Incident response
Put an operator in the war room.
Mention it in the incident thread. It reads your recent changes, logs and metrics, ranks the likely causes with the evidence, and drafts the fix for your engineer to approve.
- ISO 27001
- GDPR
- Every run auditable
checkout-api p99 latency 2.4 s, SLO 800 ms
What changed?
- Deploys cart-service 4.12.0 released at 02:58
- Config diff cache ttl 300 → 0 in 4.12.0
- Metrics cache hit rate 96% → 31% from 02:59
- Logs checkout-api timeouts calling cart-service from 03:01
- Runbook “Checkout latency” page in Confluence
- 1Likely Cache TTL set to 0 in cart-service 4.12.0Hit rate fell one minute after the release; timeouts follow it.
- 2Effect, not cause Database connections at 88%Rose only after the cache misses began.
- 3Ruled out Traffic spikeRequest rate flat across the window.
Drafted rollback Roll back cart-service to 4.11.3
- up to 90%faster incident resolution
- under 5 minto a root cause with evidence
- 60–70%of alerts diagnosed on the first pass
- up to 70%less downtime
Enterprises running operators on Alomana
What it does
Six jobs it takes off the bridge.
Most of an incident goes on finding the cause. The operator does that legwork and shows its work.
- 01A change went outFinds the deploy, commit or config change before the alert
- 02The signalsReads the logs, metrics and traces around the alert
- 03What you already knowChecks the runbook and past incidents in Confluence or Jira
- 04Several suspectsRanks the likely causes, each with its evidence and what it ruled out
- 05The fixDrafts the rollback, pull request or config change for approval
- 06The write-upDrafts the postmortem from the run record
How it works
Connect. Mention. Approve.
Connect
Slack, Confluence, Jira, your observability stack.
Mention it
Tag it in the incident channel.
Approve the fix
You say yes. It acts and logs it.
Where it stops
The fix waits for your engineer.
- Proposed changes wait for an engineer's yes
- A declined action is not retried
- Every run logged: input, output, tools, user
Run recordINC-4127 · illustrative
- input
- M. Ferraro, on-call SRE: “What changed?”
- tools
- deploys · config diff · metrics · logs
- cause
- Cache TTL set to 0 in cart-service 4.12.0
- fix
- Roll back cart-service to 4.11.3, waiting
- user
- Approved by M. Ferraro · logged
- Measured on
- MTTR
- Time to root cause
- Engineer hours per incident
- People pulled into the bridge
- Works with
- Slack
- Jira
- Confluence
- Snowflake
- PostgreSQL
- Your observability stack via MCP
- Your code host via MCP
- Trusted for
- ISO 27001
- GDPR
- Never trains on your data
- Single-tenant for enterprise
A custom operator typically goes live in up to 4 weeks.
Questions on-call teams ask
What is an AI incident operator?
An autonomous agent your on-call engineers ask during an incident. It reads the recent changes, logs, metrics and runbooks you connect, ranks the likely causes with the evidence, and drafts the fix for an engineer to approve: a rollback, a pull request, a ticket or the postmortem.
How does an investigation start?
An engineer asks: mention the operator in the incident's Slack thread, or ask it in Alomana. It works the incident from there and posts what it checked, what it found and the fix it proposes.
Does it change production on its own?
Changes it proposes wait for an engineer's yes: sends and writes through Alomana's connectors, and MCP tools that declare a write. Connect your observability tools read-only. A declined action is not retried, and every run is logged: input, output, tools and user.
Which systems does it connect to?
Alomana has 20+ connectors, including Slack, Jira, Confluence, Snowflake and PostgreSQL, activated per deployment. Systems outside the library, such as your observability stack or code host, connect through a remote MCP server.
How long does it take to deploy?
A custom incident operator typically goes live in up to 4 weeks, including validation and integration.
Is our production data private?
Alomana is ISO 27001 certified and GDPR-compliant, and your data is never used to train AI models. Enterprise deployments run single-tenant, in a dedicated instance.
Bring one past incident.
Talk it over with sales: what the operator needs from your systems, and how it goes live.
Alomana is a secure enterprise AI platform where autonomous agents, called operators, run business processes end to end. Every run is fully auditable.