You can't secure an AI agent you can't see
AI agent observability means knowing exactly what your agents did — every tool call, command, file read, and API request — as it happens, not reconstructed after something breaks.
AI agent observability is the ability to see and understand what autonomous agents are actually doing at runtime. Application logs tell you an agent ran; observability tells you it read three files, called two APIs, and tried to write to a path it shouldn’t have.
For agents that act on real systems, this visibility is the foundation everything else rests on. You can’t write policy, catch abuse, or pass an audit for behavior you never captured.
What AI agent observability should capture
Useful observability records the actions an agent takes, not just the prompts and completions. At minimum:
- Every tool call — name, arguments, and result — as a structured event.
- Shell commands, file reads and writes, and network or API requests.
- Which agent, which session, and which policy decision applied.
- Whether each action was allowed, blocked, or required approval.
Observability without leaking secrets
The catch with capturing everything is that agent traffic is full of secrets — tokens, keys, private data. Naive logging turns your observability store into a breach waiting to happen.
Prismor redacts secrets at the source, before events ever leave the machine, so you get complete visibility without creating a new place for credentials to leak.
From observability to control
Prismor captures agent activity as a structured, searchable event stream at the tool-call boundary — the same boundary where it enforces policy. That means observability and control share one source of truth: what you can see, you can also govern, block, and prove later in an audit.
Frequently asked questions
What is AI agent observability?
It is the ability to see what AI agents do at runtime — every tool call, command, file access, and API request — as structured, searchable events, so you can understand, secure, and audit their behavior.
How is agent observability different from LLM observability?
LLM observability focuses on prompts, tokens, and model outputs. Agent observability focuses on actions — the tool calls and system operations an agent performs — which is where security risk actually lives.