps changing as execution proceeds. An attacker — or a runaway agent — can exploit this by chaining together individually innocuous steps that gradually converge on a dangerous goal. In security, this is a variant of what's known as a "Low-and-Slow Attack." Traditional rule engines struggle to handle this because they typically set thresholds on individual events, lacking the ability to perform semantic correlation analysis across actions and time. Effective defense requires adopting ideas from SIEM (Security Information and Event Management) systems — treating the agent's action sequence as a whole for pattern matching, rather than auditing each step in isolation.

The Biggest Obstacle: Lack of Runtime Visibility

The original poster closed with a key observation: "Without runtime visibility, it's very difficult to distinguish legitimate behavior from malicious behavior."

This captures the core pain point in current AI Agent security governance. Most teams manage pre-authorization (assigning permissions to agents) and post-hoc auditing (reviewing logs), but lack the ability to observe the agent's execution in real time.

Without a runtime view, you can't see how an action chain is evolving step by step, and you can't make "allow or block" decisions at critical junctures. By the time log analysis surfaces a problem, the damage has usually already been done.

Runtime Visibility refers to the ability to observe a system's internal state, execution paths, and behavior in real time while it's actually running. In traditional software engineering, this is typically achieved through APM (Application Performance Monitoring), distributed tracing (e.g., OpenTelemetry), or log stream aggregation. But the challenge with AI Agents is that their "behavior" isn't limited to function calls and network requests — it also includes reasoning steps, tool selection logic, and multi-step planning. These processes often happen inside the LLM's "black box," making them difficult to capture with conventional methods. Approaches currently being explored in the industry include: injecting tracing hooks into each tool call an agent makes, recording full reasoning chain traces (chain-of-thought traces), and building observability interfaces directly into agent frameworks (such as LangChain and AutoGen). Without this capability, security teams are essentially trying to prevent theft in a warehouse with no security cameras — only able to discover losses after the fact.

How to Build a Signal Framework

Drawing on this discussion, a practical agent risk signal framework can be built around several dimensions:

Behavioral baseline comparison: Establish a baseline for what normal agent behavior looks like. Significant deviations trigger attention — rather than simply asking whether a given action was "unexpected."
Intent alignment checks: Don't just verify whether individual actions comply with rules; evaluate whether the action chain as a whole still serves the original task objective.
Dynamic least-privilege convergence: Even when permissions are granted, monitor whether the agent actually needs to use them. Out-of-scope invocations are a signal.
Endpoint allowlisting: Explicitly define the legitimate targets an agent can communicate with. Any connection outside the allowlist should be blocked or trigger an alert.
Runtime monitoring: Invest in tools that can observe agent decision-making and execution in real time. This is the prerequisite for shifting from "after-the-fact remediation" to "in-the-moment intervention."

Final Thoughts

This discussion reflects a serious engineering challenge that's emerging as AI Agents mature in production: as agents become more autonomous, the security boundary is shifting from "can it do this?" to "should it do this?"

Access control addresses the former, but the latter requires more sophisticated signal detection and runtime visibility to support. For teams currently deploying agents, rather than chasing perfect pre-authorization, it's worth focusing energy on building observable, interruptible runtime defenses — because the most dangerous behaviors are precisely those that look completely compliant.