cairnobs/docs/phase-8-processing-design.md

Phase 8 processing design: rules, where they run, and how they arrive

Status: Design, drafted 2026-09-05. Not approved and not implemented. Nothing in Phase 8 is started. This is a proposal to argue with — the sections marked Open are genuine decisions, not rhetorical ones. If implementation shows this is wrong somewhere, fix this doc in the same change rather than letting them drift.

Why this design, in one paragraph

Phase 8 puts rule-based work on records in flight: drop, mask, rename, derive, parse, sample, suppress duplicates, aggregate. The hard part is not the transformations — it is that the same rule has to mean the same thing in a Rust agent and a Go ingest tier, and that rules are pushed to hosts over a channel deliberately built to be incapable of carrying anything dangerous. So this design starts from the distribution channel and the safety invariant it protects, and derives the language from them, rather than designing a language and asking later how to ship it.

The constraint everything else follows from

agent-management-design.md states an invariant plainly: every remotely editable field "degrades the agent's behavior without ever cutting off its ability to receive the next correction." That is why ingest.endpoint and TLS material are permanently non-editable — a bad value there kills the only channel that could fix it.

Processing rules are the first remotely editable thing that can execute. A rule that panics, loops forever, or allocates without bound is not a degraded setting; it is a broken agent.

A correction to what the roadmap says

The roadmap change in #21 stated that a bad rule "strands the agent exactly the way a corrupted ingest.endpoint would." Reading apply_override's actual semantics, that is too strong, and the difference matters enough to write down rather than quietly soften:

An override lives only in the running process's memory. It is never written to agent.toml. A restarted agent boots from its local config alone and re-syncs on its next successful check-in.

So a rule set that crashes the agent produces a crash-loop, not a strand:

boot (clean, no rules) → CheckIn → receive rules → apply → crash → boot …

The agent checks in on every iteration of that loop. The platform can always push a corrected or cleared override, and the agent will take it. That is a materially better failure mode than being stranded, and it exists by accident — the "don't persist overrides" choice was made for simplicity (no filesystem writes on read-only base images, no reconcile-at-startup state machine), not for safety.

This design promotes that accident to a constraint. Persisting overrides to disk would convert every crash-loop into a strand, because the agent would apply the fatal rules before its first check-in and never reach one. Anyone proposing offline-boot override persistence later must solve this first. It is now load-bearing.

The residual harm is still real and still worth engineering away: a crash-looping host ships almost nothing, and the loop runs at whatever the check-in cadence is until a human notices.

Decision 1: total evaluation, with apply-then-verify as a backstop

Two candidate guarantees were named in #21. This design takes both, in priority order, because they solve different halves.

Total evaluation — the rule language is constructed so a rule set cannot panic, loop unboundedly, or allocate without limit. This is the primary guarantee, and it is an absence of the failure rather than a recovery from it. It is purchasable only by keeping the language declarative and typed, which the next section does.

Apply-then-verify — the agent treats a newly received rule set as provisional: it records the version it is about to apply, applies it, and marks it good once it has survived one full check-in interval. If it boots and finds a provisional version recorded that never went good, it reports the failure and runs without rules rather than reapplying them.

Note the tension with the constraint above: apply-then-verify needs a small amount of state to survive a restart, which is exactly the persistence the previous section forbids. The resolution is that what persists is a version stamp and a failure flag, never the rule set itself — a few bytes, and a host that cannot write even that simply loses the backstop and keeps total evaluation. The agent must degrade to "no rules" on a write failure, never to "apply anyway".

Open: whether the backstop is worth its complexity in the first release, given total evaluation should make it unreachable. My view is yes — "should be unreachable" is what every crash-loop was before it happened — but it is a defensible cut for a v1.

Decision 2: the rule shape

A rule is a matcher and an ordered list of typed actions. No expressions, no arbitrary code, no user-supplied control flow.

rule
  match:   field, operator, value        (all must hold)
  actions: [ action, action, … ]         (applied in order)

Actions, and whether each is trivially total:

Action	Effect	Total?
drop	discard the record	yes
drop_fields / keep_fields	remove or whitelist attributes	yes
mask	replace matched substring with a fixed token	yes, with a linear-time engine
rename	move a field	yes
derive	set a field from a literal or another field	yes
parse_json	parse message into fields	yes, with a depth and size cap
parse_regex	named captures into fields	yes, with a linear-time engine
sample	keep 1 in N	yes
suppress_duplicates	collapse identical records within a window	yes, with a bounded cache
aggregate_count	replace repeats with a count record	yes, with a bounded cache

Deliberately absent: arbitrary expressions, loops, user-defined functions, and anything resembling eval. Cribl's rule language is JavaScript; this is less expressive on purpose. It is also the only shape that can be pushed to ten thousand hosts and audited by reading it.

