A product manager asked the company’s AI copilot for background on a competitive deal. The agent returned a polished brief that quietly stitched in confidential roadmap plans, unreleased feature specs, and internal positioning notes from a restricted launch doc. Nothing looked broken. The agent found relevant context, produced a helpful answer, and moved on. Security only learned about it later, when the brief was forwarded outside the company.
That is the state of AI agent observability for most enterprises. You can see that an agent ran. You can see how long it took and what it cost. You cannot see the thing that matters most: what data the agent touched on the way to its answer.
The AI security category has grown fast. There are good tools and real use cases now. They still share one blind spot, and it is the one that should worry a security team most.
What is AI agent observability?
AI agent observability provides a view into autonomous AI agents so you can see how they reason, what tools they call, what they retrieve, and whether their behavior matches what they were built to do. It applies the ideas behind application observability, traces, metrics, and logs, to a system that thinks in steps rather than executing a fixed path.
One clarification up front, because the term gets muddied. This is not infrastructure monitoring or AIOps, where AI helps you watch servers and networks. Agent observability is the reverse: humans watching the AI. The subject under observation is the agent's own decision-making, not the CPU it runs on.
The core signals look familiar to anyone from an engineering background: metrics, evaluations, logs, and traces — MELT.
- Metrics show latency, token usage, cost, error rates, and throughput.
- Evaluations provide scored judgments of whether the output was correct, relevant, safe, or on-task.
- Logs capture raw inputs, outputs, and intermediate messages.
- Traces show the step-by-step path of an agent's run, including LLM calls, tool calls, retrieval steps, and reasoning transitions.
That evaluation layer is what separates agent observability from classic APM. A payment API either returns the right balance or it does not. An agent can return a fluent, confident answer that is wrong, and nothing in your latency graph will flinch.
Why traditional observability breaks down for agents
Deterministic software does the same thing every time. Give it the same input, get the same output, trace the same path. Agents do not work that way. The same prompt can produce different actions depending on context, prior steps, and which tool the model decides to reach for. That single property breaks most of the assumptions your existing observability stack was built on.
The failure modes repeat.
The first is silent failure. An agent can succeed by every technical measure, return a 200, finish the run, log no error, and still do the wrong thing. It books the wrong meeting, queries the wrong database, or answers a question using data it should never have opened. Your monitoring sees success. The business sees a problem.
The second is the multi-step, non-linear execution path. A traditional request is a straight line you can read top to bottom. An agent branches, loops, calls tools in parallel, and revises its own plan mid-run. Engineers who have tried to debug this describe the mismatch plainly: a flame graph or a flat span list was designed for linear request paths, and it buckles when the execution tree forks and merges. APM handed down its visualizations from a world of straight-line requests. Agents do not move that way.
The third is the handoff. Enterprises are not deploying one agent. They are deploying fleets that delegate to each other, agent to agent, through emerging patterns like A2A communication. Every handoff is a place where context, permissions, and data get passed along, and every handoff widens the blind spot. By the time you are three agents deep, "what happened here" is a hard question to answer.
None of this makes observability optional. It means the tooling has to be rebuilt for non-determinism, and the category knows it.
The pillars of AI agent observability
Most platforms converge on a similar set of capabilities. This is table-stakes ground that every serious tool covers, so the short version will do. The long one lives on a dozen vendor pages.
Tracing and spans give you the execution record: every LLM call, tool call, retrieval, and reasoning step, ideally following OpenTelemetry semantic conventions so the data is portable. Evaluation scores quality, whether through LLM-as-judge, code-based checks, or human review, and runs both offline against test sets and online against live traffic. Cost and latency track token usage and response times, usually at p50 and p95, with attribution down to the individual agent or tool so you know what is expensive. Guardrails catch the unsafe outputs: hallucination, toxicity, and jailbreak or prompt injection attempts. Governance and compliance tie it together with audit trails and mappings to frameworks like the EU AI Act, ISO 42001, and the NIST AI Risk Management Framework.
