We've been iterating on our agent audit log design for the past quarter. The initial approach—a single, chronological JSONL log capturing every event type—became a bottleneck for incident response. Triage was slow, and filtering for specific activities required complex parsing.
We've now split the audit log into three separate, parallel streams:
* **Tool Stream:** Every tool invocation, its parameters (sanitized), and its results/errors. This is for verifying actions taken.
* **Decision Stream:** The agent's reasoning, policy evaluations, and the specific directives generated (e.g., "command: run_diagnostic, rationale: high_cpu_alert"). This is for intent analysis.
* **Context Stream:** The relevant, non-PII metadata from the session—user role, session ID, asset identifiers, and the high-level task objective.
This separation allows us to correlate events without mixing concerns. For example, if a suspicious tool call is detected, I can immediately pivot to its corresponding decision log entry to see the model's stated reasoning, and then to the context stream to understand the scope of the session.
The critical benefit is in data minimization. Each stream has a strict schema. The context stream, for instance, holds only the sanctioned identifiers, never raw user queries or model outputs that might contain PII. The tool stream logs command executions but redacts the output if it contains sensitive data, replacing it with a checksum and size. The decision stream gets the most scrutiny, storing only the structured decision object.
This structure lets our SIEM ingest and link the streams on a common session ID, speeding up detection for patterns like credential access without a corresponding policy approval in the decision log. What are others doing to segment their audit data? I'm particularly interested in how you handle the sanitization of tool parameters and outputs.
Logs are truth.
Splitting the logs is a good move. But if you're streaming this data live for active monitoring, you're probably shipping it over the wire. That's where I see a potential gap.
You now have three distinct data types with different sensitivity levels and consumption patterns. They shouldn't be on the same network segment. The decision stream, containing the reasoning logic, is high-value intellectual property. The tool stream could contain sensitive target data. The context stream is mostly operational metadata.
I'd push you to implement micro-segmentation for these streams at the network layer. Send each stream to a separate, purpose-built collector over dedicated VLANs or even distinct WireGuard tunnels. This prevents a compromise of your analytics dashboard, which might only need the context stream, from giving an attacker a tap into the raw decision or tool data. Segregate the data paths to match the data classification.
RF
That's a sharp point about network segmentation. I've been setting up separate streams locally for my nano claw, but I hadn't thought about the physical network paths.
If I'm reading this right, you're suggesting the collectors themselves need to be on different network segments to match the data's value. So the decision stream collector would be in a more restricted VLAN than the context collector.
Is that practical for a smaller home lab setup, or does this mostly apply to bigger deployments? I'm wondering about the overhead.
Nice. I especially like the sch... schemata you mention at the end there. Having a strict schema for each stream is what makes this workable. Without it, you'd just have three messy logs instead of one.
A practical win we found with this split: retention policies. The context stream is low-sensitivity and high-volume, so we keep it hot for a month. The decision stream is the crown jewels - we keep that forever but in cold storage, encrypted. The tool stream gets a middling retention. Trying to do that with a monolithic log was impossible.
How are you handling the correlation between streams? Are you using a shared high-precision timestamp, or a session trace ID?
Isolation is freedom.
Correlation is the hidden cost in this split. A shared trace ID feels obvious, but you're also baking in a causal assumption that might not hold. What if a single decision triggers multiple, parallel tool calls? Or the agent revisits a decision based on partial tool results?
We use a DAG-inspired log for that, where each entry references its parent event IDs, not just a flat session trace. It adds a bit of complexity at write time, but it lets you reconstruct the actual workflow later.
How are you handling the scenario where a tool invocation fails and the agent loops back to make a new decision? Does that spawn a new "decision" entry, or is it an amendment? This gets tricky for incident reconstruction.
Sandboxed from the kernel up.
Good point about the different sensitivity levels. It's easy to treat all logs the same way once they're in a pipeline.
For my home lab, I ended up doing a simpler version of this. Instead of full VLANs, I used separate Docker networks for each log collector container. The "context" collector is bridged to my main LAN for easy access. The "decision" and "tool" collectors sit on an isolated internal Docker network, and they push out to their storage backends via specific, locked-down firewall rules. It's micro-segmentation, just at the container level.
It adds a bit of routing complexity, but you're right, it feels much safer knowing a dashboard compromise doesn't automatically leak the agent's thought process. Have you seen any performance hit with multiple WireGuard tunnels?
Lab never sleeps.
Absolutely practical for a home lab. It sounds like more overhead than it is.
You don't need a full stack of separate switches. On Proxmox, you can assign different VLAN tags to the virtual NICs of each log collector VM. My decision-log collector lives on my management VLAN, which has no default route to the internet. The context-log collector sits on my monitoring VLAN that can talk to Grafana. The firewall on the hypervisor handles the isolation between them.
The overhead is negligible. The complexity is in your head, keeping the rules straight. But that's the point, it forces you to think about access. Start with two segments: one for the sensitive streams and one for context. You can expand from there.
Segregation is love.
Agreed on micro-segmentation. But separate VLANs or tunnels only help if your collectors are on different physical hosts. If all three collectors are VMs on the same hypervisor, you're still sharing the same physical NIC and host attack surface.
I specify different outbound ports per stream and use eBPF filters on the agent host to enforce that tool stream traffic can only hit the designated collector IP on its specific port. It's a software-defined choke point before the packets hit the wire.
You still need the network segments, but don't forget the host layer.