Good Stake -> Productive Agent

many believe that an AI agent is just a well-written prompt
beyond that, it is very important to select the proper agent pieces:
> LLM
> Tools
> Memory
> Triggers
> Feedback loop
not a single point - the agent is just an empty talker
1. LLM: the reasoning engine
this part defines objectives, course of action, and design of execution.
but LLM itself doesn't auto-access your systems, retain stable context, or act in the real world
that is why “just using GPT” is not the same as building an agent
2. Tools: the execution layer
it's hands for agent, this layer converts thought into action
your agent can check data, send messages, etc using tools
but without tools, ai agent is just text generation system
3. Memory: the context layer
it's what makes your agent consistent over time
this could be user preferences, schemes and styles of text outputs, etc
but remember: do not use your memory as a piece of paper with notes
this strategy is only going to get you a performance hit and make your outputs confusing
4. Triggers: the decision to wake up
a good agent does not need to be always running
it should cause itself to awaken by happening
this strategy works much better than polling system
5. Feedback loop: the process of improvement
a productive agent is not just reacting – it improves over time
i.e. its outputs are checked, errors are highlighted and corrected into prompts, tools, memory, or evals