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Ejentum

Retrieve a task-matched cognitive operation from the Ejentum Reasoning Harness and inject it into the next LLM step. Each operation is engineered in two layers: a natural-language procedure plus an executable reasoning topology (graph DAG with gates, parallel branches, and meta-cognitive exits). Eight modes: four dynamic (reasoning, code, anti-deception, memory) plus four adaptive (adaptive-reasoning, adaptive-code, adaptive-anti-deception, adaptive-memory) that pre-fit the operation to your task via an adapter LLM. Dynamic available on all tiers including the 30-day free trial; adaptive requires Go or Super.

Actions8

Adaptive Reasoning Harness

AI-generated

Summary

Retrieves a task-specific reasoning scaffold from the Ejentum Reasoning Harness API, rewritten by an adapter LLM to fit your task, for injection into a later LLM step. Requires a Go or Super tier account.

Inputs

  • query (required) — Short description of the task you're about to perform (e.g. "diagnose intermittent 503s under load"). The harness uses it to match and adapt the scaffold. It is trimmed, and an empty or whitespace-only value throws an error for that item.

Output shape

One output item per input item, with json containing mode ("adaptive-reasoning"), query (the trimmed query) and response (the raw API response body).

The scaffold is a natural-language procedure plus a topology DAG, but the source doesn't define the exact response structure. Each item is paired to its input item. With Continue On Fail enabled, a failed item returns { error: message } instead of stopping the workflow. The request is a POST to the credential's base URL, or https://api.ejentum.com/harness/ if none is set, and authenticates with the ejentumApi credential.

Examples

Example 1: Give a downstream AI agent a task-fitted reasoning procedure before it diagnoses an incident.

Operation: Adaptive Reasoning Harness; Query: ={{ $json.incidentSummary }}. Pass the response field into the next LLM node's prompt.

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