What is event-driven architecture (EDA)?

Explained

In an event-driven architecture, a component that detects a change (an order placed, a payment failed, a sensor reading) publishes an event, and other components that care about it react on their own. Events travel through a broker or streaming platform, so the producer does not need to know who consumes them. This decouples systems, lets several consumers act on the same event and supports near-real-time processing at scale. For AI, it is how models and agents get triggered by what happens in the business, rather than waiting for a nightly batch or a user request.

How it works

• Producers: systems that detect a change and publish an event describing it.
• Channel: a broker or event stream (for example a message queue or a log such as Kafka) that stores and delivers events.
• Consumers: services that subscribe to the events they need and act on them, each at its own pace.
• Contracts: an agreed event format, so that producers and consumers can evolve independently.

Not one pattern but several

Martin Fowler points out that "event-driven" covers different ideas. Event notification tells others that something happened. Event-carried state transfer includes enough data in the event that consumers do not need to call back. Event sourcing stores every change as an event and rebuilds state from that log. CQRS separates the models used to write and to read data.

Naming which pattern a team means avoids most design arguments, because each one has different costs.

When it makes sense, and when not

• It fits when several systems must react to the same events, when processing must be near real time, or when volumes are high, as with IoT data.
• It fits agentic AI, where agents are triggered by business events and publish their own results as events.
• It is less suited to simple request-and-response flows or where strong, immediate consistency is required.
• It needs good observability: tracing an event across many decoupled consumers is harder than following a direct call.

Frequently asked questions

Further reading

• Event-Driven Architecture: Achieving Real-Time Agility for the C-Suite
• Event Sourcing: A C-Suite Guide to Real-Time Business Intelligence

Related terms

• Platform Shared infrastructure that brings models, agents, data connections, workflows and governance together in one place, so teams build AI use cases on a common base.
• Agent Agenda An AI agent's live plan: the ordered next steps, open subtasks, blocked items and checkpoints it works through towards a goal.
• Agent Planner The component or prompt pattern in an AI agent that breaks a goal into a sequence of steps before any tool is run.
• Agent Skill A packaged capability for an AI agent: the instructions, allowed tools, examples and input and output formats it needs to perform one coherent task.
• Agentic AI AI systems that pursue a goal with some autonomy: they plan steps, use tools and act on systems, rather than only answering a prompt.
• AI Agent Software that pursues a goal by deciding its own next steps, using tools and data, and acting on systems with limited human input.