AES Research

AI R&D for long-horizon agentic systems.

I build the architectural patterns that let multi-agent systems survive real production work — not demo runs, not controlled benchmarks, but the kind of long-horizon autonomous operation where context compounds, memory matters, and every autonomous output has to be verifiable before it goes anywhere.

What I work on

• Two-tier memory architectures that persist across 40+ agent sessions without context bloat
• Cross-project director agents coordinating sub-agent swarms via role-tagged addressing and role-boundary self-monitoring
• UAT harnesses that surface the residual context gap — the failure modes synthetic benchmarks miss
• Skeptic membranes that enforce verifiable outputs and retained human authority on autonomous agent work
• Production ML pipelines with factor capture, ensemble clustering, and multi-year out-of-sample validation

Writing

• Two-tier memory for production agents: what chat systems don’t tell you — a working architecture for agents that need to remember across sessions without choking on their own context
• The context window is a battery: surviving compaction on long-running agents — what happens to your calibration when the context fills up, why compaction drops the work you need most, and the discipline that protects you from it
• Attention management: routing between native LLM capability and augmented skills and tools — the control plane that decides, at every step, where the work should happen

More deep-dives in progress.

Live demo

Try the live architecture demo → Type a question and watch the skeptic membrane, two-tier memory lookup, and attention-routing decisions fire in real time alongside the response. Backed by a Cloudflare Worker that keeps the API keys server-side and enforces the demo prompt; the worker source is open at /worker/.

About

AES Research is the independent R&D program of Daniel Higuera, run since 2017 in parallel with industrial R&D leadership at Hitachi Energy (grid-planning and wholesale-market software for North American ISOs and utilities). The work spans 12+ AI-native projects under a unified agentic architecture.

Twenty years of professional experience across energy markets, production ML, and industrial software. AI and ML expertise developed independently over 15+ years, predating formal academic curricula for most modern sub-disciplines.

LinkedIn · Résumé available on request · Contact via LinkedIn or email