The Problem Statement It rarely happens that an AI agent decides to drop the database to “recover space”. But if your agent gets too creative, how can you prevent it from doing harm? To truly leverage the potential of AI agents, we need to grant them significant autonomy. I’ve been experimenting with various approaches, and my agents went the “wrong path” more than once. Typically, those incidents were harmless (my test agents run on a dedicated, isolated server).

The Problem Statement I wanted my agent to help me manage my emails, but I do not want it to be proactive and send emails in my name (which it did during my initial research phase). AI tried to spam potential customers - not what I wanted!!! Unfortunately, the default Gmail API is not very selective - the modification access also allows sending emails. The Implementation GPT5.5 suggested building a custom MCP extension to add extra guardrails.

I am impressed by how that model can navigate web pages and analyze data. Unfortunately, when that model generates a report, it makes even more spelling mistakes than I do. On the other hand, Bielik-11B-v3.0-Instruct is not as smart and capable as the Holo model - there is no way I would use it as the main “brain” of my local agent. But guess what? There is a way to make those two models cooperate in a way that each acts in the area it absolutely dominates!

How fast must your local AI be? The obvious answer is: my local AI must be as fast and as smart as the one offered by OpenAI (or other commercial vendor). "I want the best quality, and I want it now!" Ok, I get that, but let’s focus on the business reality for a sec. Probably, you won’t be able to match the performance of commercial AI providers. And that is totally fine.

Not always the latest, greatest, most hyped solutions deliver the best business outcomes. Often, it is hard to measure the actual impact of the “new shiny thing” on the business. Everyone writes about the MTP (Multi-Token Prediction) and how it improves the LLM performance. I wanted to leverage it to boost my local AI development team. My business case was the following: I wanted to switch from Qwen3-Coder-Next-UD-Q4_K_XL to Qwen3.6-27B-MTP-UD-Q4_K_XL for local agentic coding.

That is the common belief I see on the internet. I do not agree with that and have a process to prove otherwise. Currently, open-source models that can run on affordable hardware are not as capable as commercial AI models running in huge data centers. Everyone knows it, and I do not argue with that. My point is that it is perfectly possible to use open-source models running on local hardware to generate enterprise-class solutions that directly impact business operations and provide tangible business results.