AI Agent Systems

Overview

I design and build multi-agent AI systems — architectures where specialized agents cooperate through shared memory and orchestration, instead of a single large prompt trying to do everything.

How I Build This

• Autonomous agents with clearly separated responsibilities (analysis, decision, execution) rather than one do-everything agent.
• Memory systems — combining graph-based structural memory with vector-based similarity memory, so agents can recall both relationships and patterns.
• Orchestration layers that coordinate agent hand-offs and shared state, instead of letting agents silently overwrite each other's context.
• Tool calling wired to real external systems (APIs, databases, execution venues), not mocked demos.
• Evaluation pipelines to test agent behavior against known scenarios before it runs unsupervised.

This is the same architectural approach behind S.A.M.S., a multi-agent trading system combining a knowledge graph, vector memory and local LLM inference.

Who This Is For

Teams that have already tried a single-prompt "AI feature" and hit its ceiling — and need an actual system architecture underneath, not a bigger prompt.

FAQ