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Custom Agents

Building Custom Agents with Causely​

Most custom agents can access telemetry, but struggle to determine what is actually happening, what caused it, and what action is safe.

Causely provides the system intelligence layer needed to interpret telemetry consistently and make reliable decisions.

Whether you are building internal incident tooling, AI-driven workflows, or automation pipelines, Causely provides the structured system intelligence needed to interpret telemetry and act safely.

When to Build a Custom Agent​

Building a custom agent is the right approach when:

  • You have internal workflows that do not map to existing tools
  • You want to integrate reliability decisions into your own systems
  • You are building end-to-end automation (detection → diagnosis → action)
  • You need full control over logic, policies, and execution

How It Works​

Custom agents use Causely to move from raw telemetry to structured, system-aware decisions.

Custom agents typically interact with Causely in one of two ways:

Use the Causely MCP Server to provide a standardized interface for agents.

Your agent:

  1. Receives a signal (alert, event, user input)
  2. Queries Causely via MCP
  3. Receives structured outputs (root cause, dependencies, explanation)
  4. Takes action based on those outputs

This is the fastest way to integrate Causely into agent-based systems.

2. API Integration​

For more control or non-MCP environments, you can interact directly with the Causely API.

Your system:

  1. Sends queries to Causely (for example, root cause, topology, health)
  2. Receives structured, machine-consumable responses
  3. Uses those responses to drive logic and actions

What Your Agent Gets from Causely​

When integrated, your agent can:

  • Identify the true root cause of issues
  • Understand service dependencies and failure propagation
  • Evaluate blast radius before taking action
  • Work with structured, consistent outputs
  • Provide explainable decisions

This allows your agent to move beyond querying data to making reliable decisions.

Example Workflow​

Scenario: Alert → Automated Response

  1. Alert is triggered from your monitoring system
  2. Your agent queries Causely
  3. Causely returns:
    • Root cause service
    • Impacted services
    • Explanation
  4. Your agent:
    • Routes to the correct team
    • Executes a runbook or remediation
    • Logs the decision and outcome

Design Considerations​

When building custom agents with Causely:

  • Trust boundaries: define what actions can be automated vs require approval
  • Policy enforcement: gate actions based on risk or impact
  • Observability: log decisions and reasoning for auditability
  • Fallbacks: handle cases where no clear root cause is identified

Next Steps​