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When an agent works well, the model gets the credit. When it fails, the failure is usually somewhere else — a loop that never terminated, state lost on restart, a tool error swallowed silently, context that overflowed mid-task. Almost none of those are model problems. They are orchestration problems, which is where the real engineering…
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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…