In this video, I will answer the question ‘What is Agentic AI, and what are AI Agents? Is Agentic AI the future of project management? And will Agentic AI take our jobs?
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What Agentic AI is
Agentic AI systems can act autonomously to achieve specific goals. It does this by breaking a task into specific steps, which it can then follow. Unlike RPA, it can make contextual decisions, without needing the logic to be pre-programmed.
Agentic AI systems can act autonomously, setting and pursuing their own goals. Unlike traditional AI, which follows specified tasks, agentic AI can learn from experience, adapt to changing circumstances, and make decisions independently.
Agentic AI systems can act autonomously, setting and pursuing their own goals. Unlike traditional AI, which is primarily task-oriented, agentic AI can learn from experience, adapt to changing circumstances, and make decisions independently.
It can perform complex tasks, such as operating autonomous vehicles, providing personalized healthcare, and even assisting in scientific research.
How Agentic AI works
Agentic AI operates independently and can offer highly specific solutions, adapting its actions and even specific goals, based on events or changes in data or the situation. It uses Machine Learning to improve its performance and Natural Language Processing (NLP) to discuss tasks and confirm requirements.
The agent can access multiple sources of data, multiple AI and non-AI T tools, and a set of commands, prompts, or goals from human originators. In the goal initialization step, the core AI model responds to the user’s prompt. It may need to first gather information from data sources, which can include the internet.
It uses these to determine a task list of goal-directed actions to take that impact its environment (real or virtual) through Actuators.
It assesses the impact of the actions through one or more sensory tools (Sensors) and uses that data to determine the next interim goals and action steps. Through the cycle, some agents can learn and update themselves.
Users don’t need to constantly send prompts with new instructions. The AI agent will run once you give it an objective or a stimulus to trigger their behavior.
What Agentic AI can be used for
Agentic AI already offers a very wide range of applications that include;
- Software development
- Medical diagnosis and prescribing
- Cybersecurity monitoring and response
- Financial market analysis and trading execution
- Content and Social Media marketing
- Sales lead generation and nurturing
- Autonomous robots and drones
- Customer service and customer support
- Recruitment advertising, application screening, assessments, and process scheduling
- Staff training, support, and coaching
- Supply chain optimization, vendor management, re-ordering, and inventory control
- Personal productivity and meeting support
And…
- Project requirements gathering, user story creation, scheduling and budgeting, resource allocation and management, and stakeholder analysis and communication
Benefits of Agentic AI:
- Efficiency
- Availability (24/7)
- Data-driven decision-making
Agentic AI vs RPA
What’s the difference between an AI Agent and RPA (Robotic Process Automation)?
The main difference between AI agents and Robotic Process Automation (RPA) lies in what they know and how they adapt.
RPA automates repetitive, rule-based tasks using structured data, following predefined scripts. It knows what it has been programmed to know and follows the rules a person has set for it.
AI agents can handle unstructured data, learn, adapt, and make complex decisions. They can therefore handle use cases that have not been considered explicitly in their programming.
Essentially, RPA mimics the human actions that we have given it, while AI agents more closely mimic human thinking and reasoning.
The Future of and Concerns about Agentic AI
The development of agentic AI also raises ethical concerns, including issues of safety, accountability, and potential biases. As this technology continues to advance, it is crucial to address these challenges to ensure its responsible and beneficial integration into society.
Challenges
- Accountability and transparency
- Bias
- Oversight and governance
- Security and privacy
- Control and threat of malfunction or runaway errors compounding
One solution is ‘Human-in-the-loop frameworks’ that can increase oversight over AI agent systems. Here, the agent’s actions pause at pre-determined points in the workflow. The Agent sends a notification to a human user, who must review decisions, information, and scheduled tasks, and then either approve or change how the AI agent will continue the task.
The big question is, ‘Who is to blame if AI makes a mistake?’ This is the challenge that is limiting the adoption of autonomous systems like self-driving vehicles. We don’t have the legal frameworks to assess the balance of culpability between developers, trainers, or operators; or maybe data, software, or hardware vendors, or data, software, or hardware owners.
So, will AI Agents Take our Jobs?
Yes. Without a doubt, Agentic AI will displace jobs and change the way organizations deliver both internal and customer-facing services. Human workers will be replaced by AI agents in many – likely all – industries. It is too soon to know when, how, and to what degree this will happen. But, at the same time, new roles will open up in AI usage, development, and maintenance, along with human-in-the-loop roles.
The future is agentic.
Recommended Videos and Articles to Help with Artificial Intelligence in Project Management
Carefully curated video and article recommendations for you:
- What is Artificial Intelligence (AI)? | Video
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- Artificial Intelligence in Project Management: AI and PM Primer
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- How can AI Transform Project Management?
- How will Artificial Intelligence Impact the PM Profession?
- What is Robotic Process Automation (RPA)? …and Intelligent Automation?
- AI Governance Essentials: A Comprehensive Guide for Enterprises and AI Projects
- How to Write Good AI Prompts: Prompt Engineering
What Kit Does a Project Manager Need?
I asked Project Managers in a couple of forums what material things you need to have, to do your job as a Project Manager. They responded magnificently. I compiled their answers into a Kit list. I added my own.
Check out the Kit a Project Manager needs
Note that the links are affiliated.
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Agentic AI is the new (current) wave that we need to get up to speed on. McKinsey tells us that deploying agentic AI successfully isn’t easy. Here’s what we’re learning about how to get it right. One year of agentic AI: Six lessons from the people doing the work: https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work