In recent years, we’ve seen major leaps in artificial intelligence—from chatbots that answer questions to models that generate art and text. But now, one of the most exciting (and disruptive) developments is what’s called agentic AI:


What is Agentic AI?

Agentic AI refers to systems that go beyond simply answering or assisting; they initiate actions, make decisions, coordinate across tools, and manage workflows autonomously. Rather than a human asking step‑by‑step, the AI behaves like a teammate.
For example:

• An AI agent that not only drafts emails, but actually sends them, schedules follow‑ups, and handles responses.

• A system that monitors supply‑chain indicators, triggers orders, re‑allocates resources, reports anomalies and optimises logistics—all without human micro‑management.
  This kind of capability is more advanced than earlier ‘assistive’ AI tools, and is now gaining traction.

Why Now? The Drivers

Several factors are converging to make agentic AI viable and important:

1. Maturing AI capabilities – Improvements in large language models, computer vision, multi‑agent frameworks and integration across tools make more complex tasks possible.

2. Data & connectivity – With high volumes of structured & unstructured data, strong networks (5G/edge), and APIs everywhere, AI agents have richer inputs and better access to systems.

3. Business demand for automation – Organisations seek to streamline workflows, reduce human overhead, improve speed & reliability. Agentic AI promises that.

4. Cloud & edge infrastructure – More compute, cheaper storage, distributed architectures allow AI agents to be deployed at scale.

Where Agentic AI Is Already Making Impact

Here are some practical use‑cases where agentic AI is gaining traction:

• Customer service & operations: AI agents can kick off a service request, interact with backend systems, escalate if needed, follow up—all without full human handover.

• Supply‑chain & logistics: Monitoring inventory, detecting delays, rerouting shipments, negotiating with providers—an agent handles many moving parts.

• Enterprise productivity: Scheduling meetings, aggregating reports, managing tasks across teams, reminding stakeholders, adjusting timelines.

• Specialised domains: In finance, healthcare or legal, agents can monitor for compliance, flag anomalies, prepare documentation, recommend actions.
  In India specifically, for example, a report predicts that agentic AI could reshape over 10 million jobs by 2030.

Key Benefits

• Efficiency & speed: Fewer bottlenecks, quicker decision loops.

• Consistency & scalability: Agents can scale tasks across volumes humans cannot manage easily.

• Cost‑effectiveness: Over time, automation of routine decisions reduces overhead.

• New business models: Agents enable services that were previously too complex or expensive to deliver.