Proactively run tasks like deployment monitoring, operational reports, and resource optimization on a schedule or on trigger
Run your workflows on a cadence, or fire agents on event triggers like deploys, alerts, and other production events
Watches #engineering for deploys; runs post-deploy health checks.
On deploy event
Posts daily deploy + incident digest at 7 AM PT.
Daily at 7:00 AM
Monitors orders-db-cluster for query regressions and lock contention.
On alert event
Weekly resource-utilization report across clusters and services.
Weekly · Fridays
Tasks like deployment monitoring, operational reports, resource optimization, and more with a template library to start from
Triggered by a deploy notification in #engineering (compare URL, smoke test link, "deploy kicked off"). Post all updates in the deploy thread.
| Environment | Type | Log channel | Cluster |
|---|---|---|---|
dev0 | Pre-prod (staging) | #engineering-log-dev | dev0-cluster |
app0 | Production | #engineering-log-prod | app0-cluster |
rocket | Production (dogfood) | #engineering-log-prod | app0-cluster |
Environment progression: dev0 → app0 → rocket. Each gates the next.
{github_run_id}.{attempt}-{7_char_sha} (e.g. 24893095514.1-dac7506). The github_run_id is the dev0 build run, not the prod deploy run.Before running the health check, verify the prod deploy pipeline completed successfully for this deploy tag (Deploy to Cells workflow for app0, Deploy to Rocket from Cells for rocket).
Background agents pre-investigate every item so you open Resolve to verified findings and recommended next steps
Good morning. Overnight, agents reviewed 47 items across 6 channels.
4 need your attention · 2 scheduled for today · 41 resolved cleanly
Finding: Deploy 8ab7b0a hasn't started on app0-cluster — previous deploy still in progress. Health check will run once the rollout begins.
Finding: Scheduled 8:00 AM run did not fire — agent was unresponsive between 04:35–16:29 UTC. Will resume on the next scheduled run tomorrow.
Finding: Likely candidate: new code path in order-fulfillment.ts hits a slow query at p99 only under load. Re-checking after next traffic window.
Finding: Caught at deploy time and rolled back cleanly. No customer impact recorded. No further action needed.
Use chat for live debugging, multi-turn investigations, or to trigger background agents on demand
What's going on with checkout-router p99 today? Is it the deploy at 10:14 PT?
Yes — drift starts ~2h after the deploy. p99 was steady at ~142ms through 12:00 PT, then climbed to ~387ms as traffic ramped.
Likely candidate: new code path in order-fulfillment.ts hits a slow query at p99 only under load. Error rate and throughput look normal — this is latency-only.
Set up an agent to watch checkout-router latency after every deploy going forward. Alert me if drift >25% within 2h.
Done. I'll watch every deploy that touches checkout-router and alert you if p99 drifts more than 25% within 2 hours.
Background agent created
Removing the toil of investigations, war rooms, and on-call.
“Resolve AI allowed us to move from hours to minutes for investigations in many incidents. We pull fewer engineers into war rooms, on-call is materially better, and that translates directly to advertiser trust and revenue protection for a billion-dollar ads business.”
“Resolve AI proved it could deliver real results in a constrained environment. It identified dependencies, surfaced accurate root causes 72% faster than our teams, all while integrating cleanly into our existing stack.”
“Resolve AI has changed how our teams work through production incidents. What used to take hours of manual investigation and coordination across teams now gets resolved in a fraction of the time. Our engineers aren't only faster, they're focused on the work that actually drives impact.”
“We’ve seen the value of AI in development, and now we’re applying that same approach to production. We started by partnering with Resolve AI for alert triage, incident investigation, and root cause analysis. We’ve seen positive signs of improvement in mean time to resolve for our critical incidents, and the North Star is self-healing systems.”
“What excites me most about Resolve AI's background agents is that I’m no longer starting from zero. The alerts are already investigated. The deployment summaries are already written. The findings are verified and the next steps are waiting for me. A lot of the operational work I used to handle manually is now happening continuously in the background with my oversight. I’m still making the important calls, but I can operate at a scale that just wasn’t possible before.”
“Resolve AI feels like a teammate who’s already done half the work. It tells me immediately if something’s critical or can wait, saving countless hours and frustration.”
“Resolve AI helps my team navigate incidents by correlating signals across logs, metrics, traces, and code automatically. Instead of switching between multiple platforms hunting for clues, we get immediate context. At our deployment velocity, that speed makes all the difference.”
“Incident response at our scale isn't about collecting more signals. It's about understanding why something is failing, quickly enough to limit customer impact.”
Proactive, time-triggered checks on a cadence.
Agents watch rollouts and investigate before alerts fire.
Automated knowledge capture after every interaction.
Anything that runs on a schedule or trigger. Deployment monitoring, health checks, operational reports, post-incident cleanup, resource optimization, runbook validation. If your team does it repeatedly, agents can run it.
Start from the template library. Pick a workflow template, customize the scope and schedule, and assign it to the right level (org, team, or personal). Agents start running immediately.
Time-based schedules (daily, weekly, custom cadence), event triggers (deploys, alerts, threshold crossings, investigation completion), and on-demand via chat.
Everything your background agents have pre-investigated. Each item includes findings, severity, and recommended next steps. Items are ranked by what needs your attention first.
Yes. Click any item to see the full investigation, or open a chat session to ask follow-up questions and explore further with agents.
Org-level agents run company-wide policies and monitoring. Team-level agents are scoped to your team's services and workflows. Personal agents handle your individual preferences and recurring tasks.
Yes. If a scheduled health check or deployment monitor detects something complex, it can escalate to Agent Teams automatically.
Machines on-call for humans