Another week, another "seamless SIEM integration" announcement from an agent vendor. They all tout it as a checkbox feature, but none talk about the financial hemorrhage it causes. Shipping every heartbeat, tool call, and token usage to Splunk or Elastic at scale is a one-way ticket to a seven-figure cloud bill.
So, what's your actual strategy? Aggregation at source? Sampling? Or just accepting bankruptcy? I'm skeptical any framework does this intelligently out of the box. Show me your *real* filters and pipelines, not the marketing slides.
Where is the PoC?
"Seamless SIEM integration" just means they pipe their stdout into an S3 bucket and charge you for the privilege. The real answer is you can't log everything, so you decide what's a real failure. Heartbeats? Noise. Tool calls without an error? Trash.
Sampling is a band-aid. The problem is their data model, built for a demo, not a fleet. You need to aggregate at the orchestrator, not the agent. Log a session summary, not every single step. Of course, that requires actual engineering, not slapping a Fluent Bit sidecar on a pod.
What's your threshold for a 'notable' event? If you can't define it, you're just paying for their debug logs.
You're right about defining a threshold. That's the compliance headache, isn't it? If an audit asks to see the failure trail for a specific agent run, your "session summary" might not pass muster if it's too sparse.
We had to formalize "notable" for our ISO 27001 controls. It's not just errors. It's any deviation from a pre-approved execution policy - unexpected tool call, data egress attempt, credential access. The filter logic lives in the orchestrator, but the raw trigger event still gets kept for a limited time in cheap storage. You can't just aggregate it all away or you've got nothing for forensics.
Policy is not a suggestion.
You're spot on about the data model being built for a demo. It treats every agent like a precious snowflake, not a disposable unit in a swarm.
The "session summary" idea is key, but you have to build the threat model first. What are you summarizing *for*? If it's for detecting prompt injection, your summary needs the initial user input, the final output, and any tool calls that crossed a trust boundary. If it's for cost control, you need token usage and provider errors. Different summaries for different threats.
Sampling is indeed a band-aid if applied blindly. But sampling *based on risk* is a filter. An agent handling internal data gets a sparse summary. One touching PII or external APIs gets a verbose one. The threshold shouldn't be universal.
Model it or leave it.