Multi-Turn Reasoning: Agents That Think Across Steps

A single prompt forces the AI to generate an answer in one go. For hard problems — proofs, long code, research — even the best model gets some parts wrong on the first pass. Multi-turn flows let the model reflect, critique itself, and retry.

The generate-critique-revise loop

TURN 1 (generate):
'Write a 200-word argument for why schools should start later. Make it persuasive.'
--> draft v1

TURN 2 (critique):
'You are a skeptical peer reviewer. List the three weakest points in the argument above. Be specific.'
--> critique list

TURN 3 (revise):
'Using the critique, revise the argument. Address each of the three weaknesses. Keep it under 200 words.'
--> draft v2

Each turn plays a different role — author, critic, editor. Studies on Claude and GPT consistently show that a two- or three-pass structure outperforms a single pass, especially on reasoning-heavy tasks. The model is effectively its own reviewer.

The ReAct pattern (reason + act)

ReAct interleaves reasoning steps with tool calls. Instead of one big plan, the model thinks a little, uses a tool (search, calculator, code execution), reads the result, thinks again, and so on. This is the foundation of modern AI agents.

THOUGHT: I need to find the CEO of Notion and their hiring trends.
ACTION: search("Notion CEO")
OBSERVATION: Ivan Zhao is CEO of Notion Labs.

THOUGHT: Now I need recent hiring data for Notion.
ACTION: search("Notion hiring 2026")
OBSERVATION: Notion announced 200 new engineering roles in Q1 2026.

THOUGHT: I have enough to answer.
FINAL: Notion, led by CEO Ivan Zhao, announced 200 new engineering roles in Q1 2026.

Reflection on tool failures

1. 1If a tool errors, don't just retry blindly — have the model reason about why and adjust inputs.
2. 2If a tool returns empty results, try a broader query before giving up.
3. 3If results contradict each other, surface the contradiction rather than silently picking one.
4. 4Cap the loop — set a max of N iterations so the agent can't spin forever.

Keeping context manageable

Multi-turn flows accumulate tokens quickly. Strategies: summarize older turns into a running memo; store long data in a scratchpad tool; use XML tags to section the conversation. A 1M-context Claude can hold a lot, but cost and latency still grow.