Agent Teams (Multi-Agent Systems)

Single agents have context limits and capability ceilings. Agent teams — multiple agents collaborating under a coordination layer — can tackle tasks that exceed what any single agent could handle.

Why Agent Teams?

Parallelism — independent sub-tasks run simultaneously, reducing total wall-clock time
Specialisation — each agent is optimised for a specific role (researcher, writer, reviewer, coder)
Scale — teams can tackle tasks too long for a single context window by dividing work
Redundancy — critical outputs can be reviewed by a second agent before acceptance

Coordination Patterns

Pattern	Framework	Best for
Hierarchical	Google ADK, LangGraph	Clear task decomposition with a known structure
Role-based	CrewAI	Collaborative workflows with defined agent personas
Handoff	OpenAI Agents SDK	Sequential pipelines with specialised agents
Conversational	AutoGen (Microsoft)	Exploratory problems with dynamic discussion

2025 Production Landscape

By 2025, the six major production frameworks each embody one of these coordination philosophies. See the agents index for the full framework comparison table.

The A2A protocol (Google, April 2025) enables agent teams to span organisational and vendor boundaries — an agent built on LangGraph can delegate to an agent built on Google ADK, mediated by the A2A standard.

Source

Google A2A Protocol, April 2025

## Key Considerations

Coordination overhead — every inter-agent message adds latency and token cost; design for minimal necessary communication
Shared state — teams need a reliable way to share intermediate results; use explicit state management (LangGraph's checkpointing, or a shared database)
Failure propagation — if one agent fails, the team needs a recovery strategy; don't assume sub-agents always succeed