Hands-on technical engineer writing about production AI systems: state, orchestration, retrieval, evals, observability, recovery, and the places AI products fail after the demo works.
Signal & State exists because most AI work does not fail at the moment the model produces text. It fails at the seams around the model: stale context, missing state, unsafe tool calls, unobservable runs, unclear approval paths, brittle retries, and recovery logic nobody designed.
Ferre writes from the engineering layer around those seams. The goal is not to review model releases or collect prompt tricks. It is to make production failure modes visible before teams turn a promising prototype into product infrastructure.
Every issue is meant to leave technical leaders and senior builders with a sharper architectural question, pattern, or failure model.
What the system persists, resumes, exposes, and uses to decide what happens next.
How model output becomes bounded action through tools, permissions, stop conditions, and approval gates.
How teams reconstruct runs, mine failures, detect regressions, and decide whether behavior is actually good enough.
Ferre has spent 10+ years building scalable Laravel applications, AI-enabled products, and practical internal systems, with public claims of 200+ deployments and 50+ projects on ferre.dev.
Past work includes research portals, enterprise quote-to-purchase workflows, performance-sensitive booking systems, and editorial dashboards.
The operating context is practical software delivery: pull requests, reviews, deployment pipelines, issue trackers, remote teams, and code that has to keep running after handoff.
His AI systems practice starts with one repeated workflow and designs the surrounding context, tools, review, logs, and fallback behavior before pretending the agent is autonomous.
Architecture breakdowns for CTOs, technical founders, and engineering leaders building AI systems past the demo. No hype, no model-release recaps, no “top 10 AI tools.” Unsubscribe in one click.