Agentic AICoT

Chain-of-Thought

CoT

Chain-of-Thought (CoT) is a prompting technique that instructs an AI model to reveal its reasoning step by step. By explicitly decomposing complex problems into logical intermediate steps, response quality for analysis, calculation, and planning tasks improves significantly.

Why does this matter?

For mid-market companies, Chain-of-Thought is crucial because it makes AI decisions transparent. When an agent performs supplier evaluation, CoT shows each assessment step — price, quality, delivery reliability — individually. This builds trust and enables human review.

How IJONIS uses this

We systematically integrate CoT prompting into all AI agents we develop with LangChain and LangGraph. Every reasoning step is logged and can be traced through a dashboard — a basic requirement for auditable AI decisions in enterprise use.

Frequently Asked Questions

Does Chain-of-Thought slow down AI processing?

CoT generates more tokens and thus requires slightly more time and cost per request. In practice, the overhead is marginal (milliseconds), while the quality improvement on complex tasks is substantial — fewer errors means less costly rework.

Can I view the Chain-of-Thought steps of my AI agent?

Yes. We implement tracing systems (e.g., LangSmith) that log every reasoning step. You can trace every decision your agent makes — an important requirement for compliance and internal audits.

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