Engineering at HubAI
Deep dives into the problems we found, the principles we follow, and the architecture decisions that make structured AI code generation work at enterprise scale.
How We Engineered Context Windows That Don't Degrade
LLM context windows are finite, expensive, and degrade under load. We discovered that chat-based tools waste 50K-200K tokens per scaffold — most of it noise. Here's how HubAI treats context like memory in a high-performance system.
Making AI Agents That Never Lose Their Place
Context resets are the silent killer of AI agent productivity. A long session fills the context window, the agent restarts, and all progress is lost. We built a two-agent pattern with structured progress tracking that makes every session resumable.
The Evaluation Loop: How We Measure and Improve AI Tools
You shipped tools. Agents use them. But are they working well? Without evaluation, you're guessing. Here's the four-step loop we use to measure tool effectiveness and systematically improve agent performance.
Designing Tools That AI Agents Actually Use Correctly
Poorly designed tools are the #1 source of agent failure. We discovered that if a human can't choose which tool to use from the description alone, an agent can't either. Here's how we engineer tools as contracts between deterministic systems and non-deterministic agents.
From Chaos to Pipeline: Choosing the Right Workflow for Every Task
Most AI tools default to maximum complexity. We learned that complexity must be earned, not assumed. Here's how HubAI uses three workflow patterns — prompt chaining, routing, and evaluator-optimizer — in a strict hierarchy.
Why We Built a Multi-Agent Brain Instead of a Single Chatbot
A single agent juggling requirements, database design, frontend architecture, and QA produces mediocre results at every stage. We built six specialized agents with isolated contexts and a MessageBus that prevents context pollution.
How Classical Design Patterns Tame Non-Deterministic AI
AI agents are inherently non-deterministic — every call can produce different results. We use classical GoF design patterns not as convention, but as structural guardrails that impose determinism on the system surrounding the AI.
Teaching AI Agents to Think Before They Act
We discovered that agents rush to action without reasoning over tool results. In long chains of tool calls, the model acts on stale or incomplete information. A zero-side-effect 'think' tool solved the problem.