Speculative execution (agents)

Speculative execution in agents launches multiple plausible tool calls in parallel before knowing which the user wants — accepting the winning result and discarding the others — to cut perceived latency.

Distinct from speculative decoding (token-level), speculative execution operates at the agent step level. The agent predicts the most likely tool call and pre-runs it in parallel with the LLM's actual decision; if the prediction matches, the result is already ready; if not, the unused result is discarded. Used heavily in voice agents and latency-critical assistants in 2026 where waiting on tool calls before responding produces awkward pauses. The cost is wasted compute on rejected predictions; the win is materially better perceived latency on the happy path.

When to use speculative execution (agents)

• Latency-critical voice agents.
• Realtime assistants where tool calls add noticeable lag.

Common mistakes

• Speculating too widely — wastes compute without consistent wins.
• Side-effecting speculative calls — tools that change state can't be rolled back.

FAQ

Related terms

• AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
• Agent loop — An agent loop is the repeating cycle of an AI agent — observe state, decide on an action (usually a tool call), execute, observe the result, and repeat — until a goal is reached or a stop condition fires.
• Voice (LLM apps) — Voice in LLM apps refers to the full speech pipeline — speech-to-text (STT), language model, text-to-speech (TTS) — that lets users converse with an AI assistant in spoken language.
• Speculative decoding — Speculative decoding is an inference technique where a small "draft" model proposes several tokens at once and a large "verifier" model accepts or rejects them, cutting latency by 2-4x.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/speculative-execution.md.