Agent is not a chatbot — and companies are paying the price

Most businesses still think of agentic AI as a slightly smarter version of a chatbot. That's a fundamental mistake. While a chatbot waits for your prompt and gives a one-off reply, an agent works completely differently. It monitors system state, makes decisions independently, calls APIs of various tools, browses databases, and repeatedly executes entire chains of tasks. Without human intervention.

This constant activity means that instead of occasional queries, the infrastructure suddenly faces thousands of small, context-heavy interactions across models, tools, and data repositories. And that's something traditional corporate servers and cloud architectures were never built for.

We saw a concrete example this April. GitHub had to pause new user sign-ups for Copilot Pro, Pro+, and Student plans. The reason? Agentic coding modes — long, parallel coding sessions — consumed orders of magnitude more compute power than the original pricing model had accounted for. GitHub simultaneously tightened limits and removed access to Claude Opus models from lower-tier plans, as reported by InfoWorld.

Four weaknesses that agentic AI exposes

According to an analysis by Nutanix, published in Forbes, there are four key areas where infrastructure fails when deploying agents:

1. Tokenomics instead of throughput

Agents behave more like traders than batch scripts. Each interaction consumes a different number of tokens, and without visibility into how many tokens each agent burned, you can't predict costs even at the level of a single workflow. IDC estimates that large enterprises underestimate AI infrastructure costs by an average of 30% — and this will persist until 2027. Most companies measure costs retrospectively, not in real time. The result is uncontrollable cloud bills.

2. Chaos in access management

Large companies don't run a single AI project, but dozens — across departments, regions, and product teams. Each one needs different permissions, works with different data, and competes for the same resources. Without strong tenant isolation and model-level access control, chaos ensues, threatening both security and reliability. According to Gartner, by 2029, roughly 70% of enterprises will deploy agentic AI into IT infrastructure (up from less than 5% in 2025) — but only those that solve access management in time.

3. Data in the wrong place

Agentic systems are extremely sensitive to where data resides. If storage and data pipelines aren't optimized for AI access patterns, expensive GPU units sit idle waiting for data. With growing context windows and chained RAG retrieval, tool calls, and real-time inference, every delay compounds. The result is a slow system that users stop trusting.

4. The operational nightmare the day after

Models change, GPU generations become obsolete, frameworks evolve, and security requirements tighten. If every hardware replacement or model update means a new multi-month engineering project, the IT team spends all its time maintaining infrastructure instead of creating value. Organizations are therefore shifting to a software-defined, cloud-based operating model that enables seamless updates without outages.

What to do about it? Four principles for survival

Companies serious about agentic AI need, according to analysts, to establish four foundational building blocks:

Central control plane — a single place for managing models, routing, budgets, monitoring, and enforcing policies across all teams.

Infrastructure without vendor lock-in — the ability to run AI workloads across different hardware and environments without dependency on a single supplier.

AI-native data services — storage, caching, vector pipelines, and high-throughput data paths without which agents cannot function effectively.

Economics and governance at scale — quotas, cost chargeback to departments, access control, and self-service portals that enable broad AI adoption without losing control.

What this means for Czech companies

For Czech businesses, this warning is especially timely. While large corporations like Škoda Auto or banking houses are already experimenting with agentic AI, most domestic companies are still in the first-chatbot phase. According to a ChannelTrends survey, Czech entrepreneurs see AI primarily as a tool for making email communication more efficient — they're still far from autonomous agents managing entire processes.

But that doesn't mean they can afford to underestimate infrastructure. On the contrary — Czech companies have a unique opportunity to learn from the mistakes of their Western counterparts and build AI architecture correctly from the start. Betting on cloud solutions from European providers (which meet GDPR and EU AI Act requirements) makes sense not only technically, but also regulatorily.

In the European context, moreover, the EU AI Act requires transparency and control over AI systems, turning a central governance platform for managing agents from an optional perk into a regulatory necessity. Companies that invest in the right infrastructure today will save twice over — on fines for non-compliance and on the cost of rebuilding the system two years from now.

The most common mistake: agents as projects, not a platform

The biggest killer of agentic initiatives is the "one project, one team, one technology" approach. Companies fund individual use cases, buy models, launch a pilot. At first it looks promising — but after a few months, marketing runs on a different model than development, each department has its own security policy, and three different tools are in use. IT then fights uncontrolled chaos, finance deals with unexpected bills, and security tries to patch holes that appear faster than they can fix them.

The solution is to stop seeing agentic AI as a set of projects and start building it as an enterprise platform — with unified management, predictable costs, and security built in from day one.