Fix typos (#11208)

### What problem does this PR solve?

As title

### Type of change

- [x] Documentation Update

Signed-off-by: Jin Hai <haijin.chn@gmail.com>

docs/guides/agent/best_practices/accelerate_agent_question_answering.md

@@ -17,7 +17,7 @@ An Agent’s response time generally depends on many factors, e.g., the LLM’s
 
 - For simple tasks, such as retrieval, rewriting, formatting, or structured data extraction, use concise prompts, remove planning or reasoning instructions, enforce output length limits, and select smaller or Turbo-class models. This significantly reduces latency and cost with minimal impact on quality.
 
-- For complex tasks, like multi-step reasoning, cross-document synthesis, or tool-based workflows, maintain or enhance prompts that include planning, reflection, and verification steps.
+- For complex tasks, like multistep reasoning, cross-document synthesis, or tool-based workflows, maintain or enhance prompts that include planning, reflection, and verification steps.
 
 - In multi-Agent orchestration systems, delegate simple subtasks to sub-Agents using smaller, faster models, and reserve more powerful models for the lead Agent to handle complexity and uncertainty.