Get these right and you have real visibility into how your agents perform. You will know when they are slow, when they are expensive, when they go off the rails, and when their answers degrade. That is a strong foundation.
It is also incomplete. Every capability above watches the agent's behavior and its system telemetry. None of them answers the question the sales manager was asking.
The blind spot: data visibility inside agent observability
Agents do more than call tools and generate text. They read, retrieve, and pass data. Sensitive data flows through tool calls, into RAG context windows, across agent-to-agent handoffs, and into memory and vector stores that persist long after the run ends. Standard tracing records that a retrieval happened. It rarely records what was in the payload.
So your observability stack can tell you an agent made a retrieval call that took 240 milliseconds and cost 1,800 tokens. It usually cannot tell you that the retrieved chunk contained a customer's medical records, a block of source code, or a spreadsheet of unreleased financials.
No mainstream observability tool answers these well today:
- Did this agent's RAG retrieval pull PII, PHI, source code, or regulated data into context?
- Which agents have touched a given sensitive record, and how many hops did it travel through, from agent to tool to sub-agent to memory?
- Where does an agent's memory or vector store physically persist data, and does that location violate data residency rules?
- If an agent is compromised or misconfigured, what is the real blast radius measured in data exposed, not just systems reached?
This is the difference between watching behavior and understanding exposure. Think of it as DSPM meeting agent observability: classification and sensitivity context layered onto every trace and span, not just system telemetry sitting next to it.
Our own research says the gap is close to universal. In the 2025 State of AI Data Security Report, based on a survey of more than 900 IT and security leaders, 83% of enterprises reported they are already using AI. Only 13% reported strong visibility into how that AI interacts with sensitive data. Autonomous agents were named the hardest thing to secure by 76% of respondents, while just 9% monitor AI activity in real time. Two thirds, 66%, had already caught AI over-accessing sensitive data. Only 11% could automatically block risky activity when they saw it.
Put those numbers together. Most organizations have adopted AI and cannot see what it touches. Fewer still can stop an agent that reaches too far. The sales manager's question is the norm, not an edge case. It surfaced in a hallway because the tools that should have caught it were watching the wrong layer.
As Tamar Bar-Ilan, Cyera's co-founder and CTO, puts it: "Data context is the ultimate boundary. Before you can secure what an agent does, you have to know what it can see."
What data-aware agent observability requires
Closing the gap does not mean ripping out your observability stack. It means extending it so that data sensitivity becomes a first-class signal alongside traces, cost, and evaluations. Four practices matter.
Tag traces and spans with data sensitivity at the point of retrieval or tool call, not after the fact. The moment an agent pulls a chunk into context is the moment to know whether that chunk is regulated. Bolting classification on downstream, after the data has already moved through three agents, is forensics, not observability.
Map agent-to-data relationships the way you already map service-to-service dependencies. You know which microservice calls which. You should know which agent can reach which datastore, through which identity, and with what level of privilege. That map is what turns "an agent did something" into "this agent could see these 14 sensitive stores, and touched 3 of them today."
Extend audit trails to answer who and what, not just which function. A traditional log says function X was called at time Y. A data-aware trail says which agent accessed which sensitive data, when, how, and under whose delegated authority. That is the record you need when an auditor or an incident responder comes asking.
Govern agents like identities. An agent with broad, standing access to sensitive data is a risk whether or not it ever misbehaves. Apply least privilege to agents the same way you would to a human employee, and rightsize permissions before deployment rather than after an incident.
This is where Cyera's approach starts, because it sits on top of the data discovery and classification the company already does. Cyera's agent security follows a continuous lifecycle. Discovery maps and catalogs every active agent, its model dependencies, approved actions, and data connections, building an Agent Graph of the relationships between users, agents, permissions, tools, and data. Governance evaluates configurations and behavioral drift against each agent's intended purpose. Protection inspects prompts, responses, and intermediate tool calls and retrievals in real time, blocking unauthorized queries before they execute. Validation runs red-team simulations to test guardrails and produce audit evidence.
