How to Use

How to trigger the SRE AI Agent and get incident investigations in Slack.

Trigger the Agent

In any Slack channel where Pagerly is installed, mention the bot with the assist keyword:

@Pagerly assist <describe the issue>

Examples

Message	What the Agent Does
@Pagerly assist seems people are having issues with billing on dashboard	Queries GitHub for recent commits, checks Vercel deployments, searches logs for errors
@Pagerly assist API latency is high for payment service	Queries Datadog/Grafana metrics, checks K8s pod health, looks at recent deployments
@Pagerly assist kafka consumer lag is increasing on orders topic	Checks Confluent for consumer group status, queries Prometheus metrics, checks ArgoCD deploys
@Pagerly assist database connections are maxing out	Queries PostgreSQL for active connections, checks RDS metrics, looks at recent changes

What Happens Next

When you mention @Pagerly assist, the agent:

1. Acknowledges your request instantly in the thread

2. Creates an investigation plan — decides which data sources to query based on your description

3. Queries your connected tools — you'll see live updates like:

   • 🔍 Querying GitHub...

   • 🔍 Querying Vercel...

   • 🔍 Querying Datadog...

4. Produces a structured report — a root cause analysis with:

   • 📋 Incident Report with a unique ID

   • 🔴 Root Cause — what broke and why

   • 📝 What Happened — timeline of events

   • 🔗 Evidence — links to commits, logs, dashboards

Investigations typically take 30–90 seconds depending on how many data sources are connected and how many tools the agent needs to query.

Mapping Data Sources to Teams

The SRE Agent is team-scoped — each on-call team gets its own set of connected data sources. This ensures the agent only queries what's relevant during an investigation.

How to Map

1. Go to SRE Agent Integrations in the Pagerly dashboard

2. Click Add Integration

3. Select the integration type (e.g., Datadog, Kubernetes, GitHub)

4. Enter the credentials (API key, URL, etc.)

5. Select the team this integration belongs to

6. Optionally, add a context prompt — this tells the agent how your team uses this tool

Navigate to SRE Integration page in the dashboard

Select the integration and fill in credentials

Fill in the integration details and map to a team

Context Prompts

When mapping a data source, you can provide a context prompt — a short description that helps the agent query more effectively.

Integration	Example Context Prompt
Datadog	"Our services use service:api-gateway tag. Production env uses env:prod."
Kubernetes	"Production namespace is prod. Critical deployments: api, worker, web."
GitHub	"Main repos: org/api, org/frontend. Production branch is main."
Grafana	"Main dashboards are in the SRE folder. Key dashboards: api-latency, error-rates."

Good context prompts dramatically improve investigation quality. The more the agent knows about your setup, the faster it finds the root cause.

Tips for Best Results

• Be specific — @Pagerly assist API latency is high for payment-service in us-east-1 is better than @Pagerly assist things are slow

• Connect multiple tools — The agent is most powerful when it can correlate signals across logs, metrics, traces, and code changes

• Add context prompts — Tell the agent about your naming conventions, key services, and namespaces

• Use in incident channels — Trigger the agent in your war room channel during active incidents