How to Create Always-On Agents with IBM Developer Resources

Updated

IBM Developer's "Create always-on agents" featured banner points to a specific build pattern: connect real-time event streams with AI agents using Confluent, Apache Flink, and watsonx Orchestrate to build responsive workflows. The page offers a 30-day free trial entry point and a "Build event-driven agents" path. This guide explains what that stack does, how the pieces fit, and how to approach building an always-on agent, using the resources IBM Developer surfaces.

What "always-on" means here

An always-on agent is not a chatbot that waits for a user to type something. It stays subscribed to a stream of events and reacts when relevant events arrive. The IBM Developer banner frames this as "responsive workflows" — the agent is triggered by data in motion rather than by a request.

Typical use cases for this pattern:

  • Monitoring an event stream (transactions, logs, sensor readings, messages) and acting when a condition appears
  • Enriching or classifying events as they flow, then routing them to the right system or person
  • Coordinating multi-step responses where an AI agent decides the next action based on live context

The three components and their roles

Component Role in the pattern
Confluent Event streaming platform — carries the real-time event stream the agent listens to
Apache Flink Stream processing — transforms, filters, windows, and aggregates events before the agent acts
watsonx Orchestrate Agent orchestration — coordinates the AI agent's reasoning and actions across tools and services

The logic of the stack: Confluent moves events, Flink shapes them into something meaningful in real time, and watsonx Orchestrate gives the agent the ability to decide and act on what it receives.

Steps to build an event-driven agent

  1. Start from the IBM Developer build path. The featured banner links to "Build event-driven agents," which is the intended starting point for this pattern. Use it as your outline rather than assembling the stack from scratch.
  2. Establish the event stream. Set up the Confluent side so events flow continuously. The agent's "always-on" property depends entirely on this stream being live, not polled.
  3. Process events with Flink. Define the transformations or filters that decide which events are worth the agent's attention. Doing this in Flink keeps the agent from being flooded with raw noise.
  4. Wire the agent in watsonx Orchestrate. Connect the processed stream to the agent and define the actions it can take. This is where "responsive workflows" get their behavior.
  5. Verify with a live event. Send a test event through the stream and confirm it reaches the agent and triggers the expected action. If nothing happens, check the Flink stage first — a filter that drops the event is the most common silent failure.

Access and trial conditions

The IBM Developer page advertises a 30-day free trial as the entry point to this build. That is the only pricing signal present on the page — it does not state what happens after 30 days, whether a credit card is required, or what tiers exist beyond the trial. Treat the trial as a way to evaluate the pattern, and confirm ongoing terms on the product pages before committing to production use.

Where to go next on IBM Developer

The site organizes its content under Build, Explore, and Learn, alongside Upcoming events. For this topic:

  • Build — hands-on paths like the event-driven agents build
  • Learn — background material on generative AI, data science, and Java, which IBM Developer lists as its core learning areas
  • Upcoming events — the page lists a virtual hackathon, the Prague Open Source Summit Europe, and the IBM TechXchange Conference 2026 in Atlanta, which are opportunities to see this stack applied

If you are new to the platform overall, start with the general question of what IBM Developer is before diving into the agent build — the site is a training and learning hub, not a single product.