Agents on davethehuman.com

chain-of-thought

Thu, 07 May 2026 00:00:00 +0000

COT (Chain of thought) is a style of intermediate-step generation that the LLM uses to make all the reasoning stages explicit and easier to follow.

With COT, the LLM does not just recall a fact but it rather gets to the conclusion through intermediate passages, resembling a person articulating their thoughts out loud.

Example:

Prompt: “Alice has 3 apples and Bob has 5 apples. Alice gives 1 apple to Bob. How many apples do they have together?”

agentic loop

Tue, 05 May 2026 00:00:00 +0000

The agentic loop consists of the core components of an AI agent:

Brain (or reasoning engine)
Planning: the agent uses techniques like COT or ReAct to decide what to do next based on previous outcomes
Memory:
- Short-term: the immediate context window (current conversation)
- Long-term: vector databases or logs that allow the agent to “remember” past experiences across different sessions
Tools (capabilities): APIs, web search, code execution environments, or database access. Tools allow the agents to affect the real world (e.g. booking a flight, sending an email, etc.)

AI agent

Tue, 05 May 2026 00:00:00 +0000

An AI agent is an autonomous software system that uses a reasoning engine (typically a Large Language Model) to perceive its environment, reason about goals, and execute multi-step actions using external tools to achieve a specific objective. It basically uses an LLM as its brain.

Unlike standard LLMs, which are reactive (answering prompts), agents are proactive (pursuing goals). Some example differences between them:

capabilities: the standard LLM can generate text and code, and the AI agent can get to action execution via APIs/Tools
logic: the LLM works with one-shot generation while the AI agent can do iterative planning and correction
memory: a standard LLM resets at every session while the AI agent’s memory persist via external storage

The agentic loop identifies the core components of AI agents.