Cognotik | CausalInferenceTask - Industrial Reasoning Engine

ExecutionConfig.jsonJSON

{
  "observed_effect": "High latency in checkout service",
  "potential_causes": [
    "Database connection pooling",
    "Recent deployment of Auth module",
    "Network congestion"
  ],
  "evidence_sources": ["logs/checkout/*.log"],
  "input_files": ["src/services/AuthService.kt"],
  "build_causal_graph": true
}

→

Session UI OutputRendered

Causal Analysis Results

Root Cause: Auth Module Token Validation

graph TD

Auth_Deploy -->|Increased CPU| Latency
DB_Pool -.->|Correlation| Latency
Confounder[Traffic Spike] --> Auth_Deploy

Mechanism: Synchronous crypto calls blocking event loop
Confidence: High (85%)

Test Workspace Browser

Explore actual analysis artifacts and Mermaid diagrams generated by the CausalInferenceTask.

Configuration Parameters

Field	Type	Description
`observed_effect` *	String	The observed effect or outcome to explain (e.g., a bug or performance drop).
`potential_causes`	List<String>	Specific hypotheses to investigate. If empty, the agent identifies causes from evidence.
`evidence_sources`	List<String>	Glob patterns for logs, metrics, or data files to be used as evidence.
`input_files`	List<String>	Specific files or patterns (e.g. `*/.kt`) to be examined as primary input.
`related_files`	List<String>	Additional files for context that are not primary evidence but relevant.
`build_causal_graph`	Boolean	Whether to generate a Mermaid-based visualization of the causal chain. Default: `true`.
`identify_confounders`	Boolean	Whether to explicitly look for variables that create spurious correlations. Default: `true`.

Embedded Execution (Headless)

Use the UnifiedHarness to run this task as a standalone tool in CI/CD or scripts.

import com.simiacryptus.cognotik.plan.TaskType
import com.simiacryptus.cognotik.plan.tools.reasoning.CausalInferenceTask.Companion.CausalInference
import com.simiacryptus.cognotik.plan.tools.reasoning.CausalInferenceTask.CausalInferenceTaskExecutionConfigData

fun runAnalysis(harness: UnifiedHarness, projectDir: File) {
    val config = CausalInferenceTaskExecutionConfigData(
        observed_effect = "High latency in checkout service",
        evidence_sources = listOf("logs/checkout/*.log"),
        input_files = listOf("src/services/AuthService.kt"),
        build_causal_graph = true,
        identify_confounders = true,
        task_description = "Root Cause Analysis for Checkout Latency"
    )

    harness.runTask(
        taskType = CausalInference,
        typeConfig = TaskTypeConfig(), // Default static config
        executionConfig = config,
        workspace = projectDir,
        autoFix = true
    )
}

Direct Instantiation

val task = CausalInferenceTask(
    orchestrationConfig = config,
    planTask = executionConfigData // As defined above
)
task.run(agent, messages, sessionTask, { result -> println(result) }, config)

Prompt Segment

The following logic is injected into the orchestrator's system prompt: