Blog

Your agent passed 100 tests in the PoC and today someone is complaining. Is it failing? Expensive? Degraded? Without continuous evals, you don't have AI in production — you have AI waiting to break.

AI agents living inside Sylius and operating on catalog, checkout, and pricing. Full architecture, four production cases, and the metrics that move.

Vectorizing your documents isn't the problem. The problem is making the agent take the right decision with that context. The 7 failures that show up when your RAG leaves the notebook.

Deterministic tests remain the floor; continuous evals are a new layer on top. How they coexist, what each covers, and how we integrate them in CI so a TDD-classic team can adopt the discipline without losing ground.

Model intelligence stopped being the bottleneck — memory is. How to build an agent that learns, remembers, and stops paying the cost of rediscovering everything each day.

When several AI agents collaborate inside a system, classic DDD concepts offer the framework to design it with criteria: each agent as a context, aggregates as state owners, explicit translation between domains.

You switch from Claude to GPT and everything stops working the same way. It isn't the model. It's that your context was badly designed. Why context engineering is the real moat of serious AI projects.

Generative AI amplifies underlying architectural quality. On a well-designed system, it multiplies capability; on a poorly-designed one, it multiplies failure. Five concrete reasons and data from production projects.

A Digital Worker isn't ChatGPT with access to your Slack. It's an agent with a bounded role, assigned tools, decision thresholds, and measurable metrics. The complete anatomy, part by part.

Each platform solves a different kind of problem, and the choice shapes three years of roadmap. The practical comparison we hand to CTOs and Heads of Digital before signing the stack.

The hexagonal pattern maps precisely onto an AI agent: clean domain, ports as tools, adapters as real integrations. Architecture and a production case with code.

Before USB, every peripheral needed its own cable. Before MCP, every AI agent needed a custom integration. How MCP changes the cost of connecting AI to your systems — if you build it right.

Your HubSpot has the data but doesn't exploit it. 10 AI use cases that turn your CRM into a proactive tool — packaged in three maturity levels with real metrics.

Your AI agent has access to the CRM. The right answer isn't 'trust the model' — it's not giving it the option. Three layers of guardrails that separate a safe agent from a loose one.

Your WhatsApp chatbot recites a script. When the customer steps out of it, you lose them. An agent converses, queries your CRM, and executes the action. That's the difference between losing leads and closing them.

80% of AI projects don't make it to production. Tech rarely fails — the use case does. The matrix, the antipatterns, and the method to pick the first one with criteria.

Microservices is the mental default for many CTOs in 2026, and the modular monolith remains the right answer more often than is acknowledged. Five dimensions and three real cases to decide with criteria.

Learn what agentic AI is, how it differs from generative AI, real enterprise use cases, governance guardrails, and how to implement it safely.

Learn when custom software makes sense vs off-the-shelf, the development process, costs, and common mistakes. A guide for CTOs and decision-makers.

A detailed comparison of React Native and Flutter in 2026. Performance, ecosystem, learning curve, and use cases to help you choose the right framework.