Agent-ready APIs: clear actions and reliable retries
APIs for AI agents: validation, authorization, idempotency, and handling retries without uncontrolled duplication of actions.
Blog
One agent or a team of agents? How do we coordinate development without conflicts
One agent or more? Delegation, separate workspaces, handover of results, and code integration without overwrites.
How we prepare code for AI agents: context, rules, and test environments
Get the repository ready for AI agents: clear instructions, AGENTS.md, architectural context, test data, and reproducible checks.
Virtual machines, containers, or Kubernetes: how much infrastructure do we need?
Virtual machines, containers, and Kubernetes explained with a practical example, including criteria for isolation, operation, and scaling.
On-premises, cloud, or hybrid? Where do we build the next project?
On-prem, cloud or hybrid? Practical criteria for placing your application, data, and AI components, with explicit responsibilities and costs.
RAG in 2027: how can we connect AI agents to company knowledge
RAG guide for companies: how it works, current methods, second brain, tools for AI agents, security, and local or cloud solutions.
Build, buy, or integrate? How to choose the AI agent platform
Build, buy, or integrate? A guide for SMEs on AI agent platforms, total cost, security, piloting, and selection criteria.
How to choose the right LLM model for each task
Practical guide for SMEs: evaluating LLMs by quality, Romanian language capability, total cost, latency, confidentiality, and risk.
The next software buyer might be an agent. How can SaaS platforms sell to AI agents
Guide to SaaS for AI agents: discovery, provisioning, programmatic payments, vendors, pricing, security, and implementation steps.
MCP (Model Context Protocol): history, architecture, current status, and uses
A verified guide to the Model Context Protocol: how it works, version evolution, current status, security, and real-world use cases.