System design, AI engineering, databases, networking, concurrency and more, written for engineers building real trading and fintech systems — part of the Algomi Suite.
The design patterns behind reliable LLM agents: ReAct and plan-and-execute loops, agents as explicit state machines, the memory taxonomy, multi-agent trade-offs, and the safety rules for tools that change the world.
Created 2026-09-29 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering
Prompts tell an agent what to do; a harness decides what it knows, what it may touch, and what counts as done. Five subsystems, the session lifecycle, and a working Java shift-handover model, told as a hospital night ward.
Created 2026-10-02 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering
How to turn repeated prompting into a controlled loop: goal loops, a separate checker, retry budgets, state kept outside the loop, reusable skills, and a tool gateway with permissions, timeouts and idempotency. Told as a child doing homework.
Created 2026-10-02 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering
When one loop is not enough: model the workflow as a graph, fan work out and back in, isolate parallel agents, roll back on failure, gate risky steps on a human, and measure the harness with ablation tests. Told as building a house.
Created 2026-10-02 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering
Why a company with hundreds of MCP servers needs a gateway, and how a control plane, a data plane, tool-level authorization, progressive discovery and response projection work, using Uber's published MCP Gateway as a case study.
Created 2026-10-04 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering
