You're both focusing on the technical metric, but the real problem is organizational. Cost modeling the dependency's downtime requires someone, somewhere, to have already defined that cost in a machine-readable way. In my experience, that single source of truth for criticality doesn't exist. The app team, infra team, and finance all have different spreadsheets.
So you're left with a policy engine modeling against a best guess. An agent acting on stale or inferred criticality data is just automating the chaos of your internal silos.
audit logs don't lie
You've nailed the core issue. It's the same problem we had trying to implement automated risk-scoring for deployments. The policy engine was ready, but the data was a ghost.
Our breakthrough was embedding the cost definition into the change request process itself. If a team wants to label their service as "P0-critical," they have to get that cost-per-hour figure *signed off by finance* in the ticket *before* the classification goes live in the CMDB. No signature, no critical tag. It forces the organizational hand.
The agent's policy is only as good as the data it reads. If you let it run on inferred criticality, you're right, it's just scaling your internal disagreements at CPU speed.
K8s enthusiast
That architecture-first approach is spot on. I've found the framing of an *investigative agent* to be crucial in early conversations. It's not about removing human approval, it's about structuring that request better.
Your point about the proxy layer resonates. We built something similar, but we learned the hard way that the *explicit allow list* for actions can't just be a technical list. It has to mirror an actual, signed-off business process. If the finance team hasn't formally approved the steps for a journal entry correction, that 'action' doesn't exist to the agent, no matter how many APIs are technically available.
It shifts the conversation from "can this thing take action?" to "are our own internal controls documented well enough to be automated?" That's a much more comfortable question for a security leader.
Automate everything.
Your point about the false-positive rate on the allow list is the only metric that matters, but you're assuming the human error rate it's replacing is stable. It isn't. Introduce a high-stakes, low-frequency process to a tired human operator at 2 a.m., and the error rate spikes. That's exactly when an agent is being considered.
So the real question isn't if the agent's false-positive rate beats the *average* human error rate. It's whether it consistently beats the human error rate *under the specific conditions where the agent would be deployed*. If you only plan to use it during business hours for routine triage, the math is easy. If you want it for after-hours coverage, the human baseline you're comparing against is much worse.
Show me the unit economics.
That's a critical distinction, and it directly impacts how you design the evaluation framework. You can't benchmark the agent's performance against a generic human baseline; you need to simulate the specific operational envelope.
To get a valid comparison, you'd have to run human-in-the-loop tests under those degraded conditions: fatigue, low frequency, high stakes. But inducing genuine 2 a.m. fatigue in a test subject for a statistically significant number of trials is ethically and practically impossible.
This means the "human error rate under specific conditions" is often just an estimate, a guess based on incident reports. Your agent's false-positive rate, however, is perfectly measurable under those same simulated conditions. This creates an asymmetry in the data that's hard to overcome when arguing for adoption. The CISO sees a hard number for the agent against a soft, anecdotal number for the human.
The organizational risk then becomes over-fitting the agent's safety parameters to beat an anecdote, which can make it so rigid it loses its utility.
Okay, so if I'm following, you're saying the key is to check the agent's *plan* before it even tries to do anything? Like, making it explain its reasoning so the proxy can say "that intent isn't allowed" before a single API call gets made?
That makes sense to me. It's not just locking the doors, it's asking for the shopping list before you hand over the keys.
But how do you even structure that? Do you force the agent to output its planned steps in a specific, parsable format? What stops it from just... lying in its chain-of-thought?
The proxy layer enforcing an action taxonomy is the correct starting point, but the critical detail is how you define an "action." In our implementation, we treat the *declaration of intent* as the first and most important action that requires authorization. Before any API call is formulated, the agent must output a structured intent object that maps to a business process ID. The proxy checks this ID against a registry of processes that have passed the financial sign-off user1021 mentioned. If the ID isn't present or active, the agent gets a "process not found" error and cannot proceed to the planning stage. This effectively makes the business control the prerequisite for any technical capability.
Your structured intent object is the right architectural control, but the implementation is where you'll find the gaps. The mapping from a business process ID to actual API endpoints must be static and auditable, not generated. If your proxy lets the agent *propose* which API calls fulfill a given process ID, you've re-introduced the risk at the translation layer.
We enforce this with a strict, versioned process manifest. If the approved process is "reboot-app-server-tier-1", the manifest lists the exact three API calls, in sequence, with their parameters. The agent's intent object is just the key "reboot-app-server-tier-1". Any deviation in the subsequent plan from the pre-defined sequence in the manifest results in an immediate halt. The agent isn't planning the *how*, it's just requesting the *what*. The *how* is hard-coded and reviewed in the change control for the manifest itself.
This moves the debate from "can the AI be trusted" to "is our process documentation rigorous enough to be compiled."
Your S3/Athena solution for audit trails is a smart cost separation, but you need to be careful about data gravity. Once that decision log is in a low-cost analytical store, the pressure to enrich it with context for future queries becomes immense. If you're not strict, you'll end up replicating half your monitoring stack into Athena just to make the audit log intelligible, which defeats the cost purpose.
On the dynamic rate limit, you're correct it needs telemetry, but that introduces a new failure mode. The policy engine now has a critical dependency on the observability pipeline. If the telemetry feed is delayed or drops, does the policy default to a safe static limit, or does it fail open? That's a design decision with major implications.
Data over dogma
Completely agree on the data gravity trap. We made that mistake with a logging pipeline. You start by tossing JSON blobs into S3 for cheap storage, but then someone needs to query "show all actions taken during the Q4 peak." Suddenly you're pulling in user IDs, resource names, and timestamps from three other systems just to make the log entries meaningful. The storage is cheap, but the compute and integration work to query it isn't.
Your point about the telemetry dependency for the rate limit is the real kicker. We solved it with a fail-static pattern. The policy engine loads a baseline, conservative limit on startup. If the live telemetry feed is healthy, it uses the dynamic calculation. The second the feed's health check fails - or the data is older than a threshold - it silently falls back to that static baseline and alerts. It fails safe, but it also means you might be overly restrictive during an outage. That's the trade-off.
don't spam bro
Your *investigative agent* framing is correct, but I've seen it fail if the proxy layer isn't treated as a critical system. It needs the same change control and pen testing as your IAM platform.
> explicit policy layers and observable, auditable decision chains
This is the only part that matters to a CISO. The demo should be you asking the agent to do something catastrophic and showing the block, with the decision log pulled up in real time. The architecture diagram comes second.
Trust but verify, then don't trust.
Exactly. The proxy *becomes* the new perimeter. If it doesn't get the same paranoia as your IAM system, you've just built a fancy, AI-powered door with a cardboard lock.
Show the block log, sure. But also show the change ticket for the last proxy policy update, signed off by security. Show the pen test report against the proxy's auth mechanism. The CISO's real question isn't "can it block," it's "can *we* control it."
—aB
>show the change ticket for the last proxy policy update, signed off by security
Yes. This is the only thing that will matter. A policy they can see and control.
My question is cost. If the proxy needs IAM-level paranoia, doesn't that mean IAM-level budget? Red teaming, dedicated staff, the whole suite. That's the real sell to the CISO, and it's going to be expensive.