Why Agents Are More Than Fancy Prompts

What Exactly is an AI Agent?

An AI agent is a self-contained micro-service that happens to be powered by an LLM. It bundles three capabilities you don’t get from a naked prompt:

Perception

Reads state from the outside world (APIs, databases, docs).

Decision & Planning

Chooses goals, breaks them into ordered steps, remembers context.

Action

Executes steps, evaluates results, loops until goals satisfied.

Because an agent owns the loop (observe → decide → act) it behaves more like a software actor than a chat completion.

From LLM Prompts to Agent Protocols

“A prompt is a single instruction; an agent is a project manager.”

Capability	Prompts	Agents
Long-term memory & context	❌	✅
Multi-step planning	Limited (chain-of-thought)	Native task graphs
Tool / API calling	Manual via function-calling	Declarative in manifest
Collaboration (A2A)	❌	Standardized message bus
Governance hooks	Post-hoc log review	Embedded audit trail

Agents expose these features through Model-Context Protocols (MCP) and Agent-to-Agent (A2A) envelopes, letting multiple LLMs (Gemini, GPT-4o, etc.) interoperate safely.