Myelix Agents Blog

Architecting Myelix Agents: Feynman Would Be Proud — Back to First Principles

2026-09-28T13:00:00Z

In 1985, Richard Feynman spent a summer at Thinking Machines Corporation and handed the field of computing a timeless warning: do not build machines that simply mimic humans. Build machines that do what machines actually do best. Forty years later, the AI coding industry has ignored him almost completely.

This post traces the direct line from Feynman's "automatic filing system" framing and the Connection Machine router problem through to the Synapse AST knowledge graph (12 ETS tables, microsecond graph traversal), the BEAM's preemptive actor-model concurrency, and Cortex's eight-gate Satisfaction Evaluator — including the gate_5_learned_prevention gate that cross-checks every completion against persisted MistakeSignatures.

Read the full article →

Why the BEAM Beats Containers for Agent Concurrency

2026-09-25T09:00:00Z

Spawning 50 concurrent agents on Myelix Agents costs the RAM of a single Docker container. Every competing multi-agent platform provisions a new container per agent — 30-second cold boot, gigabytes of overhead, per task. Here is the engineering proof behind that claim and why the BEAM VM was the only correct choice.

A BEAM process starts in ~3 microseconds with ~2 KB initial heap. Running 50 simultaneous container-based agents requires 10–30 GB RAM and 750–2,500 seconds of aggregate boot time — before a single line of code is produced.

Read the full article →

Why Agents Need Five Gates, Not One: The Satisfaction Evaluator

2026-09-18T09:00:00Z

The most common failure mode in autonomous coding agents is not hallucination — it is premature conclusion. The agent writes code, the compiler does not error, and the agent declares success. A minute later the developer opens the file and finds something that does not remotely match the intent.

The fix is structural: make the agent prove correctness across five independent dimensions before it is allowed to finish. Rule Adherence, Plan Adherence, Test Coverage, Diff Safety, and Final Satisfaction — all five must pass.

Read the full article →

Cutting LLM Costs by 90%: The 3-Tier Prompt Cache Architecture

2026-09-10T09:00:00Z

Multi-round agent sessions are expensive. A 10-round session with a 50k token context can cost $2–$5 per task at frontier model rates. The Myelix Agents 3-tier prompt architecture changes this equation structurally, not by trimming tokens, but by making provider-side prefix caching work as hard as possible.

By sorting prompt content by mutation frequency — Stable (role, protocol, schema) → Context (intent, evidence, memory) → Volatile (current task, tool results) — the system achieves >99% cache hit rates on the Stable tier and 85–95% on the Context tier. Real-world cost reduction: 85–91%.

Read the full article →