2SOLID AI — Engineering
AI & Machine Learning
Beyond the chatbot. We architect agent systems with persistent memory, persona identity, and guardrails — and build the infrastructure that makes them trustworthy in production.
Talk to Engineering§01 — SCOPE
Optimization is a discipline, not a demo.
The hero above is a live training run: agents descend a computed loss surface under momentum SGD, settle into minima, and get re-scattered every epoch. This page documents the practice the same way — measured, evidenced, reproducible.
§02 — CAPABILITIES
What we build
Agent systems
Persona agents with roles, tools, memory, and boundaries — coordinated as swarms, not single prompts.
Memory architectures
4DAM-style persistent context — graphs, episodic stores, interrogable knowledge. Not retrieval bolt-ons.
Model integration
LLMs wired into real operations — calls, email, documents, approvals — with guardrails and audit trails.
Verification layers
Citation enforcement, unsupported-answer boundaries, review gates — the BLUE PHYR discipline.
MCP tooling
Model Context Protocol servers and connectors — real, scoped, least-privilege access to your systems.
Data products
Pipelines and interfaces that turn operational data into decisions.
4DAM — four-dimensional agentic memory
The architecture under Synapse Brain: context stays resident and persistent; the model interrogates memory rather than hoping retrieval surfaced the right fragment. Knowledge compounds across sessions, agents, and products.
See Synapse§03 — INSTRUMENT
The hero is a live optimizer. Here is its datasheet.
A time-varying loss surface, a finite-difference gradient, and a momentum update — nothing decorative. Every parameter below is read from the source.
| 01Loss surface | L = −Σᵢ dᵢ·e^(−‖x−mᵢ‖²/rᵢ²) + ripples | three Gaussian wells + two harmonic ripple terms |
|---|---|---|
| 02Gradient | ∇L ≈ (L(x+ε) − L(x−ε)) / 2ε | central differences, ε = 0.004 — evaluated live |
| 03Update rule | v ← 0.86·v − ∇L·Δt·η | momentum SGD plus tiny positional noise |
| 04Learning rate | η = 0.10 / 0.16 | 8% of agents run hot — the bright streaks |
| 05Population | 90–160agents | scales with viewport width |
| 06Epoch | 14s | full re-scatter — training replays forever |
| 07Contours | iso-lines every ΔL = 1/7 | marched on a 6 px lattice |
OBSERVED
0.86
the momentum coefficient in the hero's optimizer
energy.js · step() — v ← 0.86·v − ∇L·Δt·η
Momentum is what carries a descent through ripples that trap naive gradient followers. Production training works the same way — which is why the heroes on this site are instruments, not illustrations.
§04 — EVIDENCE
Show the work: the optimizer step, verbatim.
The update running above you, unsimplified — momentum, gradient, and noise exactly as written.
step(dt, t) — energy.js
site/src/scripts/energy.js
- GRADIENT
gx = (L(x + e, y, t) − L(x − e, y, t)) / (2·e) — e = 0.004
central differences on the live surface
- UPDATE
a.vx = a.vx * 0.86 - gx * dt * (a.hot ? 0.16 : 0.1); a.vy = a.vy * 0.86 - gy * dt * (a.hot ? 0.16 : 0.1); a.x += a.vx + Math.sin(t * 1.3 + a.ph) * 0.0004;
energy.js · step() — verbatim
- EPOCH
epochClock > 14 → seed() — every fourteen seconds the population re-scatters and the descent runs again.
non-convergence is part of the exhibit
§05 — DISCIPLINE · How models get trusted
Evals before ship
Behavior measured against task suites and refusal sets — not vibes in a demo.
Guardrails as architecture
Clearance levels, action limits, escalation gates — constraints the model cannot prompt its way around.
Citation enforcement
Unsupported answers get refused, not smoothed over. The BLUE PHYR discipline.
Audit trails
Every action attributable — what the agent did, what it was allowed to do, what it saw.
Scoped access
MCP connectors grant least-privilege reach into real systems. Nothing ambient, nothing implicit.
Have a model problem?
From architecture to integration — scope it with the people who ship the evidence.