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Agent kernel for self-improving systems

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Reliability

Browse articles in Reliability

Adaptation
Why AI Agents Loop Forever
The Cost of Always-On Agents
Automatic repair
Behavior Change Without New Code
Continuous operation
Efficiency
Why Prompt-Only Agents Break at Scale

ByteNex

BX-System : an agent kernel that generates its own tools, verifies them against expected outcomes, and learns from the results.

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Glossary

Principles

Agent Kernel Architecture: The Complete Guide
Agent Kernel vs Agent Framework
BX-System vs. LangGraph: Kernel vs. Framework
How to Evaluate an Agent Kernel: The 6-Point Checklist
What Is an Agent Kernel?
Declarative Flows vs Imperative Scripts
Implementation Guide: Signing Tools with SHA-256
How to Build a Declarative Flow
One Cycle: Generate, Verify, Record, Update
How an Agent Generates Its Own Tools
Outcome Verification: Expected vs Actual
How to Verify Agent Output
Strategy Weights: How Outcomes Change Behavior
Reward Between 0 and 1: Designing the Verdict Scale
Undeclared Tool Calls: When a Tool Is Invented
The AST Engine: Rule-Based Code Repair
Why LLM-Generated Code Fails in Production
Defining Repair Rules for Generated Code
Kill Switch and Isolated Context
RAM-Only Execution for Core Modules
Encrypting Tool Packages with AES-GCM
Stable State: What the Kernel Returns To
Pausing and Resuming an Autonomous Kernel
Workflow Context and Memory State
Context Loss Between Runs
Measuring Efficiency Without Guessing
The Cost of Always-On Agents
Behavior Change Without New Code
Why Prompt-Only Agents Break at Scale
Unverified Automation: The Silent Failure
Why AI Agents Loop Forever
Continuous operation
Self-protection
Adaptation
Automatic repair
Efficiency
The Five Operating Principles in One Page
Signing Every Tool with SHA-256
Role Profiles: Prompt Prefix, Bias, Routing

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