PagerDuty

Connect an agent via an official MCP server G

Agent access

Drive the product through a documented public API G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Build against official SDKs G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Subscribe to events via webhooks G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Rely on versioned APIs with a documented deprecation policy G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Explore an interactive API reference with runnable examples G

Api quality

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Use an official CLI G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Alerts from my monitoring tools are ingested through documented sources and routed to the right team by conditions I define C

Routing

Define rules that trigger actions automatically on events G

Escalation policies walk unacknowledged pages through multiple steps — delays, fallback responders, and repeat rounds — until someone acknowledges C

Escalation

My agent can acknowledge, escalate, and resolve incidents end to end through a documented API or MCP connection — no dashboard in the loop C

Agent ops

AI writes the incident as it happens — live summaries, drafted updates, and scribed call notes — so responders respond instead of typing C

Ai summaries

Postmortems follow a real workflow — templates, drafting from the timeline, review, and publication G

Postmortems

Build on-call schedules with rotations, layers, time zones, and round-robin coverage that match how my teams actually work C

Schedules

Export all of my data in open formats and leave G

Declare and run an incident from chat — Slack or Teams — with channels, roles, and updates created for me C

Declaration

First-party integrations cover my observability stack — Datadog, Grafana, Prometheus, CloudWatch, Sentry — with documented setup C

Alert sources

Prevent my data from being used to train AI models G

Self-host the core product G

Duplicate and related alerts are deduplicated and grouped so one incident pages one human, not fifty C

Noise reduction

A condition-based workflow engine automates the toil — updates, reminders, field changes — triggered by incident events C

Workflows

AI drafts the postmortem from the incident record — timeline, contributing factors, follow-ups — ready for human review G

Ai summaries

Do everything through the API that I can do in the UI G

Runbooks attach to incidents and their steps can trigger automatically — creating channels, assigning tasks, running diagnostics C

Runbooks

A full mobile app lets me acknowledge, escalate, and resolve from my phone at 3am C

Mobile

Pages reach me over the channels I choose — push, SMS, phone call, and email — with per-channel notification rules C

Paging

An AI investigator digs into the probable cause — correlating changes, telemetry, and similar past incidents — before a human even asks C

Ai investigation

Follow-up actions from incidents are tracked to completion and sync to our issue tracker C

Follow ups

Perform bulk operations across many items at once G

Publish a hosted public status page — custom domain, subscriber notifications — driven from incident state G

Status pages

The incident timeline is captured automatically — alerts, actions, and chat decisions — and I can edit or annotate it afterwards C

Timeline

The platform integrates with the tools around the incident — Jira, Slack, Teams, Zoom, GitHub — so state flows both ways C

Workflow tools

I get reliability analytics — MTTA/MTTR trends, incident load, on-call health — to see whether we are actually improving C

Metrics

Take an override, swap a shift, or request coverage without an admin rebuilding the schedule C

Schedules

Schedule recurring jobs or workflows G

Choose where my data is stored (region/residency) G

Control data retention and deletion G

Opt out of telemetry and usage tracking G

Read the product's source under an open license G

Internal stakeholders get structured incident updates they can subscribe to, without joining the war room C

Communications

Incidents carry defined roles (commander, comms lead) and task checklists so response stays coordinated under pressure C

Coordination

My shifts sync to my personal calendar via a feed so I always know when I'm on the hook C

Quality of life

Run private or internal status pages with access control for customer-specific or employee-only audiences G

Status pages

Version, review, and roll back my automations G