Agent decision-making under resource constraints is bounded rationality reborn: Rational Metareasoning uses VOC rewards to save 20-37% of tokens, BATS proves that adding budget without budget awareness is futile, FrugalGPT cascades cut costs by up to 98%, and Speculative Actions reduce latency by 20%. The three constraints ultimately converge into a single Pareto curve, and the overarching trend is moving from humans tuning knobs to models making resource-rational decisions on their own.
Inferring another's beliefs/goals/intentions from observed behavior is called Machine Theory of Mind. Three lineages: symbolic BDI, Bayesian inverse planning, and deep learning ToMnet. The biggest controversy in the LLM era is that GPT-4 still trails humans by >10 points on ToMBench — are high scores genuine reasoning or statistical shortcuts?
Exec supports foreground/background/PTY execution with three security levels (deny/allowlist/full). Thinking has 7 levels (off to adaptive). Slash Commands come in two types: commands and directives.
Kimi is a large language model from Chinese AI startup Moonshot AI, known for its ultra-long context window, open-source strategy, and highly competitive pricing. From 200K context in 2023 to K2.5 Agent Swarm in 2026, Kimi has become a force that the global AI market cannot ignore.
An AI agent is not a black box — it is built from three layers: what it knows (Context), how it thinks (Cognition), and what it can do (Action). Understanding these three layers is the key to grasping why agents are sometimes brilliant and sometimes go off the rails, and how to design a truly effective agent system.
For complex queries, have the LLM map out what information is needed and in how many steps — then execute that plan. More systematic than thinking on the fly.