// GLOSSARY -- AGENTIC AI

What is Chain of Thought (CoT)?

1 min read Updated

Chain of Thought (CoT) is a prompting technique where an LLM is guided to show its step-by-step reasoning process before arriving at an answer, significantly improving accuracy on complex tasks.

WHY IT MATTERS

Chain of Thought prompting was a breakthrough in LLM capabilities. By asking a model to 'think step by step,' researchers found dramatic improvements in math, logic, and multi-step reasoning tasks. The model doesn't just output an answer — it shows its work.

For AI agents, CoT is particularly important because agents need to reason about which actions to take. A financial agent deciding whether to execute a trade benefits from explicit reasoning about portfolio state and risk parameters.

CoT also improves auditability. When an agent's reasoning chain is visible, humans can review why specific decisions were made — critical for financial compliance and debugging.

FREQUENTLY ASKED QUESTIONS

Does Chain of Thought always improve performance?
Not always. CoT helps most on complex reasoning tasks. For simple factual recall or classification, it can reduce efficiency without improving accuracy.
What's the difference between CoT and ReAct?
CoT focuses on reasoning steps. ReAct (Reasoning + Acting) interleaves reasoning with actions — the agent thinks, acts, observes, then thinks again. ReAct is the standard pattern for tool-using agents.
Can CoT prevent agent mistakes?
CoT improves reasoning quality but doesn't eliminate errors. External validation like policy enforcement is still necessary for high-stakes decisions.

FURTHER READING

Enforce policies on every tool call

Intercept is the open-source MCP proxy that enforces YAML policies on AI agent tool calls. No code changes needed.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
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