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Building Ansina: A Blueprint for a Deterministic, Dual-Model AI Runtime

· 2 min read
Luciano Giacchetta
Luciano Giacchetta

Designing a lean, provable AI agent runtime built from the ground up on Python ≥ 3.14: introducing the architecture blueprint for Ansina.

Currently in its blueprint phase and moving rapidly toward initial implementation, Ansina focuses on a tight, deterministic core where every architectural choice serves operational control, high signal-to-noise ratio, and zero unnecessary bloat.

Here is an inside look at what Ansina brings to the table as it prepares to launch:

📐 Pure Hexagonal Architecture​

  • Streamlined REST Surface: Exposes a clean, single internal FastAPI REST API with zero channel bloat or unnecessary gateway protocol overhead.
  • Deliberate State Management: Starts with a minimal SQLite schema, growing persistence intentionally rather than accumulating hundreds of unmanaged state tables.
  • Never-Throw Streaming Contracts: Features an ApiProvider port where streaming errors are returned synchronously as terminal stream events, guaranteeing predictable error paths.
  • First-Class Redaction: Implements structured logging with redaction hardcoded into the formatting pipeline to ensure sensitive data is filtered before it hits disk.

🫀 The Dual-Model Core: Heart & Brain​

Ansina separates autonomic local liveness from remote cognitive reasoning to eliminate round-trip network hops for basic decisions:

  • The Heart (In-Process Autonomic Loop):

  • Uses an embedded ≤4B parameter model running natively in-process via MLX on Apple Silicon (with a llama-cpp-python fallback).

  • Bounded strictly by an 8k context window to ensure optimal prompt performance.

  • Drives an always-on tick loop tasked exclusively with deciding idle vs. act vs. escalate without network overhead.

  • The Brain (Remote High-Reasoning):

  • Connects to 35B+ parameter models via an OpenAI-compatible BrainProvider port for complex reasoning tasks.

🎯 Hardware Target & Testing Strategy​

  • Hardware First: Optimized explicitly to run as an always-on engine on local Apple Silicon hardware (M4 Mac Mini, 16GB unified memory).
  • Black-Box Verification: Uses an E2E test harness that spins up python -m ansina as an isolated subprocess, testing readiness, migrations, and auth strictly over HTTP.

The design is locked, the constraints are set, and implementation of M0 — Skeleton is about to begin.