An intelligent agent that knows our architecture, reads our logs, queries our metrics, searches our incidents, and investigates production issues like a senior engineer who’s been here since day one.
Give it an alert, an error, or a question about the system. Here’s what it brings to the table.
Searches past incident tickets by alert name, service, or error pattern. Instantly surfaces past root causes, resolutions, and open action items before you even look at logs. Pattern matching against your team’s institutional memory.
Given an alert name, finds the matching runbook with battle-tested investigation steps. Dozens of runbook pages and knowledge base articles at its fingertips.
100+ custom metrics. CPU, memory, event loop delay, error rates, blast radius fleet metrics (pod count, restart sweep, session load, memory outlier detection).
Real-time queries with trace correlation. SQL-like aggregation for top-errors, deployment before/after comparison, blast radius scoping (tenants, sessions, pods), and infrastructure audit logs.
Sees a stack trace in the logs? It clones the service repo, locates the exact source file and line, reads the code, and explains the logic flaw. Works across all services. Context-aware code correlation powered by deep architectural knowledge.
Built through autonomous learning — the agent analyzes each service, mapping request flows, dependencies, and service criticality automatically. It understands the full cloud-native architecture, including the request lifecycle, layered security model, failure cascades, and dependency graph. Includes detailed insights into multiple services, covering critical paths, metrics, common failure modes with symptom-to-cause mapping, and key configuration details.
Compares any two releases. Shows merged PRs, linked tickets, authors. Finds which release contains a specific change.
Before root cause analysis, quantify the impact: affected tenants, sessions, pods, regions, and duration. Multi-dimension scoping with SLA classification (Negligible to Critical) and escalation guidance. Data-driven, never assumed.
Service A is down? Complete outage. Service B is down? Broken flow only. Structured blast radius determination for any failure.
To guarantee the agent doesn’t hallucinate findings or skip critical diagnostic phases, its behavior is governed by a strictly sequenced protocol. It didn’t just run queries — it systematically encoded the exact method a senior engineer uses to diagnose complex systems, culminating in a structured investigation report.
MANDATORY first step. Calculate absolute start/end times (±1h) to prevent temporal drift. Extract the alert’s exact UTC time, compute query boundaries, and state the window explicitly. All subsequent queries use these absolute timestamps — never relative time windows that silently drift.
Query incident tracking and knowledge base systems for similar past incidents or runbooks to establish a known baseline. If a matching pattern is found, reference immediately — skip redundant investigation.
Parse the alert and scope impact across tenants, sessions, services, and regions. Quantify the blast radius with data-driven metrics: who is affected, how many, and how severely. Determines escalation level — from “negligible” to “critical.”
Scan core metrics (CPU, memory, errors, latency) for correlated anomalies. Identify whether the signal is isolated or part of a broader systemic pattern across the infrastructure.
Aggregate and classify errors to map the failure surface across services and pods. Top-errors ranking, pod distribution, multi-service sweep — building the full error landscape.
Follow the failing request via distributed traces to isolate the exact point of failure. Cross-service span hierarchies, slow span identification, and request flow visualization.
Link the issue to source code and recent deployments to identify the root cause. Locate the source file from stack traces, compare error rates before vs. after deploys, and surface the exact change that broke it.
The mandatory adversarial gate. Re-validate all assumptions — time window, region, environment, queries. Does the root cause timestamp align with the alert? Does the evidence actually support the conclusion? Any mismatch invalidates the result. Born from a real misdiagnosis where a wrong time window produced a plausible but incorrect answer. A single CRITICAL failure invalidates the entire investigation.
A real alert came in at 4:53 PM on a Thursday. Here’s exactly what happened.
2026-03-05T14:53:00Z UTC. Query window: 13:53–15:53 UTC.