Underneath, Access Trail logs access activity across cloud and on-prem, showing which agents touched which data, when, and how. That is the record the sales manager's security team did not have. And because Cyera runs DSPM, DLP, and agent security under a single policy engine, the rules that govern human access to a sensitive store automatically apply to any agent that inherits access to it. Classification runs at 95% precision, so the sensitivity labels attached to a trace are dependable enough to act on.
Data-blind vs. data-aware observability
The clearest way to see the gap is to walk the same four scenarios through both models.
ScenarioData-blind observability seesData-aware observability seesRAG retrievalA retrieval call happened, its latency, and its token costWhether the retrieved context contained PII, PHI, secrets, or regulated data the agent was not authorized to seeTool call to an internal APIThe API was called and returned 200What sensitive records the API exposed to the agent, and whether that crossed a policy boundaryMulti-agent handoffAgent A passed a task to Agent BWhat data traveled with the handoff, how many hops it made, and which agents can now see itAgent memory persistenceData was written to a vector storeWhat sensitive data now persists, where it physically lives, and whether that violates residency requirements
The left column is useful. It keeps your agents fast and reliable. The right column keeps them from becoming your largest unmanaged data exposure. You need both. Most stacks give you only the left.
How to get started
Build data-aware observability in layers.
Start with tracing. Instrument your agents with OpenTelemetry semantic conventions so every run produces a portable, structured trace. Add evaluations next, so you are scoring quality online and offline rather than guessing. Layer in guardrails for the unsafe-output cases: hallucination, toxicity, prompt injection. Then add the step most teams skip. Attach data classification and sensitivity context to the traces you already collect, so every retrieval and tool call carries a label for what kind of data moved.
That final layer answers the sales manager. It is also the one your current observability vendor likely does not offer, because it depends on knowing your data, not just your systems.
Cyera plugs into that last layer. If you want to see what data your agents can reach, take a look at Access Trail. If you are earlier in the journey and want to benchmark where you stand, the AI Security Readiness Assessment maps your posture against frameworks like NIST AI RMF and ISO 42001.
Frequently asked questions
What is the difference between AI observability and AI agent observability?
AI observability broadly covers monitoring any AI system, including single-model applications where you mostly care about output quality, latency, and cost. AI agent observability is narrower and harder. It covers autonomous agents that reason across multiple steps, call tools, retrieve data, and hand off to other agents. Because the execution path is non-deterministic and branching, agent observability has to trace decisions and tool use, not just inputs and outputs.
What is the difference between agent observability and agent security?
Observability tells you what an agent did. Security controls what an agent can do. Observability is largely a read function: trace, measure, evaluate, alert. Security is an enforcement function: rightsize permissions, block unauthorized access, intercept risky tool calls before they execute. The two overlap on data. You cannot secure what an agent touches until you can see it, and seeing it has limited value if you cannot act.
Do I need agent observability if I already have LLM observability?
Yes. LLM observability typically watches a single model call: the prompt in, the completion out, the tokens and latency. Agent observability watches an entire workflow of chained calls, tool use, retrievals, and handoffs, where the failure modes live in the connections between steps rather than in any single call. LLM observability is a component of agent observability, not a replacement for it.
What data should I track when monitoring AI agents?
Track the standard signals first: traces and spans, token usage and cost, latency, error rates, and evaluation scores. Then add the signal most tools omit: the sensitivity of the data flowing through each retrieval, tool call, and handoff. Knowing that an agent retrieved 1,800 tokens is operational data. Knowing those tokens contained regulated PII is security data. You need both to run agents safely.
Can AI agent observability tools see sensitive data flowing through agents?
Most cannot, at least not by default. Standard tracing records that a retrieval or tool call occurred, along with its performance, but not whether the payload contained PII, PHI, source code, or other regulated data. Seeing that requires classifying data at the point of access and attaching sensitivity context to the trace. That capability comes from a data security layer such as Cyera, not from observability tooling alone.
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