Tools that record what AI agents do in production, 2026

At a glance

Category	Examples	What it records	What it does not
Agent transcripts	Claude Code, Cursor, agent frameworks	The agent's own account of its steps	Anything the agent did not write down, or chose not to
Cloud audit logs	CloudTrail, Cloud Audit Logs, Azure Monitor	API calls made with a credential	Which agent, on whose instruction, under what policy
Kubernetes audit	Kubernetes audit policy	Requests to the API server	Anything outside the cluster, and the reason for the request
Container isolation	Edera, gVisor, Kata, urunc	Nothing by itself; it bounds reach	What the agent did inside the boundary
Observability	Datadog, Grafana, Splunk	Symptoms and traces after the fact	The decision, the instruction, the policy verdict
Policy engines	OPA, Cedar	The rule that was evaluated	Whether the action then happened, and what it touched
Action record	NOFire AI	The action, the policy verdict, and the blast radius, written outside the agent	Nothing yet standardised across vendors

How this list was built

This is a category question rather than a product shortlist, because the category barely exists. The other lists in this set rank a field of nine or ten. Here the honest answer is that most teams assemble something from parts, and the parts were built for other purposes.

So the table above is organised by what each kind of tool actually captures, not by vendor. Every row is a real thing teams use for this today. Every "what it does not" column is the gap that shows up when somebody asks for proof rather than for debugging.

Read from vendor documentation and the relevant regulation in August 2026. Rechecked quarterly, with the date at the top moving when it is. We build one of these, and this is the list where that shows most, because the category is thin enough that we are one of few naming it at all. Treat the framing as ours and check the gaps yourself.

The tools

Agent transcripts are what most teams have. Claude Code, Cursor and the agent frameworks keep a record of their own steps, and it is genuinely useful when you are debugging why an agent did something strange. It is not evidence, for the reason stated in the FAQ above: the account and the action come from the same process.

Cloud and Kubernetes audit logs are the closest thing to a neutral witness that most estates already run. CloudTrail records the API call. The Kubernetes audit log records the request to the API server. Both are outside the agent, which is the property that matters. Both are also anonymous with respect to agency: they record that a credential did something, not which agent held it, who instructed it, or whether anything approved it.

Container isolation is the containment half rather than the recording half. Edera runs workloads in a container-native Type-1 hypervisor with per-workload zones and names AI agent sandboxing among its use cases; gVisor, Kata and urunc address the same shared-kernel problem differently. None of them tells you what happened inside the boundary, which is the point of the boundary.

Policy engines such as OPA and Cedar record the decision. What they do not record is the consequence: whether the permitted action then ran, and what it touched.

NOFire AI writes the action, the policy verdict that allowed or refused it, and the computed blast radius, from outside the agent's own process. The reason it is out of band is the same reason a cloud audit log is more useful than a transcript.

What a usable record has to contain

Working backwards from what an auditor or an incident review actually asks, a record is useful when it answers five things about a single action. Most of what teams run today answers one or two.

Which agent. Not which credential. A shared service account used by four agents makes every one of them unaccountable, and this is the most common failure in estates that think they are covered.

On whose instruction. The human or the upstream system that set the task. Without it there is no chain from a business decision to a production change, which is the chain a review is trying to follow.

What it was allowed to do at that moment. Policy is not static. An action permitted in March may be refused in June, and a record that does not capture the verdict as it stood cannot be reconstructed later.

What it actually touched. The API call is the visible part. What matters for a review is the reach: which services, which data, which customers were downstream of that action. That requires a dependency model, which is why this is the row most products cannot fill.

Who wrote the entry. Covered above, and the one that decides whether any of the other four can be trusted.

Score whatever you run today against those five. Most teams find they have the third and fourth from nowhere at all, the second only in a ticket somebody typed, and the fifth failing outright because the agent's own transcript is doing the work.

Where each tool is blind

The blindness in this category is structural rather than incidental, and it is the same one everywhere: whoever writes the log is the thing you are trying to hold to account.

Transcripts fail this completely. Policy engines pass on the decision and fail on the outcome. Cloud audit logs pass on the action and fail on the intent, because the credential carries no notion of which agent or which instruction. Isolation passes on containment and records nothing.

Ours: an out-of-band record is only as good as the boundary it observes from, and it covers actions that pass through the gate. An agent acting through a channel we do not mediate is outside it, which is why the isolation question and the recording question have to be answered together rather than treated as alternatives.

And the category-wide gap worth stating plainly: there is no standard for what an agent action record should contain. Every vendor here, us included, has invented its own shape, so nothing is portable and nothing is comparable. That is a bad position for a capability regulators are starting to ask about.

How to choose

Ask one question of every vendor, and ask it first: who wrote the log, and could the agent have changed it? That single question sorts this entire field, and most answers to it are uncomfortable.

Then work backwards from what you have to produce. If an auditor asks for evidence that an agent did not touch a production database last March, list what you would hand over today. The parts that are missing are the shortlist. Runtime policy patterns covers how the gate and the record fit together, and what AI agent governance covers defines the surrounding terms.