Port + Datadog: The Missing Layer in Self-Healing Infrastructure

Datadog sees everything. It knows latency is spiking on shopping-cart-api-v2. It knows the error rate crossed the SLO threshold 47 seconds ago. What it doesn't always know is who owns this service, what changed in the last deploy, whether there's an open CVE tied to this repo, what the runbook says to do, or whether this is the third time this week the same scaling pattern has been triggered.

That missing layer is organizational context. And without it, Datadog, similar to other observability tools, can detect an incident but it can't auto-resolve one. A human has to close the gap.

Port is that missing layer. It is the engineering system of record that gives Datadog the context it needs to act, not just alert.

Why Alerts Still Wake People Up at 2am

The promise of observability was that better data would mean fewer fires. Teams invested in Datadog, wired up every service, and the alerts got more precise. But the on-call rotation didn't get any easier. While it got more accurate around what to page people about, it didn’t get any faster at resolving the issue that triggered the alert.

The problem was never detection. Detection is solved. The problem is the triage loop that follows: five to fifteen minutes of a groggy engineer reconstructing organizational knowledge that the system should already have. Who owns this? What changed? Is there a runbook? Should I scale it or roll it back?

That loop exists because monitoring systems are built to observe infrastructure. They are not built to hold organizational knowledge: service ownership, team structure, deployment history, security posture, business criticality. When an incident fires, the observability platform hands off to a human because there is nowhere else to hand off to.

Port closes that gap. It gives automated systems - including autonomous AI Agents - somewhere to go to easily retrieve the missing piece of organizational context.

Port as the Engineering System of Record

Port's Context Lake is a live catalog of everything your observability platform doesn't know: which team owns each service, who is on call, what tier the service is, what the runbook prescribes, what changed in the last deploy, what security vulnerabilities are open, and how all of these entities relate to each other.

The integration between Port and Datadog works in two directions, and both directions matter.

Port automatically pulls Datadog real-time data into the unified context lake. It continuously ingests Datadog resources (monitors, SLOs, services, teams, hosts) into Port's Context Lake as queryable, correlated entities with rich metadata. The service: tag on a Datadog monitor becomes a live relational link to the service entity in Port, which carries team ownership, runbook references, deployment history, Snyk findings, and more. Runtime telemetry and organizational knowledge become a single context graph.

Port MCP feeds Port's context back into the incident workflow. Self-healing automation also requires the ability to query Port's organizational knowledge at incident time and act on it. Port's MCP server exposes the Context Lake as a live, queryable source that an AI agent can call against mid-workflow. When an incident fires, the agent calls Port MCP to retrieve service ownership, the applicable runbook, recent deploy history, and on-call assignment, then uses that context to decide what action to take and executes it. Port's organizational intelligence becomes a runtime input to autonomous  resolution, not just a dashboard for humans to read.

Together, Ocean and MCP create a closed loop: Datadog signals what is wrong, Port knows what to do about it, and the system acts.

Self-Healing Incidents: What the Loop Looks Like Here is what autonomous incident resolution looks like when Port and Datadog are wired together.

shopping-cart-api-v2 latency spikes. Datadog fires. The webhook reaches Port instantaneously. 

An AI agent receives the alert and calls Port MCP to query the Context Lake. It retrieves the full organizational picture in a single pass: this is a Tier-1 service owned by the Payments team, the runbook for this monitor class prescribes a horizontal scaling action before escalation, the last deploy was 22 minutes ago, and there are no open critical Snyk vulnerabilities on this repo.

The agent executes the runbook automation: scale the service, wait 90 seconds, check SLO recovery. Depending on the agentic workflow you set up and the scope of the agent’s permissions, it can even trigger a coding agent to suggest a PR to fix an issue, test it and deploy it, then validate the fix. Determining that the SLO recovered - the agent closes the incident, logs the action, and posts a summary to the Payments team Slack channel. No page sent. No engineer was woken up.

