AGENTIC A.I

Agentic  A.I refers to artificial intelligence systems that can autonomously perceive their environment, reason about goals, plan multi-step action, use tools and execute tasks with minimal human intervention. These systems are designed to operate with a high degree of autonomy, making decisions and taking actions to achieve specific objectives.

There are several types of agentic  A.I and they are; reactive agents, deliberative agents ,single agent system, multi-agent system , learning agents and hybrid agents.

Reactive agentic A.I responds directly to the environment with no planning. They are fast, efficient for simple tasks but with limited adaptability and foresight. They are useful for basic customer service and simple automation. Example includes simple chatbots, reflex agents etc.

Deliberative agentic AI plans and reasons before acting. They are useful in handling complex tasks with foresight but are slower and need more data to operate efficiently. They are used for logistics planning and solving complex problems. Examples include strategic decision making and planning systems etc.

Single agent system are individual AI models with access to tools (web search, code execution, file systems APIs) that autonomously complete multi-step tasks. An example is claude using tools to research, write and send a report in one session.

Multi-agent systems involve multiple specialized AI agents collaborating one agent might plan, another executes, another verifies similar to a team of specialist. Frameworks like autogen, crew AI, and Landgraph are built on this model.

Learning agents improve overtime through reinforcement learning or feedback adapting their strategies based on outcomes.

Hybrid agentic A.I combines reactive and deliberative agentic A.I architecture. They balance speed and planning in their operation. However they are very complex in their operation. They find use in self-driving cars and industrial automation. Examples include autonomous vehicles, advanced robotics etc.

Agentic AI basically works through perception, reasoning, action and learning. It first perceives to understand it environment and data available before reasoning to plan and decide necessary action. Then it acts to execute tasks using the tools available. It also learns and adapts from experience or feedback in its operation.

The advantages of agentic A.I are; it handles complex, multi-step reasoning that RPA cannot.

It adapts dynamically to unexpected situations. It can work across unstructured data (email, PDF, images, voice etc.). It is capable of creative problem solving and decision making. It scales intelligently; it doesn’t require reprogramming for edge cases. It can orchestrate other tools, APIs and even RPA bots.

The disadvantages of agentic  A.I are; less predictable than scripted RPA and its output can vary. It is harder to audit and explain decision (interpretability challenges). It is at risk of hallucination, confident but wrong actions.  It possesses grave security concerns, autonomous agents with broad permissions are a significant attack surface. It requires careful guardrails to prevent unintended or harmful actions. It has higher computational cost compared to simple bots. It is still maturing and its reliability in production environments is not yet fully proven.

Agentic  A.I finds application in the following; software development (autonomous coding assistants such as claude code, github co pilot work space). Customer service (A.I agents that resolve complex cases end to end not just triage). Research and analysis (agents that search, synthesize and produce reports autonomously). Supply chain ( dynamic, rerouting and procurement decisions in response to disruptions). Health care ( clinical decision support that gathers records, analyzes data and proposes treatment paths). Legal ( contract reviews, due diligence, case research across large document sets). Cybersecurity (autonomous threat detection, investigation and response).

The future of agentic AI depends on the advances and development in the following technologies; increased autonomy, human AI collaboration and ethical consideration etc.

 

SOURCES:

• Artificial intelligence: foundations of computational agents by David L. Poole and Alan K Mackworth.
• Human compatible AI by Stuart Russel.
• Multi-agent systems by Michael Wooldridge.
• Reinforcement learning: an introduction by Sutton and Barto.
• Designing autonomous agents by Pattie Macs.
• AI agents in action by Michael Lanham.