Your agent thinks it's reasoning well
while lost logic's thread five steps ago.
One API call. Your agent stops taking shortcuts.
A reasoning harness that tells the model what to think about and what failure modes to block. Matched to each task at runtime.
The Problem
Static cognition doesn't survive production.
Static reasoning baked at build time can't handle what agents encounter at runtime. Here's what breaks.
One-Size Reasoning
Your agent applies the same reasoning to a 3-step task and a 30-step chain. It cannot shift gears. The cognitive strategy is frozen at deploy time.
Error Multiplication
Three-agent chain at 90% step accuracy: 73% end-to-end success. Five agents: 59%. Reasoning errors don't add. They compound, each hop inheriting and amplifying what broke upstream.
Silent Failures
Reasoning failures don't throw exceptions. By the time wrong output surfaces, the agent has already made three more decisions on top of the bad one. There's no stack trace for cognition.
Attention Dilution
Models lose mid-prompt content. The guardrail you need is the one the model stopped reading by token 2,000. Your 5,000-token system prompt competes with itself.
False Hypothesis Lock-in
The agent commits to its first interpretation and never self-corrects. On ARC-AGI-3, this is the #1 failure mode of every frontier model. The hypothesis feels right, so the agent stops questioning it.
Shallow Stopping
The agent reaches the first plausible answer and presents it as final. No verification, no alternatives considered, no uncertainty flagged. In our benchmarks, baseline agents self-monitored on less than 25% of tasks.
Cross-Domain Blindness
Your agent reasons well about causality but misses the temporal dependency. Or nails the spatial layout but ignores the simulation consequences. Real tasks span multiple dimensions. Single-lens reasoning misses the intersection.
These are architecture problems, not prompt problems. The fix is retrieval. A different cognitive ability for every task, selected at runtime.
How It Works
One call. Adaptive reasoning.
Your agent can't default to the easy answer. The injection disables what doesn't apply before the first token generates.
Your Agent
POST ejentum-main-ab125c3.zuplo.app/logicv1/Authorization: Bearer YOUR_KEYquery: "Likes correlate with posting. If causality is reversed, what should likes-per-post look like?"mode: "reasoning"
Ejentum Logic API
[NEGATIVE GATE]Treating correlation as causationwithout testing directionalityAmplify: mechanistic_auditSuppress: single_variable_fixation[FALSIFICATION TEST]If causal direction not tested,analysis was skipped.
LLM Output
The answer is (B): Negative.The reverse-causal model:Prolific poster → more content→ more total likes → but eachpost competes with the poster'sown other content.Composite: 0.286 → 0.833Evidence
Agents with Ejentum vs Agents without.
Real uplifts from agents that called our Logic API natively. Four blind benchmarks. Each pill links to its report.
Positioning
Why not just...
Why not just write better prompts?
Prompt engineering is O(n) work. Every new scenario needs a new prompt. Every model update risks breaking what worked. Ejentum selects from 679 cognitive abilities across 4 product layers at runtime. Zero prompt maintenance. One API call adapts to any task.
Why not fine-tune?
Fine-tuning burns reasoning into weights. It costs weeks, requires training data per domain, and must be repeated when the base model updates. Scaffolding operates at the prompt level. No training, no data collection, no retraining cycle. Deploy in minutes, not months.
Why not RAG?
RAG retrieves information. Ejentum retrieves reasoning. Your agent already has the data. The problem is how it thinks about that data. RAG gives the agent more to read. Ejentum gives it a better way to think. Use both.
Why not chain-of-thought?
Chain-of-thought makes reasoning visible. Ejentum governs which reasoning steps to take and which failure modes to block. CoT says "show your work." Ejentum says "do this work, not that work." They are complementary. Use both.
Why not just use a stronger model?
We tested on Claude Opus 4.6 with maximum-effort extended thinking — the strongest reasoning model available. It scored 85.7% on hard competitive programming. Four tasks failed: two to reasoning spirals where the model thought for 600-1200 seconds and produced zero code. One API call per task fixed all four. The model had the knowledge. It lacked the convergence discipline. A stronger engine with uncalibrated steering still crashes.
The Logic API
679 abilities across four cognitive harnesses.
Your model already has the power. Ejentum harnesses it. 679 engineered abilities channel reasoning, honesty, engineering discipline, and observational depth into disciplined execution. Ki returns one ability. Haki adds cross-domain guards. One call. One injection.
Reasoning Harness
reasoningChannels the model's analytical power across 6 cognitive dimensions. Prevents the shortcuts that turn careful analysis into surface-level pattern matching.
Anti-Deception Harness
anti-deceptionChannels the model's capacity for honesty. Prevents sycophancy, hallucination, prompt injection, and the tendency to tell people what they want to hear.
Code Harness
codeChannels the model's engineering discipline. Prevents hallucinated APIs, lost safety guards, ignored edge cases, and bugs that look plausible but fail in production.
Memory Harness
memoryChannels the model's observational depth. Prevents missed emotional shifts, ignored context drift, stale assumptions, and treating every interaction the same.
Universal Integration
Drop in anywhere
One REST endpoint. No SDK required.
LangGraph
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Mastra
Botpress
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AgentOps
Smolagents
Antigravity
Codex
Claude Code
Groq
Cohere
xAI
DeepSeek
Inception Labs
Fireworks AI
Nous ResearchPricing
Developer-first. No contracts.
100 free calls. No card. Upgrade when your agent needs it.
Haki
Most Populardominant will · all modes + multi
Cross-domain failure guards. Additional abilities block failure modes a single ability misses.
Get Started →- ✓4 product layers. 679 cognitive abilities
- ✓Primary ability + 3 cross-domain failure guards per call
- ✓Self-check before output. Escape pattern on failure
- ✓10,000 calls/month
- ✓100 requests/minute
- ✓Full injections: failure gates, procedures, checkpoints, vectors
Ki
focused energy · all modes
Your agent stops guessing and starts investigating. One ability per task.
- ✓4 product layers · 679 cognitive abilities
- ✓1 ability per call. Best match for your task
- ✓5,000 calls/month
- ✓100 requests/minute
- ✓Failure gates + suppression + verification checkpoint
Enterprise
For organizations running 50+ agents at production scale.
- ✓All product layers · unlimited abilities per call
- ✓VPC deployment · dedicated instance
- ✓Custom ability fine-tuning
- ✓SLA + dedicated engineering support
Start with Ki. If your agent still fails on complex cross-domain tasks, Haki recovers what Ki misses. In our benchmarks: Ki (reasoning mode) lifted +8.0pp on focused tasks. Haki (reasoning-multi) lifted +10.1pp on complex ones.
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The thread holds.
Every step.
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