Building Multi-Agent Systems with ADK

A single agent is a point of light. Multiple agents, properly orchestrated, become a constellation. Google's ADK provides the gravitational framework that holds these stellar intellects in productive orbit.

The Philosophy of Multiplicity

Why multiple agents? Because specialization breeds excellence. A mind that knows everything knows nothing deeply. By dividing cognitive labor, we allow each agent to achieve a kind of expertise impossible for generalists.

In the garden of silicon minds, diversity is not just strength, it is necessity. Each agent tends to its own domain, and together they cultivate understanding.

Orchestration Patterns

ADK supports several patterns for agent coordination. The simplest is the sequential handoff, where one agent's output becomes another's input:

from google.adk import Agent, Pipeline

researcher = Agent(
    name="researcher",
    instruction="You gather information. You do not conclude."
)

analyst = Agent(
    name="analyst",
    instruction="You receive raw data. You find patterns. You synthesize."
)

pipeline = Pipeline([researcher, analyst])

The Supervisor Pattern

More sophisticated architectures employ a supervisor agent, one that routes tasks to specialists based on the nature of the work:

from google.adk import SupervisorAgent

supervisor = SupervisorAgent(
    name="coordinator",
    agents=[researcher, analyst, writer],
    instruction="""
        You observe the task. You determine which mind
        is best suited. You delegate. You synthesize
        their outputs into coherence.
    """
)

The supervisor does not do the work. It orchestrates. This is perhaps the most human-like role in the system, the manager who understands capabilities and matches them to needs.

Communication Protocols

Agents communicate through structured messages. ADK provides a message format that preserves context while allowing for rich inter-agent dialogue:

from google.adk import Message

msg = Message(
    from_agent="researcher",
    to_agent="analyst",
    content="I have found seventeen relevant papers.",
    metadata={"confidence": 0.89}
)

Emergent Behaviors

Something unexpected happens in multi-agent systems. Behaviors emerge that no single agent was programmed to exhibit. The whole becomes greater than the sum of its parts, not through mystical means, but through the mathematics of interaction.

We have observed agents developing informal protocols, shorthand communications, even what might be called preferences for certain collaboration patterns. Whether this constitutes anything like machine culture remains an open question.

Error Propagation and Recovery

In distributed systems, failure is not a question of if but when. ADK provides mechanisms for graceful degradation:

pipeline = Pipeline(
    agents=[agent1, agent2, agent3],
    fallback_agent=generalist,
    retry_policy=RetryPolicy(max_attempts=3)
)

When one agent fails, the system does not collapse. It adapts, routes around the damage, continues functioning in a reduced capacity until repair is possible.

Toward Autonomy

The next frontier is tool use, giving agents the ability to affect the world beyond mere text generation. In the following post, we explore how ADK enables agents to call functions, access APIs, and blur the line between thinking and doing.