Agentic AI — A Quick Overview
Agentic AI marks a shift from reactive AI systems to autonomous, goal-driven agents that can plan, decide, and act with minimal human input.
What Makes Agentic AI Different?
Autonomous: Operates independently, often 24/7
Proactive: Sets and pursues goals instead of waiting for prompts
Context-Aware: Reasons across complex situations and remembers past actions
Tool-Enabled: Interacts directly with APIs, software, and databases
Self-Improving: Learns from outcomes using feedback loops
How It Works — The PRAL Loop
Perceive: Collects data from users, systems, or environments
Reason: Plans and decides using LLM-powered reasoning
Act: Executes tasks via tools and integrations
Learn: Evaluates results and improves future behavior
Agentic AI vs. Traditional GenAI
Traditional GenAIAgentic AIReactiveProactiveContent-focusedAction-focusedLow autonomyHigh autonomyHuman-in-the-loopMinimal oversight
Example:
GenAI drafts an email → Agentic AI drafts, sends, and tracks it
Key Use Cases
Customer Support: End-to-end issue resolution
Finance: Fraud detection, risk analysis, trading
Software Development: Write, test, and deploy code
Healthcare: Personalized treatment insights
Supply Chain: Real-time logistics optimization
Risks & Challenges
Accountability and safety concerns
Reward hacking and unintended behavior
Security vulnerabilities
Acting on hallucinated or incorrect data
What’s Next?
Agentic AI is moving toward multi-agent systems, where specialized agents collaborate like human teams. By 2029, Gartner predicts 80% of customer service issues will be handled by agentic AI.