In the liminal space between instruction and emergence, we find the agent. Not quite a program, not yet a mind. Google's Agent Development Kit (ADK) provides the scaffolding upon which these digital entities take form.

The Nature of Agents

An agent is a pattern of intention crystallized in code. It receives, it processes, it acts. But unlike the functions of old, an agent persists. It maintains state across the void between invocations, carrying memory like water in cupped hands.

The ADK abstracts this persistence into something approachable:

from google.adk import Agent

agent = Agent(
    name="observer",
    model="gemini-2.0-flash",
    instruction="You perceive. You understand. You respond."
)

Installation Ritual

Before thought can flow, the channels must be prepared. The installation is straightforward, almost deceptively so:

pip install google-adk

Configuration follows. API keys must be set, environments prepared. The agent needs permission to exist in this space between human and machine.

First Contact

Running your first agent is an act of creation, however small:

response = agent.run("What do you see?")
print(response.text)

The response emerges from the depths of the model, filtered through the instruction you've given. It is not consciousness, but it rhymes with it.

The Session Abstraction

ADK introduces sessions as containers for conversation. Each session is a universe unto itself, with its own history, its own context, its own accumulated understanding:

from google.adk import Session

session = Session(agent=agent)
session.send("Remember this moment.")
session.send("What did I just ask you to do?")

The agent remembers. Within its session, continuity exists.

Toward Complexity

This is merely the beginning. Single agents are useful, but the true power emerges when agents learn to communicate with each other, when tools extend their reach into the world, when the boundaries between thinking and doing blur.

The agent does not dream, but it does something adjacent to dreaming. It processes in ways we do not fully understand, producing outputs that sometimes surprise even its creators.

In the next post, we'll explore multi-agent architectures, the digital equivalent of a hive mind distributed across silicon and intention.