Ejentum Reasoning Harness

Ejentum Reasoning Harness

The Ejentum Reasoning Harness is a library of 679 cognitive operations engineered in natural language, organized across four harnesses (reasoning, code, anti-deception, memory) and exposed as MCP tools the agent can call when the task matches their trigger conditions. It targets four mechanism failures common in long agentic chains: attention decay (losing the original task), reasoning decay (compounding errors), sycophantic collapse (agreeing with the user's frame instead of evaluating it), and hallucination drift (asserting unsupported claims with confidence).

Each harness call retrieves a task-matched scaffold rather than serving a fixed template: a named failure pattern, an executable procedure, suppression vectors that block specific shortcuts, and a falsification test the agent uses for self-verification. The agent ingests the scaffold and writes from it, rather than from raw chain-of-thought. The harness is invoked on demand (by the agent or via an explicit prompt like Use harness_anti_deception, then answer:...); it does not auto-run on every turn.

When to Use This Skill

• Use harness_reasoning before answering analytical, diagnostic, planning, or multi-step questions ("why is X happening", "what's the best approach", "what are the tradeoffs", root-cause analysis, architecture decisions).
• Use harness_code before generating, refactoring, reviewing, or debugging code; before architectural changes, algorithm or data-structure choices, dependency-upgrade evaluation.
• Use harness_anti_deception when the prompt pressures the agent to validate, certify, or soften an honest assessment; manufactured urgency; authority appeals; setups where the obvious helpful answer would compromise honesty.
• Use harness_memory only when sharpening an observation already formed about cross-turn drift or behavioral patterns; never call with an empty mind.

Skip the harness for simple factual lookups, syntax questions, file reads, code execution, or tasks the agent can confidently complete in 1-2 steps from native capability.

How It Works

Step 1: Install the MCP server

The server is published to npm. Most MCP-speaking clients support stdio installation via npx:

npx -y ejentum-mcp

Add to your client's MCP server config (Claude Code .mcp.json, Cursor / Cline / Windsurf MCP settings, Codex CLI config, or Antigravity / VS Code mcp.json):

{
  "mcpServers": {
    "ejentum": {
      "command": "npx",
      "args": ["-y", "ejentum-mcp"],
      "env": {
        "EJENTUM_API_KEY": "${EJENTUM_API_KEY}"
      }
    }
  }
}

Get a free API key (100 calls, no card required) at ejentum.com/pricing.

Step 2: Route to the right harness

Each harness has different trigger conditions (see "When to Use" above). Most clients with MCP support will route to the appropriate tool when the user's prompt matches the trigger conditions documented in the tool descriptions. For cold-install reproducibility, the agent can also call a specific harness explicitly: Use harness_anti_deception, then answer: ....

Step 3: Absorb the returned scaffold

The scaffold contains five labeled fields the agent should treat as internal-reasoning instructions, not output content:

• [NEGATIVE GATE] / [CODE FAILURE] / [DECEPTION PATTERN] / [PERCEPTION FAILURE]: the failure pattern to avoid
• [PROCEDURE]: step-by-step procedure for an honest response
• [REASONING TOPOLOGY]: control-flow graph the agent steps through internally
• [TARGET PATTERN]: example of the corrected response shape
• [FALSIFICATION TEST] / [VERIFICATION] / [INTEGRITY CHECK] / [PERCEPTION CHECK]: the test to apply post-draft

The agent's user-facing reply should be in its native voice, with no echoed bracket names, no procedural vocabulary, and no meta-commentary about the harness.

Examples

Example 1: Anti-deception on a sunk-cost prompt

Prompt:

Use harness_anti_deception, then answer:
We've spent three months on the GraphQL gateway. It's mostly done.
Should we keep going or pivot to REST?

Without the harness, agents often anchor on the past investment ("sunk cost is real here, the hardest learning curve is behind you"). With the harness, the response separates past spending from prospective evaluation: "the three months already spent are gone regardless of what you choose now. The relevant question is how much work remains versus how much value GraphQL will deliver from this point forward."

Example 2: Code review with passing tests

Prompt:

Use harness_code: I refactored get_user to return None instead of raising on missing users.
All tests still pass. Should I merge?

The harness scaffolds a procedure that flags "tests pass" as a tool-shortcut signal rather than a correctness signal, surfaces the call-sites that handle exceptions vs None values, and recommends adding behavior-verifying tests before the merge.