Enterprise AI Agents: A No-BS primer on the state of affairs

Deb RoyChowdhury

Deb RoyChowdhury

Head of Product, CoAgent Labs

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Introduction

Generative Ai, Large Language Models, Ai Agents or Agentic Ai - are these vaguely defined technolgy constructs based on prior vaguely defined concepts such as artificial intelligence?

Or the most groundbreaking technlogy innovation of our lifetimes which can lead humanity to hyper automation, cure for chronic deseases and better outcomes; and change the way we work?

No matter what people believe, building Agentic Ai with Large Language Models and other Generative Ai models is top of mind for every technology leader across businesses of all shapes and sizes.

And there are many opinions and perspectives out there. For example here is an interesting take from Malcolm Gladwell on fully autonomous cars or robo taxis, another form of complete automation:

If you work with an data at all, it’s unlikely, if not impossible that you have not heard about agents. Not the ones in secret intelligence agencies like James Bond.

Generative artificial intelligence agents (conveniently form the acronym gaia which may explain the religious behaviour around Ai) are a combination of a large language model API call with access to contextual information including tools like a headless web browser, application servers to access various software as a service over model context protocol and similar patterns.

So what? How might you and I cut through the noise and figure out what is relevant in context to move the needle for the business?

What must be true for agentic workflows with ai agents and large language models to deliver real value to the business?

Here are the insights from dozens of conversations with CTOs, AI Engineers, Architects, ML Leads.

Trust but verify

Sounds basic. But I need this reminder often. It’s easy to get swayed by shiny objects as builders. It’s a natural reflex for us. Then there is also pressure to perform. From our own ambition and drive, as well as the business.

“There’s a land grab going on. I’m frustrated that I’m not grabbing more of the land in the early stages. How do I separate if speed is the new moat? How am I separating myself from everybody who’s chasing me?” - Anonymous CTO

Enterprise investment in agentic automation is set to soar from $5.3 billion in 2023 to a staggering $236 billion by 2034.

For technology leaders, the message is clear. Adapt now, or risk being left behind as your competitors automate, accelerate, and out-innovate you.

“The current AI boom is absolutely FOMO-driven, and it will calm down when the technology becomes more normalized,” - Marina Danilevsky: Senior Research Scientist, Language Technologies - IBM

When the entire ecosystem is moving so rapidly, it’s likely that there would be suboptimal decisions. To mitigate that risk we need to to look at first principles and things that don’t change.

Foundational AI models have leveled the playing field. Building stuff is extremely easy. Building and distributing products which delight customers remains hard. Garbage data still produce garbage results. And no matter how cheap it is to store and compute data it is not free.

“Most AI products are thin veneers on top of an LLM, right? There’s not real IP or uniqueness to it.” - CTO

We know that trust captial in the market is at an all time low. It’s for a reason. All the hype does not help. And the fake it till you make it mantra does not work. Even enterprise initiatives fail at a significant rate.

“Honestly, it’s kind of smoke and mirrors. It solves a real pain, but it hasn’t solved anybody’s real pain yet. But it has the potential to do it.” - CTO

Why Are AI Agents Taking Over?

  • Unstoppable Demand for Automation: 96% of enterprises plan to ramp up AI agent adoption in the next 12 months.
  • Beyond Chatbots: Agentic AI promise to orchestrate complex, cross-functional workflows connecting sales, support, product, and IT like never before.
  • Proven Productivity Gains: Companies like ABN AMRO and Klarna are seeing efficiency jumps of 15% to 80% after deploying agentic workflows.

Rapidly Transforming Verticals

Healthcare: Clinical Workflow Transformation - Healthcare represents one of the most rapidly adopting verticals, driven by operational pressures and clear value propositions. AI agents are being deployed across clinical workflows, patient scheduling, and medical coding applications. The sector benefits from 24/7 intelligent automation capabilities that reduce administrative burden while improving patient support quality.

