Main image of article Why Agentic AI Needs You (Not Replaces You)

Chances are good you’ve already heard of agentic AI. Executives everywhere are dreaming of AI-powered “agents” that will autonomously execute dozens of tasks with zero human input. Will this future (if it comes to pass) shrink teams and kill jobs? That’s an important and pressing question—and it’s important to remember that agentic AI may also create more tech jobs, especially in the long term.

Agentic AI is an extremely complicated concept, with quite a bit that could go spectacularly wrong, and it will require skilled tech pros who can build, manage, and iterate on these systems. Today’s tech pros may become tomorrow’s “AI orchestrators,” making sure that dozens of discrete AIs operate effectively together. With that in mind, let’s explore what the next few years might hold for AI; we’ll also examine some of the AI-related skills currently on the rise, which could help inform your future training and education plans.

Summary

The race to build out agentic AI is already underway. Here are some players:

  • Agentic AI (the concept): This broad term refers to AI systems that can independently plan and execute sequences of actions to achieve a goal. For example, a customer service AI may interact with a data-analytics AI and a deliver AI to ensure that a customer’s request for a particular product can be filled in a timely manner.
  • CrewAI: This platform is designed for crafting autonomous AI agents. It integrates with third-party apps, includes dashboards into AI agents’ ROI, and works either locally or in the cloud.
  • AutoGen: Developed by Microsoft, AutoGen is a framework that enables the buildout of multiple agents that can interact to solve tasks. AutoGen’s strength is enabling different AI agents with specialized capabilities to work in concert.
  • UiPath: Framed as agentic AI for the enterprise, UIPath aims to smarten up enterprise workflows and make complex processes more autonomous.
  • Moveworks: This company’s offerings allow workers to build highly customized agents that interact with a company’s worth of plugins and systems.
  • Amazon Q Developer: A different type of “agentic AI,” Q Developer operates within the AWS ecosystem to help developers and engineers carry out a variety of tasks, such as running shell commands and writing files.

According to Cisco, agentic AI could handle 68 percent of all customer service and support interactions for technology vendors by 2028. It’s easy to imagine agentic AI taking more responsibilities in diverse fields such as coding, IT support, and much more. However, these aren't plug-and-play solutions: agentic AI will require skilled humans to design the agents, define their roles and objectives, manage their interactions, troubleshoot their inevitable errors, and critically, integrate them into larger, existing business systems.

“Talent readiness is one of the biggest barriers to scaling and unlocking value for companies,” Jack Azagury, group chief executive for consulting at Accenture, recently told Forbes. “One can invest in all the available Gen AI tools, but if your employees don’t know how or why to use them, the value will simply not be realized.”

You read that right: there’s a continual hunger for AI talent to build out these solutions—the agentic AI future simply can’t exist without you.

Let’s say it’s five years from now. You’re a software engineer, but your boss expects you to become an “AI conductor” or an “AI orchestrator,” whatever the heck that means. You just know you’re responsible for managing the company’s new agentic AI policy.

Here’s what you’ll actually be doing:

  • Strategic Design: You'll be the architect of AI teams, deciding which agents are needed, what their individual capabilities should be, and how they should collaborate.
  • Goal Orchestration: You'll set the overarching objectives for your AI agent crews and ensure their collective efforts align with business goals.
  • Performance Oversight: Monitoring the agents' performance, identifying bottlenecks or areas for improvement, and refining their programming or instructions will be key.
  • Quality Control: Ultimately, you're responsible for the output and actions of the AI agents under your "management."

Just in case you thought the AI was going to handle everything, this sort of work will require tech pros to have a masterful grasp of technical understanding, strategic thinking, and problem-solving.

One of the most significant hurdles for deploying truly autonomous agentic AI is the messy reality of existing IT infrastructure, especially for larger companies that have been running the same tech stack for years with relatively few updates (and let’s not talk about government agencies still running Windows 95).

