AI Agent Systems

Overview

I design and build multi-agent AI systems: architectures where specialized agents cooperate through shared memory and orchestration instead of one large prompt trying to do everything itself. Most "AI features" hit a wall the moment a task needs more than one step of judgment. That's usually where a real agent architecture starts making sense.

How I Build This

Autonomous agents get clearly separated responsibilities. One handles analysis, another makes the decision, a third executes it. That split matters more than it sounds like on paper, because a single do-everything agent tends to lose track of what it already decided a few steps back.

For memory, I combine graph-based structural memory with vector-based similarity memory. Agents end up able to recall both relationships between things and patterns across past situations, which a rolling context window alone can't give you.

Orchestration layers coordinate agent hand-offs and shared state so agents aren't silently overwriting each other's context, a failure mode that shows up constantly in naive multi-agent setups. Tool calling gets wired to real external systems: APIs, databases, execution venues, not mocked demos that fall apart the first time they touch production data. And before anything runs unsupervised, it goes through evaluation pipelines that test agent behavior against known scenarios.

This is the same architectural approach behind SharapovLab Trading Lab (formerly S.A.M.S.), a multi-agent trading system that combines a knowledge graph, vector memory and local LLM inference.

Who This Is For

Teams that already tried a single-prompt "AI feature," hit its ceiling, and now need an actual system architecture underneath rather than a longer prompt.

FAQ