Hot Take: How NOT to Use Agentic AI: A Playbook for Businesses

Everywhere you look right now — from pitch decks boasting trillion-parameter models to breathless LinkedIn posts about “AI employees” — agentic AI is the Next Big Thing.

What We Talk About When We Talk About Agentic AI

We’re talking about AI systems that don’t just predict or classify but act. They plan to execute tasks across multiple systems, interact with APIs, and maybe even spin up their temporary code environments to achieve a goal with minimal human hand-holding.

Think Auto-GPT on enterprise steroids, with specialised agents tackling everything from customer service triage to complex data analysis and code generation. The promise? Unprecedented automation and hyper-efficiency may finally kill off that soul-crushing backlog of Jira tickets.

My hot take? Hold your horses, and check your API keys. While the long-term potential is undeniable, the current rush to deploy agentic AI in complex business environments feels like handing a toddler a running chainsaw. Most companies are unprepared and set themselves up for a spectacular bonfire of wasted resources, operational chaos, and potentially terrifying unintended consequences.

Forget the utopian demos; let’s discuss the playbook for not using this powerful, barely adolescent technology.

The Agentic AI Hype Train: Full Steam Ahead?

First, let’s level-set. Agentic AI isn’t just a souped-up chatbot. It leverages LLMs for reasoning and planning but crucially integrates execution capabilities. It can interact with your CRM, query databases, draft and send emails, and modify code. The level of autonomy is the differentiator.

And the momentum is nuts. Gartner predicts that over 80% of enterprises will be using GenAI APIs or deploying GenAI apps by 2026, up from practically nil just a year or two ago. Agentic capabilities are the bleeding edge of this wave. But bleeding edge often means, well, bleeding.

Magic Wands Need Real Wizards (Not Just Prompts)

The most common face-plant I see coming is the “Point-and-Pray” deployment. This magical thinking pervades strategy meetings: aim your shiny new AI agent at a gnarly business problem like “optimise logistics” or “personalise customer journeys at scale,” feed it some corporate buzzwords, and watch the ROI skyrocket.

It fundamentally misunderstands these systems. LLM-based agents are brilliant pattern matches and, let’s be honest, sophisticated con artists. They’re prone to hallucination, getting stuck in repetitive loops (hello, infinite API calls!), and spectacularly misinterpreting ambiguity. They can lack real-world understanding and common sense.

Expecting them to intuit years of embedded business logic, navigate undocumented dependencies in your legacy systems, or understand subtle but critical operational constraints without painstaking setup, grounding in accurate data, and clearly defined guardrails is pure fantasy. It’s like expecting a Large Hadron Collider to assemble IKEA furniture: the wrong tool and context.

Flying Blind: The Peril of Goal-Free AI

It leads directly to pitfall number two: vapour goals and metric-free mayhem. I’ve lost count of the proposed deployments where the “objective” is something vague like “increase efficiency” or “leverage AI.” These aren’t goals; they’re wishlist items.

Deploying an autonomous system without crystal-clear, measurable objectives (KPIs, people!) is like launching a rocket without coordinates. How do you define success? Or failure? If you ask your autonomous procurement agent to “cut costs,” does it understand that switching to a dodgy supplier who violates labour laws is not the desired outcome?

Without explicit constraints and metrics (e.g., “Reduce cost for X by Y% while maintaining quality score Z”), the agent optimises in a vacuum, potentially triggering catastrophic downstream effects. You need a dashboard with dials before you hit launch, not after the crater forms.

Set It and Regret It: Autonomy Needs Adult Supervision

The siren song of “set it and forget it” is strong with agentic AI. The dream is to deploy these digital workers and watch them hum along, freeing up human brains for higher pursuits. Reality check: these systems are not static.

Data drifts, APIs change, business needs pivot, and the models themselves can degrade. Critically, the agent’s actions change its operating environment, creating feedback loops nobody predicted. Lack of rigorous monitoring, continuous evaluation, and, crucially, Human-in-the-Loop (HITL) intervention points for complex or risky decisions isn’t just laziness; it’s negligent.

Think of it like onboarding a super-smart, highly volatile intern. You need regular check-ins and the ability to grab the steering wheel. McKinsey studies consistently show AI success correlates strongly with robust governance — agentic AI demands on overdrive.

Integration Hell: Bolting Jets onto Bicycles

Here’s a dirty secret: your fancy AI agent is useless if it can’t talk to your existing mess of systems. Many companies are trying to slap these advanced agents onto brittle legacy platforms held together by digital duct tape and prayer.

It is a recipe for latency nightmares, data sync failures, security holes bigger than your last funding round, and monumental technical debt. Successfully leveraging agents often requires mature APIs, a decent microservices architecture (where it makes sense), clean data pipelines, and actual enterprise architecture foresight.

Trying to overlay agentic intelligence onto a crumbling foundation won’t fix the cracks; it’ll just make the eventual collapse more spectacular. Modernising the plumbing is often the unsexy but essential prerequisite.

Your Data, Their Playground: Security is Non-Negotiable

To do their jobs, agents often need the keys to the kingdom — broad access to customer data, financial records, proprietary code, and sensitive internal systems. Granting this without Fort Knox-level security, granular access controls (least privilege, continually!), intelligent data handling (anonymisation!), and ironclad governance (GDPR says hi!) is asking for trouble.

Imagine a compromised agent autonomously emailing your customer database to a competitor or executing trades based on faulty logic. The attack surface expands exponentially.

Security and ethical AI (bias detection, fairness, transparency) aren’t features; they’re table stakes. Remember IBM’s $4.45 million average cost per data breach? That number gets scarier with autonomous agents in the mix.

Automating for Automation’s Sake? Find Your ‘Why’

Just because you can automate a task with an agent doesn’t mean you should. High-empathy customer interactions? Complex ethical judgments? Strategic negotiations? They probably still need a human touch, maybe with AI assistance.

Throwing agents at the wrong problems — automating things that require deep human connection or nuanced intuition — can backfire spectacularly, torching customer loyalty and employee morale. The critical first question isn’t “What can AI do?” but “What’s the actual business problem, and how can technology, potentially including an agent, provide genuine value?”

It has to be strategy-driven, not a tech demo chasing validation. Augment humans, solve actual pain points, don’t just engage in “AI washing.”

Beyond the Demo: The Reality Check

The potential for agentic AI is real. I’ve seen glimpses of game-changing capabilities. But the chasm between a controlled demo and the chaotic reality of enterprise deployment is vast and deep. It’s littered with the corpses of poorly planned implementations.

Remember this playbook of pitfalls as the hype cycle churns and companies rush to deploy their autonomous digital workforce. The real work isn’t just building the agents; it’s building organisational maturity, technical scaffolding, and strategic discipline to use wisely. Without that, we’re not architecting the future of work; we’re just coding more elaborate ways to shoot ourselves in the foot.

Continue the conversation with me at Arvind@am-pmassociates.com.