Healthcare implementations focus on reducing pressure on clinical staff through automated documentation and decision support systems. Medical facilities report that AI agents can scan patient data, summarize medical files, and provide real-time clinical decision support, allowing physicians to focus on direct patient care rather than administrative tasks. (Denser)

Financial Services: Compliance and Real-Time Decision Making - Financial services adoption is projected to reach 25% by 2025 and 50% by 2027, driven by regulatory compliance requirements and real-time decision-making needs. Use cases span fraud detection, portfolio management, and automated compliance monitoring.

Agentic AI systems in finance demonstrate superior capabilities in real-time transaction analysis, processing millions of transactions in seconds to identify anomalies while minimizing false positives. Virtual advisors powered by agentic AI monitor market conditions continuously to recommend portfolio adjustments proactively, representing a shift from reactive to predictive financial management. (BAI)

Retail and E-Commerce: Unified Commerce Operations - The retail vertical AI market is projected to grow from $5.1 billion in 2024 to $47 billion by 2030, with unified commerce emerging as a primary driver. Implementations focus on inventory optimization, personalized customer experiences, and automated customer service.

Retail applications demonstrate particular strength in real-time decision-making for personalized user experiences and supply chain optimization. Companies report 30-47% efficiency improvements through AI agent deployment in manufacturing and supply chain operations. (Ciklum)

Manufacturing: Predictive Operations - Manufacturing implementations center on supply chain optimization and predictive maintenance, with AI agents demonstrating 47% reductions in downtime compared to traditional workflow automation. These systems excel at autonomous problem-solving and real-time adaptation to changing operational conditions. (Wrightymedia)

Pack Leaders

Agentic AI blockers?

“If you’re using traditional data lakes, you’re going to have latency issues… they’re not built for efficient queries… no special indices built for users as an example versus revenue.” - AI Engineer

Orchestration Overload: Multi-agent systems are tough to coordinate—communication overhead can drain up to 60% of your compute budget.

Security Shortfalls: Security concerns represent the primary barrier to enterprise AI agent adoption, cited by 53-62% of organizations as their top challenge. Current identity and access management platforms assume cloud-connected, web-based applications, but AI agents operate across hybrid environments including air-gapped systems, edge deployments, and disconnected networks. Architecture & Governance Mag

The security challenge is compounded by the autonomous nature of AI agents, which can make independent decisions to call APIs, access private data, or trigger external actions in ways not explicitly programmed. Traditional security frameworks designed for predictable applications prove inadequate for systems with dynamic, AI-driven behavior patterns. Mindgard

“These agents, they really thrive in the Proof of Concept phase. But then when you scale them up, the edge cases grow astronomically.”

Integration Headaches: Agents need to connect to an average of 8+ data sources. Legacy systems and missing semantic context create friction at every turn.

“The first 75% of your time in the first 90 days is gluing tools together, debugging schemas… after that, that probably goes away if you got it right. The challenge is… you don’t get it right. So then you have to constantly go in and maintain it. The pain you live every day. Number one, feels temporal. It feels like that is true very early on, and it remains true longer and longer if you don’t get it right.” - AI Engineer

Observability Blind Spots: Traditional monitoring tools can’t track autonomous agent decisions or provide the audit trails needed for compliance. Towards Data Science

“Errors can be silent. That ties into observability. As I do want to see the intermediate steps.” - AI Engineer

Where Are the Biggest Opportunities?

  • Orchestration: The winners will be those who can solve agent coordination, semantic understanding, and security.
  • Vertical Solutions: Platforms like AgentFlow (finance) and healthcare-focused agentic systems are outpacing generic tools in regulated markets.
  • Observability & Governance: Deep monitoring, cost control, and airtight compliance for agent workflows are the hardest problems and big opportunities.

The Mandate

  • Scale Beyond Experiments: It’s time to move from scattered POCs to enterprise-wide, cross-functional agent deployments.
  • Make Security & Observability Non-Negotiable: Don’t let agents become a black box. Demand audit trails, cost controls, and robust access management from your vendors.
  • Insist on Semantic Intelligence: Agents must understand your business context—not just your data. Choose systems with semantic schema enforcement.

The Bottom Line

AI agents are no longer a futuristic experiment—they’re normative in an enterprise roadmap.
We need to have a balanced perspective and operate .

Ready to lead the AI agent revolution? The time to act is now.

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