AI agents need to communicate effectively with websites that change layouts, APIs with their own unique quirks, databases with specific schemas, and often, brittle legacy systems. Yee-hah! This is where human expertise becomes indispensable, and it's reflected in the skyrocketing demand for AI integration and MLOps (Machine Learning Operations) skills. For example:

  • LangChain: This framework is a cornerstone for building applications powered by LLMs. It provides the essential "plumbing" by offering modules that connect LLMs to other data sources (like your company's internal documents or databases) and allow them to interact with their environment (e.g., execute code, query a database, or call an API).
  • MLflow: As agentic AI systems become more sophisticated, the underlying machine learning models they use also grow in complexity. MLflow is an open-source platform for managing the entire machine learning lifecycle.
  • Kubeflow: For enterprise-grade agentic AI that needs to operate at scale, robust deployment infrastructure is non-negotiable. Kubeflow is an open-source MLOps toolkit specifically designed to make deploying complex machine learning workflows on Kubernetes simple, portable, and scalable.

The surge in demand for these tools clearly indicates that companies are realizing they need human experts who understand both the cutting-edge AI technologies and their existing technology stack. These are the professionals who can bridge the gap and make agentic AI actually work in practice.

What’s the best way to meet this AI future? You got to learn the skills. Here’s a potential roadmap:

  1. Foundation: Master Python:
    • Why Python? It remains the undisputed lingua franca of AI and machine learning development, thanks to its readability, extensive libraries (like Pandas for data manipulation, scikit-learn for machine learning), and a massive supportive community.
    • Python for Data Analysis: This specific skill is crucial as you'll need to prepare, understand, and manipulate the data that your AI agents will use and learn from.
    • Certifications: Credentials like Certified Associate Python Programmer (PCAP) can validate your foundational knowledge and provide a structured learning path.
    • Importance: You'll use Python to customize AI frameworks, write scripts that define agent behaviors, handle data inputs and outputs, and integrate different components of your AI systems.
  2. Understanding AI Workflows (MLOps):
    • MLflow & Kubeflow: Before you can effectively orchestrate AI agents, you need to master the art of operationalizing the machine learning models that often empower them. This means understanding how to reliably train, deploy, monitor, and update models in production environments. Experience with these tools demonstrates you can manage the lifecycle of AI systems.
  3. Diving into Agents:
    • CrewAI & AutoGen: Once you have a solid Python foundation and understand MLOps principles, you can dive into the specialized frameworks for building and managing the AI agents themselves. These toolkits are where you'll design agent roles, define their interactions, and build the logic for collaborative task execution.
  4. Platform Knowledge (Deployment & Scaling):
    • AWS Bedrock: This fully managed service from Amazon Web Services provides access to a variety of high-performing foundation models (FMs) from leading AI companies through a single API. It also includes a broad set of tools to build and scale generative AI applications. Familiarity with Bedrock means you can leverage powerful, pre-trained models as the core intelligence for your agents and deploy them in a scalable, managed environment.
    • Azure AI Foundry: Microsoft's comprehensive platform, Azure AI Foundry, offers an integrated environment with tools and services to build, deploy, and manage AI solutions at scale.

With these skills, you're not just a coder; you're a strategist, a problem-solver, and a leader of AI initiatives. When targeting roles, look for titles and descriptions that emphasize:

  • Titles: “AI Orchestrator,” “Multi-Agent Systems Specialist,” “AI Solutions Architect (Agentic Focus),” “Lead AI Engineer (Agentic Systems).”
  • Responsibilities: Designing, implementing, and managing teams of AI agents; integrating AI agents into complex business processes; overseeing AI-driven projects; ensuring AI agent performance, reliability, and ethical behavior.

Emphasize your ability to combine technical proficiency in agent frameworks (CrewAI, AutoGen), MLOps tools (MLflow, Kubeflow), and cloud platforms (AWS Bedrock, Azure AI Studio) with strategic thinking, project management, and a deep understanding of how AI can deliver business value.

The rise of agentic AI isn't a signal that tech pros are going the way of the dinosaur. Instead, it's an invitation to evolve and step into more impactful roles. By embracing these new tools and the challenges they bring, you can become an indispensable AI wrangler.