TEST_TENANT_1 is the synthetic test tenant — not a real customer. POP: POP-EU-1, EDGE IP: 192.0.2.170.
envStatus: inactive and version v1.79.0. The primary-lb namespace looks like it’s been decommissioned.
| POP | Failures |
|---|---|
| POP-EU-1 | 48 |
| POP-EU-2 | 28 |
| POP-US-1 | 26 |
| POP-AS-1 | 25 |
| POP-US-2 | 24 |
| POP-ME-1 | 23 |
dial tcp 35.246.81.205:8080: i/o timeout — repeated across multiple primary-lb tenants.
v1.79.0 namespace being inactive/scaled down.
| Service | Errors | Note |
|---|---|---|
| core-engine-service | 32 | Single tenant, single pod |
| api-gateway | 19 | Context canceled (normal) |
| edge-proxy-service | 0 | — |
| auth-service | 0 | — |
v1.79.0 has been decommissioned as part of the v1.80.0 rollout. The egress router is still routing primary-lb traffic to endpoints that no longer accept connections, causing dial tcp i/o timeout across all POPs.
v1.79.0 decommission is intentional| Dimension | Detail |
|---|---|
| What | Monitoring plane — SyntheticTestNavigationFailures. primary-lb detected as inactive (EDGE_CONNECTIVITY_FAILED) across all POPs. |
| Where | POP POP-EU-1 (alert target), but failures span 6 POPs. CORE_SYS region: region-eu-west. Not POP-specific — systemic primary-lb issue. |
| When | 2026-03-05T14:53:00Z (UTC). First failures at ~15:08 UTC. Detected by monitoring alert. |
| Who | Synthetic test tenant TEST_TENANT_1 (not a real customer). Zero customer impact. |
| Why | primary-lb namespace v1.79.0 decommissioned during v1.80.0 rollout. Egress router can no longer reach decommissioned endpoints. |
| Mitigation | Update synthetic tester config to stop testing primary-lb, or wait for self-suppression. No code change needed. |
| Owner | Infra/deployment team — synthetic tester config not updated post-rollout. |
| Severity | SEV-3 (Medium) |
Built with a layered approach: generic reusable skills that any agent can leverage, plus SRE-dedicated extensions that apply an operational perspective on top.
Real-time log queries across all environments with trace correlation, transaction-centric analysis, and scoping filters. Not SRE-specific — can be used by developers, QA, or other agents on demand.
SQL-like aggregation over logs. Top-errors ranking, pod distribution, deployment before/after comparison for regression detection. Reusable for any analytical workflow.
Query 100+ custom metrics. CPU, memory, event loop delay, error rates, fleet-level metrics (pod count, restart sweep, session load, memory outlier detection). Links to dashboards included.
Trace API spans, cross-service latency, and request flow visualization. Follow a single request across the entire service mesh to pinpoint where it breaks.
Given a stack trace, clones the service repo, locates the exact source file and line, reads the code, and explains the logic. Works across all services. Reusable by any developer or agent.
Compares any two releases. Shows merged PRs, linked tickets, authors. Finds which release contains a specific change. Version-by-timestamp lookup and commit diffs.
Searches past incident tickets by alert name, service, or error pattern. Surfaces past root causes, resolutions, and open action items. Pattern matching against institutional memory.
Full-text search across internal documentation, runbooks, and knowledge base articles. Alert-to-runbook mapping for instant context retrieval.
Direct log queries against team-owned infrastructure components — synthetic testers, routers, proxies, and load balancers across multiple cloud projects and regions.
Full observability across the traffic chain — from edge to backend. Cross-project log correlation: query routing layers for upstream errors, synthetic testers for external results, and core services for health — all in a single investigation flow.
Mandatory impact scoping before root cause analysis. Quantifies affected tenants, sessions, pods, regions, services, and duration. Data-driven escalation from “negligible” to “critical” — never assumed, always measured.
Mandatory adversarial gate. Cross-checks every query against the original alert parameters: time window, cloud project, region, namespace, pod, and conclusion-evidence consistency. Born from a real misdiagnosis. A single CRITICAL failure invalidates the entire investigation.
Structured blast radius determination for any failure. Service A is down? Complete outage. Service B is down? Broken flow only. Instant service-failure-to-impact mapping using proven troubleshooting patterns.
Every cloud project, cluster, region, namespace, endpoint, and health URL across all environments. Copy-paste query templates for any environment. The context needed to ask the right question.
Request lifecycle, dependency graph, security model, communication topology, and failure cascade patterns. Built through autonomous learning — the agent mapped the system itself.
Critical paths, configuration, metrics, failure modes, and symptom-to-cause mapping for every core service. Dense domain knowledge that enables accurate interpretation of raw telemetry.
Complete service registry: repo, language, role, dependencies, and alert patterns. Schemas, thresholds, and cross-service relationships — all reusable across other agents.
Every log field documented. Log envelope structure, payload fields, tracing context, protocol-specific fields, and error shapes.