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Does an Agent Have Temperature? Sampling in Agent Systems
An agent makes six model calls to answer one question. Asking what temperature the agent runs at assumes there is a single answer. There are six, and a well-built agent may deliberately use different values for different calls. The Short Answer Temperature is a parameter of a model call, not a property of an agent….
How to Build a Prompt Template Builder for AI Agents
A prompt change has no compiler, no type error and no failing test by default. The application keeps returning successful responses while task quality quietly declines. That silent failure mode is the whole reason the templating layer deserves to be real infrastructure rather than string concatenation scattered through your codebase. Platform details reflect the landscape…
Agent Prompt Patterns and the Frameworks That Implement Them
Two different questions get asked together here, and they deserve separating. The first is which prompting patterns agents actually use. The second is which libraries implement them. The patterns have been stable for a while. The tooling has not. Framework details reflect the landscape as of mid-2026. This space moves quickly — verify current capabilities…
How to Build Tools for LLM Agents — Patterns and Anti-Patterns
A tool definition is an unusual artifact. It is read by two completely different consumers — a runtime that validates it as a schema, and a language model that reads it as prose and decides whether to use it. Most tool design problems come from writing for only one of those readers. First, a Clarification…
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Does an AI Agent Train the LLM? What Actually Changes
You correct an agent. It acknowledges the mistake, adjusts, and gets the next answer right. It feels like teaching. Tomorrow the same agent makes the identical mistake, because nothing was taught. The model that answered you is byte-for-byte the model that will answer tomorrow. The Short Answer No. An agent does not train the model….