Agents

The Agent Harness: Why Your Model Isn't the Problem

LangChain jumped from outside the top 30 to number 5 on TerminalBench 2.0. They didn't change the mo ...

File-Based Agents Don't Need a Build Step

The investment banking analyst who spends Friday night formatting a pitch deck isn't doing analysis. ...

How autonomous is too autonomous

Autonomy in agents is a slider, not a switch, and the right setting depends on the task more than th ...

Evaluating agents when there's no single right answer

Evaluating a single prompt is hard. Evaluating an agent that runs ten tool calls before answering is ...

Agent memory: episodic, semantic, and what to keep

The first agent you build has no memory beyond the current conversation, and that works for about a ...

Memory strategies for long-running agents

Long-running agents accumulate context. The job of memory design is to decide which slices of that c ...

When the agent fails: recovery patterns that don't loop forever

Agent failures don't throw exceptions. They produce plausible-looking output that's wrong, or quietl ...

Designing an agent harness that doesn't fight the model

Lorem ipsum dolor sit amet consectetur adipisicing elit. The harness around an agent matters more th ...

Tool selection: when the model should pick, and when you should

Tool-using agents look powerful in demos because the model is choosing what to do next. They look fr ...