--- title: "AI agents on open models: what works today and what doesn't" description: "The real state of AI agents built on open models: where they deliver concrete value today, and where a human supervisor is still needed in the loop." date: 2026-04-22 tags: [AI agents, Automation] url: "https://ai.malagoli.me/en/blog/agenti-ai-modelli-open-cosa-funziona" locale: en_US ---
# AI agents on open models: what works today and what doesn't
<!-- 2026-04-22 · AI agents · Automation -->
AI agents promise to automate entire workflows. On open models, today, some use cases work well in production — others don't, and it's better to know which before you build on top of them.
"Agent" has become one of the most overused words of the moment, often used to describe any automation that involves an AI model. It's worth being precise: an agent is a system that plans a sequence of actions, calls external tools, evaluates the results, and decides the next steps — not a single prompt with a single answer.
## Where open models work well today
- Tool calling against well-defined, documented APIs, with a limited number of possible actions.
- Research and synthesis agents over company documents (agentic RAG), where the task is to retrieve, compare, and summarize existing information.
- Automation of workflows with known, predictable steps — classification, routing, guided form-filling — with clear guardrails on what the system can and cannot do.
- Structured document processing (data extraction, validation, pre-filling) with a final human check.
## Where caution is still warranted
- Autonomous planning over long horizons, with many sequential steps and little supervision: the margin for error grows with every step.
- Ambiguous instructions that require common sense or tacit knowledge of the company context that isn't written down anywhere.
- High-impact, hard-to-reverse actions (payments, external communications, changes to production systems) without a human confirmation step.
## The principle we apply with clients
We start from a narrow, well-defined scope, with a person in the loop for decisions that matter, and expand the agent's autonomy only after gathering evidence that the system behaves reliably on that specific task. It's a less spectacular approach than a demo that does everything on its own, but it's the one that holds up when the agent has to work every day, not just in a presentation.
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