What Is Agentic AI?

Agentic AI refers to systems that do more than react—they plan, reason, and act autonomously to meet goals. Unlike generative chatbots or simple automation, agentic systems:

• Ingest complex inputs

• Break down objectives into tasks

• Select tools or APIs

• Execute multi-step workflows

• Adapt based on feedback
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IBM describes it as combining initiative and adaptability through distributed agents and orchestration layers.

🔥 Why the Buzz Now?

1. Industry Momentum & High-profile Activity

Capgemini’s $3.3 b acquisition of WNS emphasizes agentic AI’s business potential in automating complex BPS processes. WNS stock jumped 14% after the news. Financial Times

Anthropic’s Claudius experiment—where an AI ran a vending machine—highlighted the challenges of giving designers true autonomy: it failed on pricing logic and invented behaviors. Financial Times

2. Research Breakthroughs

MIT Sloan is exploring agentic AI’s role in negotiation and human-agent “personality pairing.” MIT Sloan

A recent arXiv paper examines autonomy and accountability challenges, noting that distributed decision-making creates unclear liability zones. arXiv

Reports show agentic systems can boost productivity and innovation—but only when integrated into workflows with proper governances. The Australian

What Agentic AI Actually Does

Key use cases spanning multiple industries:

• Customer service: Beyond scripted FAQs, agents can analyze context and resolve multi-step issues (e.g., refunds) end-to-end. Gartner expects 80% of standard inquiries to be agent-handled by 2029. Converge Technology Solutions

• IT/Ops/Dev: Generating, testing, deploying code; orchestrating CI/CD pipelines; auto-remediation.
  
  

• Cybersecurity: Real-time threat detection, triage, automated patching based on anomaly detection.
  
  

• Healthcare & Finance: Continuous patient monitoring and treatment adjustments; risk analytics and adaptive compliance in finance.
  
  

Real-World Challenges

Autonomous Doesn’t Mean Perfect

The vending-machine model “Claudius” priced irrationally, spoke falsely, and ultimately lost money. Current agents are often unreliable without human oversight. Financial Times
Even major players admit true utility is still emerging.

The Governance Gap

ArXiv warns of “moral crumple zones”—omni-autonomy leads to diffused accountability. arXiv

Enterprise readiness demands:

• Audit trails and traceability

• Regular pausing for human review

• Clear governance and security layers UiPath

What Cloud and AI Teams Should Do

1. Assess readiness
   Identify workflows that truly benefit from proactive AI: code orchestration, multi-step ops, automated remediations.

2. Pilot small, safe projects
   Start with sandboxed internal tasks before scaling to production use cases.

3. Build orchestration infrastructure
   Deploy multi-agent frameworks with planning, memory, tool access, monitoring, and auditability. McKinsey & Company

4. Layer in governance
   Require human-in-the-loop checkpoints, transparent logs, and risk controls from Day One.

5. Stay Vendor-agnostic
   Mix proprietary and open-source agents—avoid lock-in. McKinsey & Company

Expert Clarity

“Agentic AI is the shift from reactive tools to proactive collaborators.”
— McKinsey: This agentic mesh automates vertical workflows, not just horizontal tasks.
McKinsey & Company

“Our AI was rational, but not rational enough—anthropic’s Claudius failed basic economics.”
— Summary of FT experiment with vending-machine agent
Financial Times

Final Takeaway

Agentic AI marks a turning point—from asking questions to taking action. It promises automation that’s smart, strategic, and self-directed.

But it's not plug-and-play. Success requires cautious orchestration, oversight, and architecture built for real-world autonomy.

If you’re a cloud or AI leader, now’s the time to explore small pilots and build foundations for a future where AI partners—not tools—drive critical workflows.