Monitor Agents by Governing Their Actions
Watching an agent’s logs after the fact is not monitoring - it is forensics. Real monitoring means scoring every action as it happens, surfacing the riskiest decisions, and keeping a tamper-evident record of what the agent did.
Why this is hard
Agents in production act continuously and non-deterministically, so a log you read later tells you what went wrong but not in time to stop it. Effective monitoring has to be in the path of the action: scoring it, surfacing the ones that need attention, and recording the outcome - so a drifting or manipulated agent is caught while it acts, not during the post-mortem.
- Reading logs later is forensics, not monitoring
- Agents act continuously - the important action hides in the volume
- A manipulated or drifting agent needs catching in the moment
- Without a tamper-evident record, the post-mortem is guesswork
The approach, step by step
From log-reading to real-time governance
- 1
Score every action
Run each agent action through trust scoring so anomalous ones surface with explainable factors instead of hiding in the stream.
- 2
Surface the riskiest
Use decision intelligence to rank recent decisions by risk so attention goes to the actions that warrant it.
- 3
Gate the consequential
Hold high-risk or consequential actions for step-up, review, or containment rather than letting them run unchecked.
- 4
Record every decision
Write each action to the tamper-evident ledger so the record of what the agent did is complete and verifiable.
- 5
Feed outcomes back
Confirm real-world outcomes so the compounding loop adjusts the agent’s trust and future monitoring gets sharper.
How SentriAI does the work
SentriAI monitors agents by governing them: every action is scored, the riskiest recent decisions are ranked in decision intelligence, consequential actions are gated, and everything is recorded. Because it sits in the path of the action, a problem agent is caught while it acts, and confirmed outcomes make the next decision sharper.
What you get out of the box
- Trust scoring on every agent action, with explainable factors
- A ranked queue of the riskiest recent decisions
- Consequential actions gated in the moment
- A tamper-evident record of everything the agent did
Avoid the common pitfall
Frequently asked questions
How do I monitor AI agents in production?
By governing their actions in real time: score every action for risk, surface the riskiest recent decisions, gate consequential actions for step-up or review, and record everything to a tamper-evident ledger - so a problem agent is caught while it acts.
Why is log-reading not enough?
Because reading logs later is forensics, not monitoring. It tells you what went wrong after the fact. Effective monitoring sits in the path of the action so an anomalous or manipulated agent is caught in the moment.
How do I know which agent actions to look at?
Decision intelligence ranks recent decisions by risk with explainable factors, so a short queue of the actions that warrant attention replaces scanning the full stream.
Does monitoring improve over time?
Yes. Confirming real-world outcomes feeds the compounding loop, which adjusts the agent’s trust so future decisions - and the monitoring built on them - get sharper.
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Monitor agents in the path of the action
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