Real AI Agents Need Planning, Not Just Prompting
summary
Claim: most agents are just prompted LLMs and fail without planning. Built system: AI21 Mastro combines a planner plus a smart execution engine that generates candidate plans, prunes by budget, predicts cost/latency/success, parallelizes steps and replans. Results: measurable quality gains on GPT‑4o, Claude 3.5 and smaller models and on an internal requirement satisfaction dataset at the cost of higher runtime and spend. Concrete tools: planner, branch prediction, best‑of‑N sampling and validators in a dynamic planning with smart execution engine cycle.
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