Integration · Developer and DevOps

Surface Datadog observability context inside Salesforce Agentforce

Datadog is the observability platform that engineering and DevOps teams use to monitor application performance, infrastructure health, and error rates across the stack. Salesforce holds the customer record and the service history for the products Datadog is monitoring. When a customer contacts support about a degraded experience, the most actionable context often sits in Datadog — error rates, latency spikes, the specific service or region affected. When Emerge Digital connects Datadog to Agentforce, a service agent can read the relevant Datadog dashboard state or active monitor alert during a customer conversation rather than waiting for an engineering contact to relay what the observability platform is showing.

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What this unlocks

Active monitor and alert status readable during service conversations: when a customer reports an error or degraded performance, an agent can check Datadog for active alerts affecting that customer's service region or application tier before engaging — so the response is grounded in what the monitoring platform is showing, not a guess.
Service health context for customer-facing agents: Datadog's synthetic monitors, uptime checks, and error-rate metrics reflect the actual customer experience — an agent that can read these speaks to what the customer is experiencing with the specificity that vague 'we are investigating' responses lack.
Customer impact visible in incident response: when a Datadog alert triggers an engineering response, the corresponding Salesforce accounts likely to be affected are valuable context for the on-call team — an agent can surface customer tier, contract SLAs, and open cases to the incident response context.
Post-alert follow-up with affected accounts: when a Datadog monitor returns to green after an alert period, an agent can identify the Salesforce accounts that were affected and initiate proactive follow-up — so customers hear about the resolution rather than discovering it themselves.

In the customer journey

Customer reports an error — Datadog confirms it

A customer contacts support reporting that API calls are failing. Before engaging, the agent checks Datadog — an error rate spike on the relevant API endpoint is visible, started 12 minutes ago, and the engineering team has been alerted. The agent communicates this accurately: the issue is known, the team is responding, and an ETA will follow — rather than opening a parallel investigation that duplicates what engineering is already doing.

Customer impact added to the incident

A Datadog monitor triggers a P1 alert. The engineering team has the platform context; what they may not have is the customer impact picture. An agent reads the Datadog alert, identifies the Salesforce accounts with active contracts on the affected service, surfaces the customer tier and SLA commitments to the incident channel, and flags which accounts have the lowest SLA tolerance — so the incident response prioritises appropriately.

Monitor returns to green — affected accounts follow up

A Datadog monitor resolves after a 45-minute alert. The agent identifies the Salesforce accounts that submitted cases or were on the affected service during the window, prepares resolution notifications tailored to each account tier, and routes them to the account owners for review — so the customer loop closes automatically.

Why not Datadog's native alerting integrations?

Datadog integrates with Slack, PagerDuty, and other platforms to route alerts to engineering and operations channels — well-suited to keeping engineering informed during incidents. What it does not provide is Datadog metric and alert data queryable by an Agentforce agent in real time during a customer service conversation: a service agent cannot ask a Datadog Slack alert for the current error rate on a specific service, surface relevant Datadog dashboards during a call with an affected customer, or identify which Salesforce accounts are on the service a Datadog alert just flagged. Emerge Digital builds the retrieval layer that makes Datadog observability data available to agents at the customer-facing layer.

Datadog's value in an Agentforce integration sits entirely in the service and incident response stages — it is the operational ground truth for what is actually happening with the product while customers are experiencing it. Grounding service agents in Datadog data means customer-facing communications during an incident are accurate, specific, and coordinated with what engineering is seeing rather than disconnected from the operational reality.

How Emerge integrates Datadog

Emerge Digital connects Datadog to Salesforce Agentforce as a consulting engagement. We map which Datadog monitors, metrics, and alert data types agents need to read, configure the Salesforce account matching that surfaces customer impact during active alerts, define the post-alert follow-up workflow for affected accounts, and set the access boundaries that govern which agents can read which Datadog data. The integration is designed around the services and monitoring configuration your engineering team already has in Datadog.

How we structure an engagement

Related integrations

FAQ

Can the agent acknowledge or mute Datadog monitors?

By design, monitor management decisions stay with engineering. Agents read alert status and surface customer impact; they do not modify monitor configuration, acknowledge alerts on behalf of the on-call team, or mute alerts that should remain active.

How is this different from the PagerDuty + Agentforce integration?

PagerDuty is the incident routing and on-call management layer — it handles escalation, acknowledgement, and resolution workflows. Datadog is the observability layer — it captures what is happening in the system. Many organisations use both: Datadog detects the issue, PagerDuty routes the response. Emerge can connect either or both to Agentforce depending on where the data your service agents need to read actually lives.

We use New Relic instead of Datadog — can you connect that instead?

Yes. The observability integration use case — surfacing system health and alert context during customer service conversations — applies equally to New Relic, Dynatrace, and Grafana. Emerge builds the integration to fit the observability platform your engineering team uses.

How long does a Datadog + Agentforce integration take?

A focused engagement typically runs three to six weeks: mapping which Datadog monitors and metric data agents need to read, configuring the customer impact surfacing logic, building the post-alert follow-up workflow, and testing service conversation and incident response scenarios. Complexity scales with the number of Datadog services in scope and the sophistication of the customer-impact mapping.

Ground your agents in Datadog.

Tell us what your agents need to read and write in Datadog, and we'll design the integration and the governance around it.

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