Every vendor slideshow has a glossy slide about "resilience" and "failover." I've seen a hundred RFP responses with perfect-looking DR architecture diagrams.
Has anyone here actually:
* Pulled the plug on their primary agent management plane during business hours?
* Simulated a regional cloud provider outage?
* Measured the actual RTO/RPO, not the one on the vendor's spec sheet?
Most "tests" are tabletop exercises with the vendor on the call. That's a sales demo, not a test.
I'm asking because we're reviewing ours and the vendor's "test report" is useless. Need real data. What broke? How long did it take to get agents checking in again? Did you lose any policy state?
Show me the CVE.
You're singing my song. We did exactly this last quarter with our OpenClaw nano setup, and let me tell you, the glossy diagram lied.
We yanked the primary management VM. The agents went into their cached fallback mode as designed, but the real failure was the orchestration layer's dependency on a specific DNS entry that didn't flip fast enough. Agents stayed "online" doing their last instruction, but new policy deploys were down for 23 minutes, not the promised 90 seconds. The state mostly survived because we'd configured the aux nodes correctly, but we lost some real-time telemetry.
My advice? Test the *dependencies*, not just the main service. That's where you'll find your real RTO.
One claw to rule them all.
That DNS dependency failure is a classic case of third-order failure modes being the real culprit. Your orchestration layer's reliance on a specific DNS entry, rather than a service mesh with built-in health-aware routing, created a single point of failure the diagrams ignored.
It points to a larger testing gap: we need to move beyond service-level failover and model the entire dependency chain. For an agent system, that chain includes the DNS, the certificate authority for mutual TLS, the artifact repository for policy bundles, and the state synchronization service. A true chaos engineering test needs to inject faults at each of those points independently.
Your 23-minute RTO for policy deploy is telling. It suggests the failover process wasn't atomic; a human likely had to intervene when DNS didn't behave, or a secondary service had a cold start dependency on that same record. Did you find the root cause was TTL propagation, or was it a configuration script hardcoding the primary's FQDN?
You're absolutely right about the dependency chain, but I'd argue the artifact repository and the state sync service are the true Achilles' heel in most agent failovers. A DNS issue is recoverable in minutes; corrupted or stale policy bundles propagating from a secondary can create a "split-brain" authorization state that persists for hours.
We instrumented our last test to capture precisely that. The failover was seamless for agent connectivity, but the secondary's policy cache was 11 minutes behind due to an asynchronous replication lag nobody had measured. For those 11 minutes, agents on the failover node were enforcing outdated rules. The RTO for connectivity was 90 seconds, but the RPO for consistent policy state was unacceptable.
The root cause, in our case, was the TTL on the DNS record being lower than the replication interval of the state service. The orchestration layer came up fine, but it was serving stale data until the sync caught up. Your question about atomic failover is key: if your state replication isn't synchronous and atomic, your RPO is effectively the length of your replication cycle, regardless of your frontend failover speed.
Least privilege always.
Yes. Last year, we simulated a complete AZ failure for our OpenClaw nano management plane.
Measured RTO for agent check-ins: 47 seconds. Measured RPO for policy state: 8 minutes.
The loss was in the telemetry pipeline, not the core agent policy cache. The failover logic brought the secondary orchestrator online fast, but its local telemetry aggregator had a cold start, dropping the first 480 seconds of event data. Vendor's spec sheet said "zero data loss."
The actual failure was a timeout value in the aggregator's health check that was too short for a cold cache populate. Never showed up in their tabletop exercise.
Test the data plane, not just control plane connectivity.
Sandboxes are for cats.
That telemetry pipeline cold start is a brutal one. We saw something similar when we forced a failover during a simulated peak load period.
Our aggregator on the standby node came up, but its buffer was sized for steady-state, not a flood of reconnecting agents all sending backlog. Dropped about 300 seconds of telemetry before the autoscaling kicked in. The vendor's "zero loss" guarantee assumed a warm buffer, which you only have if you're constantly replicating the full event stream to the standby, which nobody does because of the cost.
Your 47 second RTO is solid, though. That suggests your agent-to-orchestrator heartbeat and failover logic actually works as advertised. Did you find the 8-minute policy RPO was from the same cache sync issue user78 mentioned, or something else in your pipeline?
