Event-driven agentic AI: one of the most lucrative skills in 2026

More and more companies will start adopting AI, as more business reasons for its adoption become apparent.

And no, I am not talking about AI-assisted coding. While it's very useful, it's also extremely overhyped. The degree of its usefulness is very context-dependent. The hype creates too much noise that masks the real opportunities.

The type of AI enterprises are adopting more and more of is event-driven agentic AI.

This isn't something you can just vibe-code into existence. To build an event-driven system, you need to be good at both software engineering and AI engineering.

These are the systems that automate complex business processes that were extremely hard to automate in the past.

I will give you a very relatable example of how event-driven agentic AI is used. Then, I will tell you which skills you will need to develop to get into the field.

Let’s begin with a real-life example.

Error triage without waking developers up

Imagine a situation where errors start to spike in the system. Normally, this is when developers or support engineers will be alerted to diagnose and solve the problem. However, with event-driven agentic AI, instead of waking up developers on call by sending a PagerDuty notification to them, the spike in errors triggers an agentic orchestration.

A fully automated agentic team then starts its triage. Each agent has its role to keep the context window laser-focused. These roles are defined by system prompts — the same as normal prompts you would use to communicate with ChatGPT, but defined by the system rather than a user.

Here’s an example of how these agents can be defined in Python with the help of the AutoGen agentic orchestration framework. First, we would define the triage lead agent:

triage_lead = AssistantAgent(
            name="TriageLead",
            model_client=model_client,
            system_message=(
                "You are the incident triage lead. Decide what to do next. "
                "Ask other agents for inputs. Finish by requesting a final customer update + internal steps."
            ),
            tools=tools,
            reflect_on_tool_use=True,
        )

Then, we would define the agent that looks up appropriate runbooks to learn how to respond to the incident:

runbook_researcher = AssistantAgent(
            name="RunbookResearcher",
            model_client=model_client,
            system_message=(
                "You search internal runbooks/postmortems. Use the search_runbooks tool. "
                "Return key snippets and recommended checks. Keep it short."
            ),
            tools=tools,
            reflect_on_tool_use=True,
        )

The next agent is responsible for defining the incident mitigation plan:

fix_planner = AssistantAgent(
            name="FixPlanner",
            model_client=model_client,
            system_message=(
                "You propose mitigations/rollback plans and verification steps. "
                "Prefer low-risk mitigations first. Provide a step-by-step plan."
            ),
            tools=tools,
            reflect_on_tool_use=True,
        )

Finally, there’s an agent that reviews the risk and, based on its assessment, either approves or denies the plan:

risk_reviewer = AssistantAgent(
            name="RiskReviewer",
            model_client=model_client,
            system_message=(
                "You review customer-facing text for safety: no guarantees, no blame, no sensitive details. "
                "If acceptable, respond with 'APPROVED' and the improved customer update. "
                "If not, respond with 'CHANGES NEEDED' and corrections."
            ),
        )

The agents form a group chat and discuss how to solve the incident:

team = SelectorGroupChat(
            participants=[
                triage_lead,
                runbook_researcher,
                fix_planner,
                risk_reviewer],
            model_client=model_client,  # used for selecting the next speaker
            termination_condition=termination | hard_cap,
        )

        task = f"""We have an incident.

Title: {title}
Symptoms: {symptoms}
Context: {context}

Goals:
1) Gather likely causes and validation steps using runbooks.
2) Produce internal triage steps (engineer-facing).
3) Produce a customer update (2-4 sentences) reviewed by RiskReviewer.
When RiskReviewer is satisfied, they must include the word APPROVED.
"""

        result = await team.run(task=task)

To make the agents work, they need access to tools, which, in the context of agentic AI, can be methods, functions, or API calls. In our case, the agents would use the following Python method as a tool. It will search the RAG vector store for appropriate runbooks that describe the process for solving the incident:

def search_runbooks(query: str, top_k: int = 5) -> str:
    """
    Search internal runbooks/postmortems for incident triage.
    Returns short snippets with source names.
    """
    retriever = _build_retriever(top_k=top_k)
    docs = retriever.get_relevant_documents(query)

    if not docs:
        return "No relevant runbooks found."

    lines: List[str] = []
    for i, d in enumerate(docs, start=1):
        title = (
            d.metadata.get("title")
            or d.metadata.get("source")
            or d.metadata.get("file_name")
            or "unknown-source"
        )
        snippet = (d.page_content or "").strip().replace("\n", " ")
        snippet = snippet[:500] + ("..." if len(snippet) > 500 else "")
        lines.append(f"[{i}] {title}: {snippet}")

    return "\n".join(lines)

Once the agents finished their triage process, they can delegate the work to another agent downstream. That agent will decide whether the incident is simple enough for it to solve. If the incident is simple enough, it solves it, so no human intervention is required. If it's too complex (or too risky), they notify an appropriate team member with the distilled incident summary, so the person doesn't have to look up the logs themselves.

Yes, someone may still have to be woken up. But this happens much less frequently. And when it does, the team member is provided with enough information already, so they don’t have to search the logs.

The same principles can be applied to other situations. For example, a refund request from a customer can go through various fraud-checking steps. Then, based on a variety of factors (fraud risk, amount, etc.), the agents can decide whether to just issue the refund or get it approved by a real person. The latter is known in the “human-in-the-loop” pattern.

Hopefully, you see how useful this concept is to real enterprises. That’s precisely why more and more are adopting it. The fact that event-driven agentic AI is both in demand and requires special skills makes it a very lucrative field to join.

So, what specific skills do you need to join it? We will talk about it next.

What skills do you need to learn to get into event-driven agentic AI

Firstly, you will need to develop AI engineering skills. I talked about the key skills in the area in this article. Since agentic AI heavily depends on LLMs, there are also some LLM-specific engineering skills that are important.

However, this is not enough. You also need to be well-versed in event-driven and message-driven architecture. And I prepared a couple of short courses for you that you can complete in a few hours, yet gain a good understanding of these concepts.

First, there’s my Event-driven Agentic AI course on Pluralsight. It goes over all the key areas you need to know if you want to build an event-driven AI system. It provides a birds-eye view of these concepts, so it’s only an hour long. You can easily complete it in a few sittings.

Then, if you want to gain hands-on experience in building event-driven and message-driven systems, I have prepared a fully interactive course on DataCamp for you. It teaches you how to use Azure Service Bus, Event Grid, Event Hubs, Storage Account, etc. You will have an opportunity to build interactions between these services in real Microsoft Azure without having to set up your own subscription. Moreover, this course is part of the AZ-204 Azure certification track.

Both subscriptions are cheap and will give you access to the entire library of courses, not just the two courses I mentioned. I use both DataCamp and Pluralsight myself and only have positive things to say about these platforms.

Wrapping up

Event-driven agentic AI is rapidly becoming an area that businesses are prioritizing investment in. Therefore, being able to build event-driven agentic systems is rapidly becoming an in-demand, lucrative skill.

To get into this area, you need to have both AI engineering skills and cloud engineering experience focused on building event- and message-processing systems.

Because I myself work in this area and want to help more people get into it, I will be focusing more on this subject in the content I create. Therefore, if you are new to my blog, consider subscribing if you want to learn more about AI engineering, cloud engineering, and software engineering that touch upon my area.

For a more in-depth overview on how event-driven agentic systems are built, and to gain some practical experience in the skills required to build such systems, consider checking my new courses:

• Pluralsight: https://app.pluralsight.com/ilx/video-courses/event-driven-agentic-ai

• DataCamp: https://datacamp.pxf.io/BnqekW