If you're building an AI agent that touches dates — booking flows, scheduling bots, "remind me on...
If you're building an AI agent that touches dates — booking flows, scheduling bots, "remind me on Friday" assistants — you've probably noticed:
LLMs are terrible at dates.
They hallucinate weekday-to-date mappings. They fencepost-error ranges. They forget what "next Friday" means in Ukrainian vs English. Asking the model to "be careful" doesn't fix it — what fixes it is moving date interpretation out of the model and into a deterministic tool.
That's what whenis is.
Use it as an agent tool
Constant-Cost Persistent Semantic State Memory Engine for LLM Agents | Towards AI
Evaluating RAG with LLM as a Judge | Mistral AI
LLMs help robots understand vague instructions and focus on key details
Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic
It happened on a Tuesday. I asked my AI coding assistant to explain a function I'd written three...
AI safeguards can backfire when models learn to mimic the signals meant to verify truth. In one system, memory design and tool…
LLMs are great at generating text. They're terrible at generating structured data reliably. If you've...
The problem While working on my Swiftcart QA automation project, I asked my AI agent a...
Every new chat with your LLM starts the same way. Hi, here's the context. Here's the stack. Here's...
Let the model interpret. Let software validate. Let workflows own state, approvals, and side effects.
