Architecture

How the Logic API works, what it returns, and how your agent uses it. For the theoretical foundations, see The Method. For the endpoint spec, see API Reference.

Request Lifecycle

Every call to /logicv1/ passes through four deterministic stages. Same input always produces the same output. Zero LLM inference cost.

POST /logicv1/
  |
  1. Authentication     Verify API key, resolve tier, enforce rate limits
  2. Retrieval          Match query against 679 abilities across 4 product layers
  3. Composition        Assemble the cognitive injection from matched ability
  4. Response           Return pre-rendered injection string as JSON

The entire pipeline executes in under one second. No LLM is called. The response is a structured injection that channels the model's existing power into disciplined execution.

What the API Returns

Ki Modes (reasoning, code, anti-deception, memory)

One cognitive ability. The highest-scoring match for your query from the selected product layer.

[
  {
    "reasoning": "[NEGATIVE GATE]\nThe checkout service crashed and we documented the incident...\n\n[PROCEDURE]\nStep 1: Trace backward from failure...\n\n[REASONING TOPOLOGY]\nS1:identify_failure -> S2:trace_backward...\n\n[TARGET PATTERN]\nReverse replay from the crash point...\n\n[FALSIFICATION TEST]\nIf an error's origin is not traced by replaying...\n\nAmplify: reverse replay; counterfactual node test\nSuppress: writing a vague post mortem summary..."
  }
]

Haki Modes (reasoning-multi, code-multi, memory-multi)

Primary ability plus cross-domain suppression graph with dynamic meta-checkpoint.

[
  {
    "reasoning-multi": "[PRIMARY]\n[PROCEDURE]\n...\n\n[REASONING TOPOLOGY]\n...\n\n[FALSIFICATION TEST]\n...\n\n[SUPPRESSION GRAPH]\nN{cross_domain_guard_1}\nN{cross_domain_guard_2}\n\n[META-CHECKPOINT]\nM{PAUSE -- Before output, verify you did NOT:...}\n\n[ON_FAILURE] ABANDON_GRAPH -> FREEFORM{...} -> RE-ENTER\n\nAmplify: ...\nSuppress: ..."
  }
]

Response Components

Section	What It Does
[NEGATIVE GATE]	Names the failure pattern the agent must avoid
[PROCEDURE]	Step-by-step reasoning instructions the agent follows
[REASONING TOPOLOGY]	Execution structure: steps, decision gates, loops
[TARGET PATTERN]	What correct reasoning looks like
[FALSIFICATION TEST]	Verification criterion to check the output against

Labels differ per product: reasoning uses [NEGATIVE GATE], code uses [CODE FAILURE], anti-deception uses [DECEPTION PATTERN], memory uses [PERCEPTION FAILURE]. Multi modes add [SUPPRESSION GRAPH], [META-CHECKPOINT], and [ON_FAILURE].

Product Layers

Each mode routes to a specialized ability collection. The query is matched against the abilities within that layer.

Dimension	What It Addresses
Causal	Direction-of-causation errors, correlation treated as cause
Temporal	Sequence errors, confabulated timelines, duration bias
Spatial	Topology failures, boundary violations, structural constraints
Simulation	Counterfactual collapse, failure to model consequences
Abstraction	Category errors, over-generalization, metaphor/mechanism confusion
Metacognition	Hallucination spirals, reasoning drift, bias blindness

A supply chain risk query scores high on Causal and Temporal. A market expansion question scores on Spatial, Simulation, and Abstraction. The routing is probabilistic, not categorical.

How to Inject

Wrap the API response in delimiters and prepend to your agent's system message, BEFORE the task prompt:

[REASONING CONTEXT]
{paste the response value here — key matches mode name}
[END REASONING CONTEXT]

Now complete the following task:
{your agent's actual task}

The injection must come BEFORE the task. The suppression vectors need to be in context when the agent begins reasoning, not after.

For multi-turn agents: re-inject per turn. Each turn may activate a different reasoning dimension. Stale injections from previous turns degrade as context fills with task-specific tokens.

Token Overhead

Mode	Response size	Approximate tokens
single modes (reasoning, code, anti-deception, memory)	~2,000-3,500 chars	~400-600 tokens
multi modes (reasoning-multi, code-multi, memory-multi)	~3,000-5,000 chars	~700-900 tokens

Compare to a typical system prompt: 5,000 to 15,000 tokens. The injection is compact by design. It replaces prose with structured constraints.

Integration

Your agent calls Ejentum like any other tool. One POST request, one JSON response, one injection.

Request

POST https://ejentum-main-ab125c3.zuplo.app/logicv1/
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

{
  "query": "Why did our deployment fail after the config change?",
  "mode": "reasoning"
}

Compatible Frameworks

Ejentum works with any system that can make an HTTP POST and inject text into a prompt:

n8n. AI Agent node with HTTP Request Tool
LangChain / LangGraph. Custom tool or LCEL chain
CrewAI. Tool injection into agent backstory
Claude Code / Agent SDK. tool_use definition
Cursor, Windsurf, Antigravity, Codex. custom HTTP tool
Any HTTP client. Direct POST, parse JSON, inject

See the Integrations guide for framework-specific code examples.

Rate Limits and Errors

Limit	Value
Requests per minute	100 per API key
Monthly calls (Free)	100 total
Monthly calls (Ki)	5,000
Monthly calls (Haki)	10,000

Error	Meaning
`401`	Invalid or missing API key
`403`	Multi mode requires higher-tier plan
`429`	Rate limit or monthly quota exceeded
`500`	Server error (retry with backoff)

For the full API specification, see the API Reference.

Documentation