If the SLO does not recover, the agent escalates with a fully assembled incident packet: the Datadog alert details, the service owner, the recent deploy diff, the runbook steps already attempted, and the on-call engineer pre-populated. The human who gets paged is not starting from scratch. They are making one decision on a situation that the system has already diagnosed.

This is self-healing infrastructure. Datadog provides the signal. Port provides the intelligence via MCP. The system is autonomous and can resolve what it can or hand off what it can't, with full context already assembled.

The Organizational Context That Makes Autonomous Resolution Possible

Four categories of Port data turn a Datadog alert into an autonomous action rather than a page to a human to start digging and fix.

1. Ownership and escalation paths. Port knows which team owns each service and who is currently on call via the PagerDuty integration. When an incident fires, the agent queries Port MCP and knows immediately where to route escalation if needed, without a human looking it up.
2. Runbooks and resolution procedures. Runbooks live in Port as structured, queryable entities linked to the services and monitor types they apply to. When a monitor alerts of an identified breached threshold, the agent can retrieve the applicable runbook from Port MCP and execute the prescribed steps programmatically.
3. Change context. Port tracks deployment history. When an incident fires, the agent surfaces the last deploy, the diff, and the engineer who shipped it as part of the incident packet. For a large class of production incidents, the cause is a recent change. Port makes that connection automatically.
4. Security posture. Port's security integrations surfaces open vulnerabilities as entities in the catalog, linked to the services they affect. The agent can check security posture via Port MCP before deciding whether a standard runbook applies or whether the incident needs a different escalation path.

Preventing the Incident Before It Fires

Self-healing infrastructure isn't only about resolution and accelerating MTTR. It's about eliminating the conditions that cause incidents in the first place.

Port scorecards let platform teams define what production readiness means and enforce it continuously across every service in the catalog. A Tier-1 service scorecard checks whether there is a linked Datadog monitor tracking error rates, an active SLO with a defined threshold, a runbook attached, and zero open critical vulnerabilities. Each rule queries live entity relationships in real time, not a quarterly checklist.

When a service fails a check, its grade degrades visibly in the Port dashboard. The changed state triggers an automated message to the service owner with the specific failing rules and a self-service action that installs the missing Datadog agent and applies the standard tag configuration. Monitoring coverage gaps get closed before a production incident exposes them. Many customers also automate this process with an AI Agent that listens for drift and fixes the configuration, installs required components, etc.

New services are handled at scaffolding time. When a developer provisions a service through Port's self-service action, the workflow drops the Datadog agent into the deployment manifest, applies organizational tags from the Port catalog (team:, tier:, env:, cost-center:), and calls the Datadog API to generate the standard monitors and SLO for that service tier. The service has CPU threshold alerts, error rate monitors, and a live SLO before the developer writes their first line of code. The service: tags are correct from day one, which means Port's relational mappings and automated runbook matching work correctly from day one.

What Changes for SRE Teams

The on-call rotation is not lighter because incidents are detected faster. It is lighter because a class of incidents never reaches a human at all.

The incidents that do get escalated arrive with full context already assembled. The engineer who gets paged is not spending the first ten minutes reconstructing service ownership and deployment history. They are making a single decision on a situation the system has already triaged, attempted to resolve, and documented.

Platform teams stop chasing service owners to add monitoring. The scaffolding workflow makes observability the default. The scorecard enforces it continuously for services that drift.

The infrastructure becomes self-aware in the organizational sense, not just the technical one. It knows who owns what, what the standard resolution is, and what has changed. Datadog provides the eyes. Port provides the knowledge. Together, they close the loop from signal to action- enabling self-healing incidents.

Getting Started

If your on-call rotation is carrying work the system should be doing, that's the gap Port closes.

If you’d like help getting started, we’re here to help! Sign up for a demo and we’ll walk you through Engineering Intelligence that gives your metrics context.

Docs:

• Datadog integration — connect Datadog to Port's Context Lake
• Port MCP server — expose organizational context to AI agents at incident time
• Self-healing incidents — the full solution overview‍
• Production readiness scorecards — close coverage gaps before incidents happen