AI agents are only as effective as the context they can access. The problem is that most of that context lives outside your observability platform.
TL;DR: OrionIQ now supports more than 600 integrations across the tools your organization already runs, from observability, ticketing, incident management, communication, documentation, and CRM systems to cloud infrastructure, security, and developer platforms. This matters because incidents are never just about telemetry. The context that explains an incident, and the context that tells you how much it matters, lives in systems outside your observability data. With this expansion, OrionIQ’s AI agents can pull that context into an investigation, reason over it together with your logs, metrics, and alerts, and then act on the platforms where your team actually works. In this post, we cover why we did it, what it unlocks for agentic observability, how it works at a high level, and a concrete walkthrough that shows how a checkout incident becomes a clear picture of business impact in Salesforce. We also address the question that naturally comes with connecting more systems: how does this access stay safe?
Traditionally, every incident forces engineers to become detectives, jumping between GitHub, Jira, Confluence, Salesforce, Slack, dashboards, and ticketing systems before they can even begin solving the problem. That investigation often takes longer than identifying the actual technical failure.
ORIONIQ AT A GLANCE
OrionIQ introduces a layer of intelligence and automation across the incident lifecycle, helping teams move from detection to resolution with greater speed and consistency.Key capabilities include:
Telemetry Correlation Across Signals
Aggregates and correlates logs, metrics, traces, and recent system changes into a single incident-level view.
Early Root Cause Hypothesis Generation
Surfaces likely causes at the start of an investigation to accelerate initial diagnosis.
Automated Triage and Context Assembly
Collects relevant telemetry and attaches diagnostic context to alerts and tickets before human review.
Alert Prioritization Using System Context
Evaluates alerts against broader system behavior and historical patterns to determine which require action.
Consistent Execution of Operational Workflows
Applies predefined investigation and response steps in a structured and repeatable way.
Shared Incident Context Across Teams
Maintains a consistent set of diagnostic information as incidents move between support, operations, and engineering.
Signal Value Identification for Cost and Clarity
Highlights high-value versus low-value telemetry to support more efficient data usage and cost control.
The best engineers never investigate incidents using telemetry alone. They gather context from every system that can explain what changed, who is affected, what should happen next, and how the business is impacted.
That’s exactly how OrionIQ’s AI agents investigate. Telemetry answers one question: “What happened?” Context answers the questions engineers actually care about: “Why did it happen?” “Who is affected?” “Has this happened before?”, and “What should we do next?”
For example, an agent that can only see telemetry can tell you a service is failing. An agent that can also see your business systems can tell you what the failure means and start the response. So we made the integration surface as wide as the modern stack itself:
Agentic observability is not about running a traditional chatbot next to your dashboards. It’s about agents that investigate autonomously: forming hypotheses, gathering evidence, and drawing conclusions you can trust. The quality of that reasoning is bounded by the evidence the agent can reach.
Wider context improves the investigation in three concrete ways:

Context is half the value. The other half is activation: the investigation should end where your team’s workflow begins, not in a report that someone has to copy-paste.
Because OrionIQ connects to the platforms your team already uses, an agent can close the loop on the same platforms it investigated:
The agent investigates, concludes, and hands off, and the handoff lands inside your existing process.
Each integration teaches OrionIQ’s agents how to work with that platform: how to connect, what the platform’s API offers, and how to use it well. When an investigation needs external context, the agent selects the relevant connected platform, queries it, and incorporates the results into its reasoning, just as it treats logs and metrics as evidence.
Connections are set up in minutes with your own credentials, and they’re built conservatively: access is read-only by default, credentials are never exposed to the AI model, and each integration is limited to its own platform. You stay in control of what the agent can see and do.
Let’s name it directly. Connecting your CRM, ticketing, and business platforms to an observability vendor means granting access to systems that hold sensitive data. That deserves scrutiny, and it’s a decision we designed for rather than one we ask you to take on faith.
Our side: a certified, audited security posture. OrionIQ runs on the same security foundation as the Logz.io platform: SOC 2 Type 2 audited, ISO 27001 certified, PCI DSS Level 1 compliant, and aligned with GDPR and HIPAA requirements. These aren’t one-time badges; independent third-party auditors review our controls on an ongoing basis. The data your integrations expose to an investigation is handled under the same controls that already protect your log data.
Your side: follow least privilege. The strongest control is granting only the access the agent actually needs. We recommend, and our setup guides walk you through, a minimal-privilege strategy:
Combined with the platform guarantees above (credentials never enter the model’s context, access is read-only by default, and each integration is confined to its own platform), this results in an agent that sees exactly what you decided it should see, and nothing more.
Observability data alone doesn’t tell the whole story. Here’s how this plays out in a real investigation.
The alert. Error rates spike on the checkout API. An alert fires, and OrionIQ begins investigating automatically.
The technical picture. The agent works through logs and metrics, isolates the failing service, and identifies the error pattern: a subset of checkout requests is failing due to timeouts. So far, this is what observability data can tell you. Now comes what it can’t.
What changed, and how to respond. With GitHub connected, the agent reviews recent changes to the failing service and flags a pull request that was merged shortly before the errors began and touched the checkout flow’s timeout handling. From Confluence, it retrieves the team’s checkout incident playbook and the architecture page showing which services sit downstream, and folds the relevant steps into its report.
The business picture. The organization has connected Salesforce to OrionIQ. The agent queries it as part of the investigation:
The activation. With the full picture assembled, the agent closes the loop: the follow-up work is opened with the technical findings and the affected-account list attached, and a summary lands where the response team works, so support and account teams see the same picture engineering sees.
What used to be a chain of manual steps across half a dozen tools, performed under pressure, is now a single investigation. The on-call engineer opens a single report that says not just “checkout is failing,” but “checkout is failing, here’s the code change that likely caused it, here’s the playbook, these enterprise customers are affected, support is already hearing about it, and here’s the ticket.”
This expansion is a step toward a larger goal: observability, where AI agents don’t just analyze your telemetry, but operate with the full context of your business and act within your existing workflows. As the integration catalog grows and more platforms support agent-driven actions, the distance between “an alert fired” and “the response is underway” keeps shrinking.
For further information, feel free to reach out to our Logz.io support team at help@logz.io
Want to see OrionIQ investigate with the full context of your stack? Book a demo or start a free trial today.
Each integration has a guided setup: you provide platform credentials (we recommend a dedicated read-only user where the platform supports one), and OrionIQ verifies the connection. Most take just a few minutes.
No. Credentials are stored securely and injected only when an API call is made. They never enter the model’s context.
Access is read-only by default. Where platforms support actions, agents can help move the response forward, such as opening follow-up work or notifying the right people. You control which platforms are connected and what access they have.
The catalog spans sales and CRM, support and ticketing, incident management, code and CI, cloud infrastructure, security, data platforms, marketing tools, and more, over 600 in total and growing.
The agent selects connected platforms relevant to the investigation at hand, just as it decides which logs or metrics to examine. It only uses the platforms your organization has connected to
Yes. OrionIQ is built on the Logz.io platform, which is SOC 2 Type 2 audited, ISO 27001 certified, PCI DSS Level 1 compliant, and aligned with GDPR and HIPAA requirements, with controls reviewed by independent third-party auditors. Data accessed through integrations is handled under the same controls as your log data.
The catalog grows continuously. Reach out and let us know what you need, and we’ll prioritize accordingly.