Using LLMs to Configure MikroTik Networks: Lessons
16 Jul 2026
A hands-on experiment in AI-assisted networking
A blogger has published a detailed account of using large language models (LLMs) to configure MikroTik networking equipment over the last few months. The write-up, framed as practical field notes rather than a formal study, documents real-world use of LLMs to set up networks for friends and friends' offices — and offers a candid look at both the upside and the failure modes of leaning on AI for infrastructure work.
What happened
According to the report, the author spent several months using LLMs as hands-on assistants for MikroTik configuration tasks. Over that period, the author:
- Configured networks for friends and friends' offices using LLM assistance
- Compiled a list of 11 tips for using LLMs effectively in MikroTik configuration work
- Built a Homebrew formula to simplify installation of MAC-Telnet
- Created a small CLI tool designed to make MAC-Telnet easier for LLMs to work with
The author's core takeaway is that LLMs function as a "chaotic force multiplier" — the models understand MikroTik and general networking concepts well, but they still make mistakes and can drift off the intended configuration path if left unsupervised.
The risks the author flags
The report highlights three recurring risks from the experiment:
- LLMs can hallucinate during network configuration tasks, introducing errors
- LLMs may go off-path from intended steps without careful oversight
- Relying on LLMs for critical infrastructure setup without incremental testing could lead to misconfigurations
To mitigate these, the author's recommendations include minimizing the scope of each task, working through changes one at a time, testing after every configuration change, and never assuming the LLM won't make a mistake — precisely because hallucination remains a real possibility.
Why founders should care
For founders building developer tools, AI agents, or infrastructure automation, this account offers a few probabilistic signals worth weighing:
- It's plausible that LLMs can meaningfully accelerate niche technical work like network configuration, acting as a force multiplier for solo operators or small teams without dedicated networking expertise.
- It's also likely that any LLM-driven automation touching critical infrastructure carries a non-trivial error rate, suggesting human-in-the-loop verification and incremental testing may be necessary design choices rather than optional safeguards.
- There may be early market demand for tooling that makes specialized or legacy protocols — like MAC-Telnet — more accessible to LLMs, as evidenced by the author's decision to build both a Homebrew formula and a custom CLI wrapper to reduce friction.
For teams building in the AI-for-ops or AI-for-devtools space, the pattern here — documented best practices paired with lightweight supporting tools — could be a template worth watching.
What's still unclear
The report leaves several open questions. It's not specified which specific LLM(s) were used, nor what exact errors or hallucinations occurred during the setups. The total number of networks or offices configured this way isn't quantified beyond "friends and friends' offices," and it's unclear whether the CLI tool or Homebrew formula are publicly available. The full list of all 11 tips also wasn't fully detailed in the report beyond the core recommendations around task minimization and incremental testing.
Bottom line
This isn't a case study with hard metrics or enterprise validation — it's an individual's documented experience. But for founders exploring AI-assisted infrastructure or devtools, it offers a grounded reminder: LLMs can extend technical capability, but treating them as fully autonomous for critical setup tasks carries real risk. Incremental verification, not blind trust, appears to be the operating model that worked here.