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Building AI Systems That Remember: What 660 Components Taught Me About Scale
AI Architecture Systems Design

Building AI Systems That Remember: What 660 Components Taught Me About Scale

Most AI tools forget everything between sessions. I built a 3-part system to fix that. Here's what actually works for production AI at scale.

Here’s a problem nobody talks about in AI adoption.

Your AI assistant is brilliant for 30 minutes. Then the context window fills up. It forgets what you discussed. Forgets the codebase structure. Forgets the decisions you made together. You start over.

Every. Single. Session.

I’ve watched enterprise teams waste thousands of hours re-explaining their systems to AI tools that can’t remember yesterday. The productivity gains evaporate into repetitive prompting.

So I built something to fix it.

The Three-Part System

Over the past year, I’ve developed an integrated AI development system with three components that work together:

  1. Context Cascade - 660 components (skills, agents, commands) that load on-demand
  2. Memory MCP - Triple-layer memory that persists across sessions
  3. Connascence Analyzer - 7 code quality analyzers that catch problems before they ship

They’re all open source. Here’s why they exist and what they demonstrate about building AI systems that actually work at scale.

Problem #1: Context Is Expensive

Large language models have context windows. GPT-4 gives you ~128k tokens. Claude gives you 200k. Sounds like a lot until you load a codebase.

The naive approach: dump everything into context upfront. Load all your documentation, all your code, all your rules. Watch your context fill up before you’ve asked a single question.

I tried this. It doesn’t scale.

The fix: lazy loading.

Context Cascade uses a nested hierarchy:

Playbooks (30) -> loaded first, ~2k tokens
    |
    v
Skills (196) -> loaded on demand
    |
    v
Agents (211) -> loaded by skills
    |
    v
Commands (223) -> embedded in agents

Only playbooks load initially. Everything else loads when needed. Result: 90%+ context savings compared to loading everything upfront.

This is the same pattern React uses for code splitting. The same pattern CDNs use for edge caching. Load what you need, when you need it, as close to the point of use as possible.

It works because most tasks don’t need most capabilities. A code review doesn’t need the deployment playbook. A research task doesn’t need the debugging agents. Load selectively.

Problem #2: AI Has Amnesia

This is the one that kills productivity.

Every AI session starts from zero. Your agent doesn’t remember what it analyzed yesterday. Doesn’t remember the bugs it found. Doesn’t remember the architectural decisions you made together.

Human teams have institutional memory. AI teams have collective amnesia.

The fix: structured persistence.

Memory MCP implements a triple-layer architecture:

  • Short-term (24h): Current conversation, recent context
  • Mid-term (7d): Project state, recent decisions, work in progress
  • Long-term (30d+): Documentation, patterns, historical decisions

But storage alone isn’t enough. You need structure. Every memory write uses the WHO/WHEN/PROJECT/WHY protocol:

{
  "WHO": "code-analyzer:abc123",
  "WHEN": "2025-12-28T15:00:00Z",
  "PROJECT": "auth-service",
  "WHY": "security-audit"
}

Why does this matter? Because agents coordinate through shared memory.

Code analyzer finds a vulnerability. Stores it with tags. Coder agent queries memory, finds the vulnerability, applies a fix. Stores the fix with a reference to the finding. Knowledge graph tracks the relationship.

No human had to copy-paste the finding. No one had to re-explain the context. The agents coordinated through structured memory.

This is how human teams work. Someone documents a problem. Someone else reads the documentation, fixes it, updates the ticket. We’ve just been making AI teams work without the ticketing system.

Problem #3: Quality Control at AI Speed

Here’s what scares me about AI-generated code.

It’s fast. Really fast. An AI can generate thousands of lines in minutes. But quantity isn’t quality. And at that speed, manual code review can’t keep up.

I’ve seen teams accept AI output without review because reviewing felt slower than regenerating. That’s a recipe for shipping bugs.

The fix: automated quality gates.

Connascence Analyzer runs 7 different analysis passes:

  1. Connascence Detection - 9 types of coupling (name, type, meaning, position, algorithm, execution, timing, value, identity)
  2. NASA Power of Ten - Safety rules from aerospace (max 60 lines per function, max 4 nesting levels)
  3. MECE Analysis - Logical organization and completeness
  4. Clarity Linter - Cognitive load and readability
  5. Duplication Detector - Code clones and copy-paste issues
  6. Safety Violations - Security and reliability patterns
  7. Six Sigma Metrics - Statistical quality measurement

The analyzer integrates as an MCP server. Every file change triggers analysis. Quality gates block completion until code passes.

Think of it like CI/CD, but for AI-assisted development. You wouldn’t ship code without running tests. You shouldn’t accept AI output without running analysis.

Why This Architecture Matters

These three components demonstrate principles that apply beyond this specific system:

Principle 1: Load What You Need

Context is a finite resource. Treat it like memory in a constrained system. Lazy load. Cache strategically. Evict when appropriate.

This applies to any AI system. Don’t dump everything into the prompt. Build infrastructure that loads context dynamically based on the task.

Principle 2: Memory Is Infrastructure

AI without memory is a parlor trick. Useful for one-off questions. Useless for sustained work.

Production AI needs memory infrastructure the same way production applications need databases. Not optional. Foundational.

Principle 3: Quality Gates Scale Better Than Review

Human review doesn’t scale to AI output speeds. Automated analysis does.

Build quality gates into your AI workflows. Run them automatically. Make them blocking. Trust the process, not individual review capacity.

Principle 4: Agents Coordinate Through Artifacts

The fastest team coordination happens through shared artifacts. Documentation. Tickets. Code comments. Design docs.

AI agents should coordinate the same way. Shared memory with structured tags. Knowledge graphs with typed relationships. Let agents discover what other agents have done.

What This Looks Like in Practice

Here’s a real workflow from last week.

Task: Add authentication to a microservice.

Without the system:

  1. Explain codebase structure to AI
  2. AI generates auth code
  3. Review manually (or don’t)
  4. Find bugs in production
  5. Repeat tomorrow, re-explaining everything

With the system:

  1. Memory already contains codebase patterns and past decisions
  2. Context Cascade loads auth-relevant skills and agents
  3. AI generates code using known patterns
  4. Connascence Analyzer flags coupling issues automatically
  5. Memory stores the implementation decision for future reference
  6. Tomorrow, the system remembers what was built

Time saved: hours per task. Bugs caught: dozens per week. Repetitive prompting: eliminated.

The Deeper Point

Building these systems taught me something about AI adoption.

The bottleneck isn’t model capability. GPT-4 and Claude are plenty capable. The bottleneck is infrastructure.

Most organizations treat AI like a magic black box. Prompt goes in, answer comes out. That’s fine for ChatGPT questions. It fails for production work.

Production AI needs:

  • Context management - loading the right information at the right time
  • Memory persistence - maintaining state across sessions
  • Quality control - catching problems before they ship
  • Agent coordination - letting specialized tools work together

These are infrastructure problems. They need infrastructure solutions.

The organizations that figure this out first will have a significant advantage. Not because their models are better—everyone has access to the same models. Because their infrastructure makes those models useful for sustained, complex work.

Try It Yourself

All three systems are open source:

The README includes installation guides. Clone them. Break them. Build something better.

That’s how this stuff improves.

I help biotech, healthcare, and professional services teams build AI infrastructure that works at scale. If your organization is hitting the limits of “just prompt better” and needs real AI architecture, let’s talk.