Agentic Incident Response
Incidents caught and handled before the page
Catches problems early and fixes known ones with approved playbooks.
For head of SRE, IT operations
What changes
Stop mean-time-to-respond reporting. Start seeing it live.
Raises incidents from the first leading signal in telemetry, often before users are affected or anyone is paged, and handles them with approved playbooks. AI agents gather context, correlate signals and propose the fix; approved runbooks take the first action; engineers step in with the evidence already assembled. Toil that playbooks now handle is counted and returned to the team every sprint.
Before · reports you wait for
- mean-time-to-respond reportingreport
- manual runbooks in a wikireport
- ticket closure countsreport
After · live on DevSemantic
- MTPI Mean Time to Predict an IncidentHow early an incident is raised3min↓
- AFR Automated First ResponseIncidents where a playbook acted first71%↑
- THR Toil Hours ReturnedEngineer hours given back each sprint46h / sprint↑
How it works
DevSemantic is a context plane for site reliability and operations. It connects telemetry, services, dependencies, releases and runbooks into one model, so problems are predicted from leading signals, handled by approved automation, and prevented from returning. The four solutions follow the life of a production problem: see it clearly, catch it before it pages, stop it coming back, and stop releases from introducing it.
We model how production really runs
Services, dependencies, releases, telemetry and runbooks in one live model.
We catch the first warning sign
Problems are raised early, often before a user notices or anyone is paged.
Approved playbooks act first
Engineers step in with the evidence already gathered. Every production change still needs a human yes.
Fixed once stays fixed
Root causes are tracked so they do not come back. Releases are checked before they deploy.
What we are measured on
One headline number. Three that back it up.
How early an incident is raised
From the first leading signal in telemetry to an incident raised with its evidence, across incidents that reached impact and those resolved before a page
3min
your baseline · 64 min95% lower in 90 days
Commitment · Down against baseline; more incidents resolved before they pageEvidence · Incident history
-
AFRAutomated First ResponseSupporting
Incidents where a playbook acted first
Share of incidents where an approved playbook took the first action before a person engaged
71%↑ vs baseline
Replaces manual runbooks in a wikiEvidence · Customer's records
-
THRToil Hours ReturnedSupporting
Engineer hours given back each sprint
Engineering hours no longer spent on work now handled by playbooks, counted per sprint with the team
46h / sprint↑ vs baseline
Replaces ticket closure countsEvidence · Customer's records
-
MTTRMean Time to RestoreIndustry comparator
How fast service is restored
From incident start to service restored
31min↓ vs baseline
Replaces reported for comparison with existing toolsEvidence · Incident history
Numbers shown are illustrative. Yours start from your own baseline, measured in the diagnostic.
Proof you can open
Every number comes from a record you own.
Here that record is the incident history. If we cannot show where a number came from, we do not report it.
- day 1 · 09:04signalleading signal · connection pool saturation trend · svc-checkout
- day 2 · 11:21incidentraised with evidence · 00:02:40 after first signal · no page
- day 4 · 13:38playbookapproved runbook: scale pool + shed batch traffic · first action taken
- day 5 · 15:55engineeron-call joined with evidence assembled · confirmed
- day 7 · 17:12sprinttoil returned this sprint: 4h 20m
Questions
What people ask before they start.
Does this replace our incident management and paging tools?
No. They stay. Agents read their alerts and tickets and work inside the same records.
Can an agent deploy its own fix?
No. A deploy always ends at a person.
How does it learn our systems?
From your telemetry, deploy history, runbooks and dependencies, kept current by named engineers through the Semantic Loop.
See Agentic Incident Response on your own estate.
A 30-minute walkthrough with an engineer. We show the MTPI loop running and answer what it would look like for you.
Every solution is sold against one headline KPI, committed for 90 days against your own baseline and reported from evidence you can inspect.
vikat.AI · your guide
Ask Yati
Which solution fits your estate, what the 30-day diagnostic measures, how a pod works inside your team: ask in your own words.