By Sebastian Chedal
The first time a conversational agent platform does something genuinely useful, it’s hard not to be impressed. You describe a workflow in plain language and watch it turn into a running system. There’s no GUI to configure, no YAML to write, no deploy cycle. You just say what you want and it shows up. If…
By Sebastian Chedal
The customer’s business ran across five systems that were until now entirely independent. QuickBooks held the financial picture. HubSpot tracked contacts and deals. WooCommerce captured every order. Mailchimp, social media platforms, and standalone landing pages generated the leads and tracked where they came from. A hand-built Google Sheets spreadsheet held the custom business logic that…
By Sebastian Chedal
Memory and context management is, in 2026, still largely something model providers have left builders to work out on their own. Claude Code ships a markdown file and a loose convention for organizing it. LangChain gives you a ConversationBufferMemory you can drop in without much ceremony. Both are honest starting points, and neither gets you…
By Sebastian Chedal
A lot of the AI-environment writing on LinkedIn and in mainstream press, while correct when it was written, has been overtaken by new data. The per-query energy and water numbers that anchored the 2024 panic narrative have come down by an order of magnitude as first-party disclosures from Google, OpenAI, and Mistral replaced 2023 best-guesses….
By Sebastian Chedal
The gap between an agent that runs in a demo and an agent that runs in production isn’t a tooling gap or a model-capability gap. It’s a discipline gap in discipline. The discipline that closes that gap is evaluation, not as a QA afterthought, but as the operating practice that determines whether the rest of…
By Sebastian Chedal
Unleashed Consulting runs paid advertising for roughly 80 local pet-services businesses across Google Ads, Meta, and Local Services Ads. Their media buyers are good at what they do. The challenge isn’t skill: it’s math. Each client runs on multiple platforms. Each platform has its own dashboard. A buyer doing deep optimization on one account is,…
By Sebastian Chedal
Ask a content professional what worries them about AI and the answer is rarely about the technology. It’s about whether the craft itself still has a place. The craft is splitting in two, and the split has very little to do with which model you use. It has to do with why you were writing…
By Sebastian Chedal
You’re past the pilot. The agent works in demos and probably in staging, and now somebody is asking the real buying question: will it hold up when nobody is watching? That question doesn’t resolve at the model layer. It resolves in the layer of code, configuration, and execution logic that sits around the model, what…
By Sebastian Chedal
The dominant question in generative engine optimization right now is whether your brand shows up in AI answers. The harder, more useful question is whether the AI recommends you when a buyer asks the comparison prompt that ends the decision. Those two outcomes are decoupled. The same AI conversation can pull a quote from your…
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
Businesses comparing Hermes Agent and OpenClaw treat it as a winner-loser question. They are not competing for the same job. They are different layers of the same stack, and in our experience the right architecture for most agentic systems runs both, nested together, with Hermes driving and OpenClaw containing. Architectural disagreement Hermes Agent and OpenClaw…
By Sebastian Chedal
When Anthropic released their multi-agent blueprint, we didn’t just read it—we deployed it. Over the last few months, we’ve validated their architectural patterns in production. Here is a look at what actually worked when rubber met the road, and where we had to adapt their reference designs for real-world reliability. Anthropic’s engineering team published one…
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
Most companies deploying AI agents hit the same wall about two months in: the agent forgets everything between sessions, can’t read the company’s actual knowledge (strategy docs, pricing logic, customer notes), and has no clean way to write what it learns back to the team’s knowledge base for human review. The toolkit for solving this…
By Sebastian Chedal
If you run a website in 2026, you have probably watched three different articles about MCP, A2A, and UCP scroll past in the last two weeks and wondered whether any of it changes what you should be doing this quarter. The short answer is yes, but probably less than the headlines suggest, and not in…
By Sebastian Chedal
(June update: Since our initial test, we’ve spent another three weeks running Codex 5.5 and Claude Code 4.7 head-to-head in our production environment. We’ve gathered hard data on where each agent excels when solving real tickets. Here is our breakdown of how these two coding agents actually perform when harnessed together.) How We Run Claude…
By Sebastian Chedal
The skeptics are mostly right. Most of what’s being marketed as “AI agents” in 2026 is a workflow with a chat interface bolted on top. A Zapier flow with an LLM step inside it. A Make automation that asks a model to write a subject line before sending the email. These are useful tools. They…
By Sebastian Chedal
In February 2026, Andrej Karpathy proposed retiring the term “vibe coding” and replacing it with something more precise. The replacement he suggested: agentic engineering. Within weeks, monthly searches for the term grew from a few hundred to nearly 3,000. The naming stuck because it named something real. This covers what the naming shift signals for…
By Sebastian Chedal
Between December 2025 and February 2026, one person used two consumer AI subscriptions to breach nine Mexican government agencies, steal about 150GB of sensitive data, and expose roughly 195 million taxpayer records. No malware team. No nation-state. No custom infrastructure. A single operator, a Claude account, a ChatGPT account, and about six weeks. The forensic…
By Sebastian Chedal
The Build vs. Buy Question Has Changed Two signals landed in the same week. A CIO.com report showed enterprises spending $280 million annually on 600+ SaaS applications. And a solopreneur documented 33 custom AI agents running her entire business for $10-20 a month. Enterprise and solo operators arrived at the same question independently: why am…
By Sebastian Chedal
Every AI agent starts each session from zero. No memory of yesterday’s decisions, no record of what worked, no access to what the agent next to it learned last week. For a one-off chatbot conversation, this is fine. For agents running 10 to 20 sessions per day across months of production work, it’s the difference…
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
What GEO Actually Is (And What Most Guides Get Wrong) Generative Engine Optimization is the practice of structuring your content so AI search engines cite it when answering user queries. Where SEO optimizes for ranking positions, GEO optimizes for citations: getting ChatGPT, Perplexity, Google AI Overviews, and other AI platforms to reference your content in…
By Sebastian Chedal
The Numbers Are In Five independent research efforts published in the first quarter of 2026 arrived at the same conclusion: most organizations deploying AI agents have no idea how exposed they are. Gravitee surveyed over 900 executives and technical practitioners and found that 88% of organizations reported confirmed or suspected AI agent security incidents in…
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
What You Ask the Model to Do Matters More Than Which Model You Use Most advice about AI agent costs starts and ends with tokens. Cache your prompts. Batch your requests. Use a cheaper model. And those tactics help, the same way compressing images helps a slow website. They’re optimizations at the wrong layer. The…
By Sebastian Chedal
Fountain City built a hydraulic 3D simulation system with zero human-written code. Here’s what actually happened. Earlier this year we built a hydraulic simulation system for a gaming client. The software generates physically realistic terrain with lakes, rivers, erosion channels, watershed detection, seasonal water cycles, and topographic mapping. It runs inside Unity 6.2 and produces…
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…
End of content
End of content
Like what you are reading? Join us for insights and into the intersection of AI, technology, and transformation to supercharge your business.
We promise not to spam you, and we never share our contacts.
"*" indicates required fields
Like what you are reading? Join us for insights and into the intersection of AI, technology, and transformation to supercharge your business.
We promise not to spam you, and we never share our contacts.