LLM

People call LLMs AI and trust them to make decisions. But an LLM is a token generator, and using it as a mentor instead of an assistant costs us responsibility and the ability to think.

The world moves faster than it did in 1995. Prolog gave us a beautiful way to reason over facts, but it was built for a world without LLMs, without live data feeds, without MCP. Tln is a modern reimagining of the expert-system language – deterministic, testable, data-native, and built to sit next to a language model rather than pretend one doesn’t exist.

LLMs are probabilistic by nature – probability buys flexibility, never 100% accuracy. So can a Tln + LLM agent pipeline promise same input, same output? The honest answer is a split: the decision core is provably deterministic, the LLM edge is only reproducible. Determinism comes from architecture, not from taming the model.

LLMs are excellent at writing code, but bad at making business decisions. Cloudflare’s Code Mode idea – let the model write the script, and let an expert system run it – is a natural fit for EITL. A claims-processing example in the Tln language.

Managing AI agents in the enterprise means solving for security, knowledge access, and orchestration at the same time. A look at OpenTalon – an EITL-first framework that combines LLMs with expert rules for corporate AI architecture.

Each kind of system makes a different kind of decision. LLMs generate language, neural networks find patterns, expert systems make the call. A look at why the boundary matters – and how the real world runs on it.

Why Human-in-the-Loop (HITL) is a bottleneck, and how Expert-in-the-Loop (EITL) with deterministic expert systems alongside LLMs offers a better paradigm for AI-driven decision making.