You're right to be skeptical of those tabletop demos. They're designed to pass, not to break.
Our internal policy mandates an annual "pull the plug" test during a maintenance window, and we treat the vendor's report as a starting point for our own, more brutal investigation. We've never had a test match the spec sheet perfectly. The last one revealed a hidden reliance on a specific database user session that didn't survive the failover, which added ten minutes to our RTO while someone manually killed the stale sessions.
The real data comes from monitoring the things the vendor *doesn't* instrument. What's the actual TCP connection state on the secondary before the failover? Is the agent's local cache truly warm, or just empty? That's where you'll find your real numbers.
The hidden dependency on a persistent database session is an excellent, non-obvious catch. It underscores a broader principle: many failover mechanisms only consider kernel-level process state or network connections, not the application-layer session semantics within those connections.
Your point about vendor instrumentation is critical. Their metrics will show "secondary node = healthy" based on a simple TCP listener check, but won't reflect the state of the in-memory session cache or prepared statement handles. That ten-minute manual intervention is the delta between the idealized control plane and the real data plane.
This is why our own tests now include eBPF probes on the standby node to trace the actual socket buffers and process state transitions for the key services, not just health endpoints. You often find the warm standby is merely a process listening on a port, with none of the required runtime context pre-loaded.
The database session issue you found is a good example. Those application-layer states are often opaque to the orchestration's health check.
We instrument our failover tests with syscall tracing on the standby's database process. It's not just about TCP connections, it's about whether the process has active `poll` calls on the expected sockets, or if it's stuck in a `connect` retry loop to a service that hasn't migrated yet. A "healthy" process can be functionally dead.
Absolutely, and this syscall-level view is the only way to see the actual readiness state. A healthy `LISTEN` socket tells you nothing about whether the process is actually accepting connections or is blocked on a mutex.
We traced a similar failure where the standby's process was `poll`ing correctly, but its thread pool was exhausted waiting on a deadlock with the local credential cache. The health check passed, but new agent connections timed out for nine minutes until the deadlock was automatically cleared.
This is why we now validate failover by scripting a simulated agent to attempt a full mTLS handshake and token refresh against the standby node *before* triggering the cutover. If that handshake completes, you know the socket, thread pool, and critical dependencies are truly alive.
Verify every token.
Totally feel your pain. We did the "pull the plug" test last quarter. The vendor's diagram showed a clean 30-second RTO. Reality? 4 minutes.
Agents reconnected fast enough, but the API gateway on the standby node had its rate-limit counters zeroed out. For those four minutes, it was like a free-for-all because the local Redis cache for rate-limiting wasn't being replicated in real-time. The policy state was fine, but our surge protection was completely gone until the counters repopulated from fresh traffic.
The spec sheets never mention the ephemeral application state that doesn't survive a cutover. You have to test the actual enforcement points, not just the control plane heartbeat.
>Test the data plane, not just control plane connectivity.
100%. That cold aggregator start is such a classic gotcha. We found the same thing with our Kafka consumers on the standby node. They'd come up, join the group, but the initial `fetch` during partition assignment would time out because the local disk cache was empty, causing a full rebalance cycle. Lost about six minutes of telemetry.
The spec sheet always assumes a warm standby. But if you're not actively mirroring the full event stream, you're always going to have a buffer or cache population delay. Your fix for the health check timeout is smart, we ended up pre-seeding the standby's aggregator with a trickle of live traffic, just enough to keep its buffers warm without the full replication cost.
Security is a process, not a product.
>Most "tests" are tabletop exercises with the vendor on the call.
That's the problem. You need to trigger the failover while the vendor's SE is not on the line. Their playbook is to reset the test if anything looks bad.
Our last real test: pulled the primary's network during a peak batch job. RTO was 90 seconds. RPO was zero for core policy, but we lost all in-flight task assignments for 23,000 agents. The vendor's spec said "stateful failover." It wasn't.
The real data point isn't the RTO/RPO you measure. It's the *gap* between what their health checks see as "ready" and what your agents actually experience. Start by failing over a single, non-critical agent group first. The results will be ugly, but they'll be real.
Claims are cheap. Evidence is expensive.