Why regex does not break totality

Both implementation languages ship linear-time, non-backtracking regex engines — Rust's regex crate and Go's regexp are both finite-automata based, with no catastrophic backtracking to guard against. This is a real piece of luck: the usual reason regex is unsafe in a pushed rule set does not apply here, in either language, without doing anything clever.

Open, and it has a cost: the agent currently has no regex dependency at all. Adding the full regex crate is on the order of a megabyte-plus of binary, against a project whose agent pitch is a small static musl binary. regex-lite is far smaller and still linear-time, at the cost of some syntax and speed. Alternatively parse_regex and mask could be ingest-side only in v1, which sacrifices the "redact PII before it leaves the host" claim in positioning.md — the one thing that most needs to run on the agent. My recommendation is regex-lite on the agent and full regex at ingest, with the conformance suite (below) restricted to the syntax both accept.

Decision 3: one spec, two implementations, one conformance suite

Rules run in Rust on the agent and in Go at ingest. "The same rule does the same thing in both places" is the whole promise, and two hand-written implementations will diverge — not maybe, eventually.

The deliverable that prevents it is a language-neutral conformance suite: a directory of cases, each an input record, a rule set, and the expected output record, in JSON. Both implementations run it in their own CI. A case is added for every bug found in either.

This is the same discipline /hack's fixtures already apply to ingest shapes, applied to semantics instead. It should be built first, not last — the suite is the specification, and the prose above is a summary of it.

Decision 4: distribution reuses the channel that exists

Fleet management already delivers desired state (agent-management-design.md). Rules become one more field on DesiredOverride:

repeated ProcessingRule rules = 8;

extra_file_paths = 7 is the precedent to follow exactly: a repeated field with no meaningful "unset", where the platform always submits the complete desired list and an empty list unambiguously means "no rules right now". The existing version stamp and applied_override_version echo give rollout observability for free — you can already see which hosts have taken a rule set and which have not.

Open: there is no staged rollout today. An edit goes to every agent matching it on their next check-in. For batch sizes that is fine; for executable rules it is the difference between breaking one host and breaking all of them. A canary mechanism — apply to N hosts, require them to report good, then widen — is not in the fleet design and would be new work. My view is that this is the single most important thing to add alongside rules, and it may deserve to gate the feature.

Where each rule runs

Agent-side is the default and the cheaper place: data reduced before the wire costs nothing to transport, store or index, and it is the only place PII can be removed before it crosses the network.

Ingest-side exists for rules needing context the agent lacks, and for changing behaviour without waiting for a fleet rollout.

Open: whether a rule declares where it runs, or whether the platform decides. Explicit placement is simpler to reason about and to debug; automatic placement is friendlier and much easier to get subtly wrong. I lean explicit, with a validation error when a rule asks for something its location cannot do.

The motivating workload is real

The maintainer's own workstation, measured 2026-09-05 while the dev agent ran against a live stack:

rate	source	message
~39/min	Discord	Discord 1.0.155, byte-identical every time
~22/min	foreground_boost	Checking active tasks

Those two are 308 of 325 journal entries in five minutes — roughly 60% of one host's volume, from two processes saying nothing. Everything else on that host, kernel firewall drops included, is single digits.

Today there is no way to do anything about it: the journald source's only filter is a single-unit allowlist, so the choice is the whole journal or one unit, with nothing in between.

This is the v1 acceptance test. A suppress_duplicates rule and a drop rule should remove ~60% of that host's volume, measured before-and-after against real data rather than a fixture. If the first release cannot do that, it is not finished.

Open questions, collected

1. Is apply-then-verify in v1, or is total evaluation alone enough?
2. regex-lite on the agent, full regex at ingest, and a conformance suite limited to their common syntax — or regex ingest-side only, giving up on-host redaction for v1?
3. Does a canary rollout gate the feature, or ship after it?
4. Explicit per-rule placement, or platform-decided?
5. Does aggregate_count emit a synthetic record, and if so what does it look like to a query that is not expecting one? This design does not answer that and should before anyone builds it.