The Svolta blog. Written from inside the work.
Focused answers on workflow measurement, automation, AI agents, integration, evaluation, governance, and operating production systems.
Focused questions. Production answers.
Each article answers one buyer or operator question and links back to the relevant guide, service, and proof. Technical depth stays when it helps the decision.
Evals before agents: the order that saves the project
Write the eval set on your own data before you pick the framework, the model, or the vector database. Skip this and the project burns a quarter relitigating quality questions you could have answered in a week.
AI agent infrastructure: the layer that makes agents safe to run
Agents fail in production for infrastructure reasons, not model reasons: no grounding, no evals, no audit trail, no data layer. The five components underneath every agent system that survives contact with a real queue.
Custom AI agent development services: what 'custom' should mean
Every vendor sells 'custom' AI agents. The word should mean your workflow, your systems, your data, your evals, and your ownership at handover. A checklist for reading proposals, and the parts that should never be custom.
Agentic workflows that survive production
A demo agent and a production agent share a vocabulary. They do not share an infrastructure. Caching, deterministic fallbacks, observability, eval gates on every change. The boring discipline most teams skip.
Process mapping is the first AI artifact
Map the process before you write a prompt. The map is where you find the work an agent can actually do, and the implicit context that will kill the project if it stays hidden until integration.
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