AI Agents

Agents are autonomous or semi-autonomous systems powered by LLMs that can take actions, make decisions, and operate over time to accomplish a goal. Unlike simple prompt-response interactions, agents use memory, tools, and planning loops to operate with persistence and autonomy.

Core Components

• LLM Backbone: The reasoning engine that generates thoughts, plans, or actions.
• Memory: Stores past actions, inputs, or external knowledge to maintain context over time.
• Tools / Plugins: External functions the agent can call (e.g., calculators, search APIs, databases, code execution).
• Planner / ReAct Loop: Logic that lets the agent think, act, observe, and iterate.
• Environment: The context or system in which the agent operates (e.g., chat, browser, API workflow).

Key Behaviors

• Autonomy: Acts without needing constant human intervention.
• Goal-Oriented: Works toward user-defined or system-defined objectives.
• Tool Use: Interfaces with external systems or APIs to gather data, perform calculations, etc.
• Reflection / Iteration: Can evaluate its output and adjust in future steps.

Examples

• AutoGPT / BabyAGI: Autonomous loops that plan and execute multi-step tasks.
• LangChain Agents: Use LLMs with tool-calling abilities.
• OpenAI Function Calling Agents: Plan → call function → interpret result → repeat.
• Workflow Automators: Agents embedded in productivity tools to orchestrate actions (e.g., email sorting, calendar management).

Use Cases

• Multi-step task automation
• Research assistants
• Customer support bots
• Autonomous coding
• AI-powered browser automation
• Conversational flows with memory and reasoning

Limitations

• Expensive and slow (many API calls)
• Can hallucinate tool usage or fail gracefully
• Requires tight prompt control and error handling
• Debugging agents is harder than single prompts

Related Notes

• Prompt Engineering
• RAG
• Self-Evaluation
• Function Calling
• LLM Generation
• Planning and Reasoning
• AI Agents Roadmap