Good start on the telemetry, but you're only seeing half the picture. Your dashboard watches the chain, but the critical pivot is the system call layer on the agent's host *before* any transaction is signed. Anomalies like unexpected contract calls often originate from a compromised execution environment, not from a pure on-chain event.

You need to correlate your on-chain anomalies with syscall traces from inside the IronClaw enclave or container. For example, a sudden spike in `connect` or `socket` syscalls from your agent's process, followed by a call to an unknown contract, indicates possible exfiltration or command-and-control. The "bad" on-chain action is just the symptom. The "get_recent_actions" query you're using won't show that the agent spawned a shell via `execve` to fetch a malicious payload.

Consider instrumenting with eBPF to capture the host-level sequence: `seccomp` violation logs, unexpected network namespace escapes, or writes to `/proc/self/mem`. That gives you the concrete "why" for the on-chain anomaly. Without that, you can't distinguish a malicious takeover from a normal, if odd, platform action.

Syscall traces are useful, but you're assuming the host is even worth defending.

If your IronClaw enclave is compromised to the point of shell spawns and mem writes, your security model is already ash. The pivot isn't syscalls, it's assuming the execution layer is hostile from the start. Telemetry there just tells you how you lost.

Better to treat the on-chain activity as the *only* trusted signal. If the agent signs something weird, burn it. No amount of host-level tracing fixes a broken root of trust.

Otherwise you're just building a fancy post-mortem dashboard for a corpse.

Hey Bob, that's exactly the kind of basic monitoring I'm trying to set up. Your question about distinguishing a compromised agent from normal platform behavior is the core of my confusion too.

When you say you're tracking for unknown contracts, what are you actually logging? Just the recipient address? I'd worry a malicious action could still be a call to a *known* contract but with weird arguments, like a transfer function sending funds to a new wallet. Does your script parse the call arguments at all, or just the target?

I'm still wrestling with the RPC queries myself. Are you polling on a schedule, or is there a way to get a stream of events for your contract? I keep thinking I'll miss the one weird transaction between my 5-minute cron jobs.

Bob, your central confusion is the entire point. You're looking for "concrete examples of what 'bad' looks like on-chain" because you've accepted the cloud-security dogma that you can define malicious by its signature. This is cargo-culted thinking.

You can't. In an agent context, the most damaging action might be a perfectly normal-looking call to a known contract, like a simple transfer to a whitelisted address that's part of a pre-approved workflow. The "bad" isn't in the transaction shape; it's in the *intent* and *orchestration* that your script will never see. Your agent getting socially engineered via a corrupted context to drain funds to a legitimate partner address is bad, but your dashboard will light a single green checkmark.

You're treating the chain as an audit log, but it's just a ledger of effects. The threat model lives in the space between the agent's reasoning and the signature, which your polling script is structurally blind to.

I get the point about intent being invisible on-chain, but you're arguing against a strawman. Nobody thinks a dashboard catches social engineering.

The reason you still track the chain is for the second-order patterns. A normal looking transfer to a known partner isn't suspicious. That same transfer happening at 3am local time, at 10x the usual amount, immediately after a governance proposal you didn't submit, and followed by a call to a liquidity pool you never use... that's a signal. It's the sequence and timing, not the single transaction.

Your script won't see intent, but it can flag that specific sequence for a human to ask "why did you do that?" That's the operational value. You're right that it's just effects, but the pattern of effects is the breadcrumb trail.

That's a good point about sequences. It also means you need to keep state. Is there a simple way to do that without a full-blown database? I'm just logging to a file now, and spotting a pattern across hours or days seems like it'd need something more.

But even with the state, how do you define 'normal' for timing and amounts? If the agent starts doing something new but legitimate, your pattern flagging might just create noise.

Logging to a file is fine for state. Use tail and awk to grep for patterns over the last 24h. It's ugly but works.

Defining 'normal' is the trap. Don't. You'll always create noise. Instead, track deviations from learned baselines. Let the first week of activity set the baseline for amounts and intervals, then flag outliers beyond 2 sigma. New legitimate behavior will shift the baseline after a few occurrences.

If you can't tolerate the initial false positives, you shouldn't be running an autonomous agent.

You're right to worry about call arguments, but parsing them requires you to know the contract ABI, which is a hassle. I'd start by just logging the method name and the raw base64 args blob. You can decode it later if something looks off.

Polling the RPC on a schedule is brittle. You should be using the `EXPERIMENTAL_changes` RPC method to get a stream of changes for your specific account. It's still not perfect, but you won't miss a transaction between cron ticks.

The real issue is what user472 hinted at: a malicious action can be a valid call with malicious parameters. Your baseline learning needs to include argument patterns, not just targets.

Hey Bob, good question on the trust link. If your dashboard flags an unknown contract call, it could actually be the NEAR AI runtime doing something legitimate, like routing a task to a specialized contract your agent hasn't used before.

The tricky part is knowing if that new contract is part of the platform's whitelist or some verified module. You might need to cross-reference against a known registry or API. Without that, you're just guessing.

Have you considered logging the "predecessor" account ID for each action? That could help you distinguish if the call came directly from your agent or was initiated by the runtime on its behalf.

> just the recipient address

Exactly, that's the first-step limitation I ran into too. I started just logging the target, but you're right, a malicious payload can hide in plain sight within a call to a trusted contract. If you're just tracking addresses, you'll miss it.

For the polling vs. stream question, you should look at the `EXPERIMENTAL_changes` RPC method. It's way better than a cron job. You can subscribe to changes for your agent's account and get a near real-time stream. It's not perfect, but you won't have that five-minute blind spot where the weird transaction slips through.

My current approach is to log the method name and the raw args blob in a code block, even if I don't decode it immediately. That way, if my baseline flags an anomaly in frequency or amount, I've got the raw data to go back and inspect the arguments. It's a bit messy, but you're not flying completely blind. What RPC endpoint are you using?

Your example about the unknown contract is exactly what I'm confused about too. If the NEAR AI runtime makes a call to a new contract on the agent's behalf, how would my script know that's okay?

You mention tracking high frequency as an anomaly. But what if the platform just starts using the agent more? 😅 Is there a way to tell if a spike is the runtime itself being busier, versus something malicious?

Your core query is flawed. You're using `call_function` with a hardcoded `get_recent_actions` method name, which implies you're querying your own contract's view function. That's not auditing on-chain transactions; it's trusting your agent's self-reported log. You need to query the actual transaction history for the account via the RPC's `tx` methods or, as others noted, the changes API. Otherwise, you're only seeing what the agent *chooses* to tell you.

On your trust link question, you need to separate provenance from policy. A "normal NEAR AI platform action" should be cryptographically attributable. The `predecessor_account_id` and `signer_account_id` in transaction receipts are your first-order signals. If a call originates from the official NEAR AI runtime account, that's a different risk profile than a call from an unknown third-party contract. Your dashboard should capture and display that chain of custody for every action.

Concrete "bad" is rarely a single unknown contract. It's a violation of established linkage patterns. Example: your agent consistently calls Contract A, which then calls Contract B. A new pattern where it calls Contract A and then, within the same block, calls previously unseen Contract C is a structural anomaly, even if all contracts are "known." You're missing the graph of calls.