Umodel Rca

UModel RCA

Investigate to a root cause over the UModel object graph. Use this as a method, not a script: let evidence decide the next query, keep competing hypotheses alive, and cite every conclusion with an object-graph, metric, or log source.

Load umodel-query first. It provides the read surfaces used here: .entity, .topo, .umodel, .entity_set | entity-call get_metrics, and .entity_set | entity-call get_logs.

Setup

Same CLI as umodel-query — load that skill and follow its Setup; the essentials:

1. Ensure umctl is on PATH — UModel's read CLI:

command -v umctl || go install github.com/alibaba/UnifiedModel/cmd/umctl@latest   # needs Go 1.22+

No Go toolchain? Download a prebuilt umctl from the repo's Releases, or build from a clone (make build-cli → ./bin/umctl). Verify with umctl version.

2. Point umctl at your UModel server and pick the workspace:

export UMCTL_ADDR=http://<host>:8080     # or pass --addr per call, or `umctl configure`
umctl workspace list -o json            # the bundled demo workspace is `demo`
umctl query run <workspace> "<SPL>" -o json

For the incident-investigation demo with real Prometheus and Elasticsearch plans:

sh examples/incident-investigation/deploy/start.sh

If the user gives a different address, workspace, or transport, follow that instead.

RCA Loop

1. Orient

Identify the incident and symptomatic entity:

• Search for an incident ID, service name, or degraded/SLO-critical entity.
• Read the impacted service fields: name, environment, owner, tier/SLO, status, recent errors, and linked datasets/runbook.
• Discover neighbors and runbook metadata before narrowing too early.

Useful starting shapes:

umctl query run <workspace> ".entity with(domain='platform', name='platform.service', query='<service-or-symptom>') | limit 10" -o json
umctl query run <workspace> ".entity with(domain='platform', name='platform.incident', query='<incident-or-service>') | limit 10" -o json
umctl query run <workspace> ".umodel with(kind='runbook_set')" -o json

2. Characterize

Confirm the symptom with telemetry, not only entity status or alert text.

• Pull target service metrics such as latency, error rate, timeout rate, QPS, retry rate, saturation, and error budget where available.
• Pull target service logs for timeout, retry-exhausted, circuit-breaker, dependency, deploy, or config landmarks.
• Prefer a short current window plus a longer timeline around suspected changes.

Use get_metrics / get_logs through the entity. The object graph fills the storage selectors such as service_id; do not guess labels manually. In open source these calls return executable Prometheus / Elasticsearch plans. Run the returned plans as described in umodel-query/references/metrics-logs.md.

3. Hypothesize

Build a candidate set before deciding:

• Recent deployments on the degraded service.
• Config changes on the degraded service and upstream callers, especially retry, timeout, circuit breaker, pool, rate-limit, and feature flag changes.
• Upstream traffic pressure from callers, promotions, campaigns, batch jobs, or order flows.
• Downstream dependency saturation or channel/provider failures.
• Runtime capacity issues such as workload/pod/node pressure.

Do not treat recency as causality. A recent change is only suspicious when it has a plausible mechanism, a topological path to the symptom, and telemetry/log movement at the right time.

4. Gather Cross-Domain Evidence

Traverse both directions around the symptomatic service:

• Upstream callers: .topo relations where the caller sends traffic into the degraded service.
• Downstream dependencies: services, routers, channels, providers, databases, queues, or runtime workloads the degraded service calls or depends on.
• Change graph: platform.config_change and platform.deployment linked by target, affected service, or business trigger.
• Business graph: active promotions, campaigns, order flows, traffic forecasts, and actual traffic.
• Runtime graph: workloads, pods, namespaces, clusters, and capacity signals.

Use runbooks as a scaffold. Read linked runbook_set observations, knowledge, and toolkits, but still verify each observation with graph/metric/log evidence.

If the path is ambiguous, read references/evidence-patterns.md for generic positive and negative evidence patterns. Do not load it when the normal loop is enough.

5. Correlate And Discriminate

Build a timeline with absolute or relative times from the evidence:

• When did the symptom start or breach?
• When did candidate changes take effect?
• Which metric/log series inflected at each candidate time?
• Does topology explain how the candidate can affect the degraded service?
• Does the mechanism quantitatively explain the blast radius or load?

Rule out candidates explicitly when evidence contradicts them. Common exclusion tests:

• Deployment: trivial change_summary, successful rollout, no rollback, no latency/error/timeout inflection near deploy time, or wrong blast radius.
• Config change: no retry/timeout/pool/circuit behavior, wrong target, no upstream/downstream path, or no metric/log movement after activation.
• Traffic event: no active event, actual traffic close to forecast, or no path to the impacted service.
• Capacity issue: saturation appears after the overload rather than before it, or scaling pressure is localized away from the failing path.

Prefer the cause that explains the full timeline with the fewest unsupported assumptions. Mark confidence lower if telemetry is missing or only indirect.

6. Conclude

Return a concise incident diagnosis:

## Diagnosis
Symptom: <quantified service impact>

Timeline:
| Time | Evidence | Source |
|---|---|---|
| <T> | <event or inflection> | <object graph / metric / log reference> |

Root cause:
<one sentence with the triggering change/event and mechanism>

Causal chain:
1. <trigger>
2. <amplifier/mechanism>
3. <saturation/failure mode>
4. <user-visible impact>

Ruled out:
- <candidate>: <why excluded, with source>

Recommended action:
<read-only recommendation, tool if present, risk, confirmation requirement, and
verification metrics>

Confidence: <high|medium|low> because <evidence strength and gaps>

Every row in the timeline and every ruled-out item must cite its evidence type: object graph, metric, log, or runbook knowledge.

Evaluation Discipline

When validating this skill on a known scenario, keep the ground truth out of the agent prompt. Use a fresh SubAgent or fresh thread, pass only the alert, UModel endpoint, workspace, and skill paths, and score whether the agent independently collects graph, metric, and log evidence before concluding.

Stay read-only. Recommend remediation, but do not execute rollback, rate-limit, scale, or restart actions unless the user separately confirms the exact action.