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Agent Integration

Building Reliable Agents with Causely​

Teams are building agents that can query telemetry, run playbooks, and take action.
In practice, these agents struggle to determine what is actually happening, what caused it, and what action is safe.

Access to data is not the problem. Interpreting it reliably is.

The Gap in Today’s Agent Architectures​

Most agent-driven systems run into three core limitations:

Information gap
Agents can retrieve telemetry, but cannot consistently determine what is happening or what matters.

System gap
There is no shared understanding of how services, infrastructure, and dependencies relate to each other.

Execution gap
Agents lack a reliable way to determine which actions are safe and how to coordinate them.

As a result, agents require human interpretation, and automation breaks down at scale.

Where Causely Fits​

Causely provides a system intelligence layer that continuously models how your system behaves. Its services, dependencies, and failure propagation.

Instead of reasoning over raw telemetry, agents interact with structured, deterministic system knowledge.
This allows decisions to be based on how the system actually behaves, not on correlation or heuristics.

Architecture Overview​

[Agent (for example Holmes or custom agent)]
                ↓
[Causely (causal model + reasoning engine)]
                ↓
[Observability + Infrastructure (metrics, traces, logs, alerts)]
  • Agent: orchestrates workflows, queries systems, and takes action
  • Causely: builds and maintains a causal model and provides deterministic reasoning
  • Observability + Infrastructure: provides raw signals and telemetry

What Causely Enables for Agents​

With Causely, agents can:

  • Identify the true root cause of an issue, not just correlated signals
  • Understand service dependencies and failure propagation
  • Evaluate blast radius before taking action
  • Work with structured, machine-consumable outputs
  • Provide explainable and auditable conclusions

This allows agents to move from data retrieval to reliable decision-making.

Example Workflow​

Scenario: High error rate alert

  1. The agent receives an alert
  2. The agent queries Causely
  3. Causely returns:
    • Root cause service
    • Affected dependencies
    • Explanation of why this is the cause
  4. The agent:
    • Notifies the correct team
    • Suggests or executes remediation

Integration Paths​

You can integrate Causely into your agent workflows in several ways:

When This Approach Is Most Valuable​

This architecture is most effective when:

  • You operate distributed systems with many interdependent services
  • You already have observability in place
  • You are building or evaluating automated incident workflows

Summary​

Causely does not replace your agents or your observability stack.

It provides the system intelligence layer required for agents to interpret telemetry consistently, identify true root causes, and take safe, coordinated action.