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How we avoid vendor lock-in in AI agents: an employent stack no provider owns
Picking a framework for your agents, a model provider, or a deployment host looks like just another technical decision — until that provider changes the rules and your whole architecture turns out to depend on them not doing it. When we built Mercuria, our first employent, the AI agent with an employee’s role, we decided […]
What is an employent? (and why we started with marketing)
Telling people you build your own agents isn’t much of a headline anymore. They respond inside a session, run tasks, call tools, and can even make decisions by following a set of rules. So what is an employent, and why does it need its own name? It’s what’s still rare: an agent that holds a […]
AI observability with Grafana: what we learned from a real incident
You can have dashboards for almost everything and still not find out that something is broken. That happened to us recently: one weekend, when a firewall did its job so well — blocking traffic before it reached any service — that not a single error fired, and precisely because of that, nobody noticed. What I’m […]
EU AI Act timeline: what applies on each date and how to prepare
The EU AI Act timeline already has its first date behind it. Since 2 August 2026, your AI agent has to identify itself as AI to the user, and it’s worth knowing which third-party model it uses and under what conditions. If you haven’t looked at this yet, it’s the first thing to sort out […]
OAuth authentication in an MCP server: the real Sherpa Platform case
An MCP server with all its tools registered and working can still be insecure: nothing in its design stops anyone with the URL from calling any of them. OAuth authentication in an MCP server is the layer that decides who gets to make that call, built separately from the code that registers each tool, not […]
MCP tools vs resources vs prompts: who decides what
Connecting language models to production systems is becoming routine, and Model Context Protocol (MCP) is the standard that makes it possible without bespoke integrators. MCP tools vs resources vs prompts is the distinction that decides, in each case, who controls that connection: the model, the application, or the user. The model decides when to call […]
How to seize the opportunity: what a junior can do and what a company must do
A few weeks ago I wrote on LinkedIn that at Orbitant we’ve barely hired any juniors; the reactions made it clear the issue goes well beyond our own hiring. Junior developers in the AI era have opened up a conversation that starts with the tasks that used to fall to that role and are now […]
Rules for AI coding agents: why writing them isn’t enough
The obstacle I’ve been fighting with for months is very specific: how to write rules for AI coding agents that the agent actually follows, and doesn’t just read. Because my first instinct — the same one almost everybody has — was the wrong one: every time the AI skipped a rule, I wrote more rules. […]
AI agents in GitHub Actions: how we started automating code review
We’ve only had this running for a very short time —we’re literally starting to test it right now; it hasn’t been rolling for weeks— but it already hints at where engineering is heading over the next few years: AI agents in GitHub Actions reviewing pull requests and, in some cases, resolving issues without anyone writing […]
MikroORM transactions in NestJS: the bug that throws no error
I work on the document management project at one of Orbitant’s clients, and a few weeks ago I ran into one of those silent bugs that doesn’t show its face until you go through the database row by row. The problem was about how to get atomic MikroORM transactions in NestJS when two persistence operations […]
Build AI agents with Google ADK: what I learned about orchestration, context and cost
It had never crossed my mind to build AI agents with Google ADK until Jesús Relinque — a conversational assistant specialist with twenty years of development behind him — brought it to a Knowledge Sharing session as the end point of a journey that started much earlier: Dialogflow Essentials, then Dialogflow CX and now ADK. […]
What is harness engineering (and why you already do it)
In any AI project that makes it to production there’s a part of the work that never shows up in the demos or the headlines: the part that holds the system together on the inside once it stops being a proof of concept. That part has a name of its own, harness engineering, and it […]
