Deployment, operational decisions, reliability
By Sebastian Chedal
An AI system that’s starting to cost real money is a different problem from an AI prototype, whose job was to prove a model could do the thing. The production system’s job is to do the thing at a margin that justifies its existence. Teams usually cross that line without noticing. The bill climbs steadily,…
By Sebastian Chedal
Anthropic’s engineering team published one of the cleanest write-ups available on how a multi-agent system actually works in practice. The post is about Claude Research, an orchestrator-subagent pattern built for breadth-first research. The architecture is optimized for a particular task class, and the price of admission is a roughly fifteenfold token cost compared to a…
By Sebastian Chedal
Most teams running an AI agent pilot are being asked the same question right now: what do we build next? The published guidance is a stack of vendor maturity models that name the stages without naming the decisions inside them, and the team ends up debating models, prompts, and platforms while the pilot stalls. A…
By Sebastian Chedal
How We Run Claude Code and Codex Together in Production: Claude Code Drives, Codex Executes Most teams treat Codex and Claude Code as a choice to make. The pattern that compounds, as of April 2026, is to run them together: not in parallel, but hierarchically. Claude Code (Opus 4.7) is the driver. It plans, holds…
By Sebastian Chedal
Agent Governance in Practice: Why April 2026 Changed the Conversation If you’re running autonomous AI agents in production, governance just went from “we should probably think about that” to “we need this implemented before August.” Three things converged in the span of a single week that made the shift unavoidable. In this article: What the…
By Sebastian Chedal
The “Agentic SEO” Category Just Formalized. Most of It Is Mislabeled. Agentic SEO became an official category in early 2026. Frase rebranded around it. Siteimprove published a definitional guide. Search Engine Land ran a practitioner walkthrough. The term now has its own SERP, its own vendor ecosystem, and its own set of inflated claims. The…
By Sebastian Chedal
The Real Thesis: Quality, Not Cost Building an autonomous content pipeline is not hard. Getting five AI agents to produce something that looks like an article takes a weekend. Getting five AI agents to produce something you would actually publish under your own name, consistently, with minimal human intervention? That took months of iteration and…
By Sebastian Chedal
The Scheduling Problem Completion-triggered orchestration is an architectural pattern where only the pipeline’s entry point runs on a schedule. Every downstream stage fires automatically when its predecessor completes. We run a multi-stage autonomous content pipeline on fixed schedules — or we did, until the scheduling layer became the bottleneck. This article is about the scheduling…
By Sebastian Chedal
The $47,000 Problem (And Why Rate Limits Won’t Save You) A LangChain agent running in a retry loop accumulated $47,000 in API charges over 11 days. A developer on Reddit’s r/AI_Agents shared their $30,000 agent loop. A smaller but telling example: the team behind Askew’s circuit breaker post burned $87 on failed requests before they…
By Sebastian Chedal
The Model Obsession Is Costing You Money Somewhere right now, a leadership team is three months into comparing GPT-5 against Claude 4 against Gemini for their next AI initiative. They have spreadsheets. They have benchmark scores. They have opinions from every vendor in the space. They have not discussed how data flows into the system,…
By Sebastian Chedal
Why We Built an Autonomous Content Pipeline Fountain City is a 27-year-old technology studio. We build autonomous AI systems for clients, and we need a steady stream of research-backed content to support that work. Blog posts, service pages, landing pages, SEO optimization, social distribution. The kind of output that would normally require a content strategist,…
By Sebastian Chedal
If your AI pilot stalled, you’re in the majority. Not a slim majority. An overwhelming one. The numbers across multiple independent studies all point the same direction: most AI pilots never reach production. The problem usually isn’t the technology. It’s five predictable patterns in how organizations plan, resource, and execute these projects. All five are…
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