20 published lessons with this tag.
When you run several cognitive agents they need to work together not interfere with each other.
When two agents try to handle the same situation they may give conflicting instructions.
When agents conflict the higher-priority agent wins.
Some agents must run in a specific order — define the sequence explicitly.
Some agents can run simultaneously while others must wait for previous results.
When agents need to share information define clearly how that information flows.
Define how the output of one agent becomes the input of another.
A meta-agent that coordinates other agents by deciding which should run when.
When one agent finishes and another starts the relevant context must transfer cleanly.
Draw the dependencies between your agents to see the full coordination picture.
When two agents each wait for the other neither can proceed — design to prevent this.
When multiple agents need the same scarce resource like your attention define allocation rules.
Common patterns like pipeline fan-out and consensus for coordinating multiple agents.
Coordination itself costs effort — keep the coordination cost proportional to the benefit.
Your set of agents is an ecosystem — it needs balance and periodic assessment.
Every new agent interacts with all existing agents — add new agents deliberately.
When retiring an agent update everything that depended on it.
Periodically assess how well your agents work together as a system.
When your agents work together smoothly the result looks like natural ability to others.
Optimize how agents connect and hand off to each other, not just how each agent performs in isolation.