Agent Coordination

• Key points are in the visual diagram above.

Without coordination, agents duplicate work, contradict each other, or deadlock waiting for resources. Coordination is the glue of multi-agent reliability.

Coordination is air traffic control — planes (agents) are capable alone, but controllers prevent collisions and sequence landings.

Scenario

Two agents both try to update the same CRM record — coordination layer acquires a lock, sequences writes, and broadcasts the final state.

Solution

In Multi-Agent Systems, apply Agent Coordination to this scenario: Two agents both try to update the same CRM record — coordination layer acquires a lock, sequences writes, and broadcasts the final state. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Agent Coordination (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Agent Coordination happens in the code.

1# Shared state via Redis2async def coordinate(agent_id, update):  # key line for Agent Coordination3    async with redis.lock(f"crm:{record_id}"):4        state = await redis.get(key)5        merged = merge_policy(state, update, agent_id)  # key line for Agent Coordination6        await redis.set(key, merged)

• Treating Agent Coordination as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for agent coordination

Quick recap — the most important points from this module.