How AI Agents Communicate: Managing Context in Workflows

Multi-agent AI systems depend on context, the information available to each agent when it makes decisions, and effective workflows are built around two core operations: retrieving context from other agents, tools, databases, APIs, or memory, and mutating context by changing records, approving requests, or completing actions. Specialized agents can supply domain knowledge, while external systems ground decisions in current data and internal memory preserves prior interactions. Chaining agents requires standardized handoffs, such as structured JSON, database updates, or summaries, so that one agent’s output reliably becomes another’s input. The main challenges in scaling these systems are often infrastructure-related rather than model-related, including secure access controls, reliable state updates, observability, and schema consistency; the material presents Kong’s connectivity platform as infrastructure intended to support these needs.

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