Why AI Agents Can’t Finish the Job

Happy Wednesday,

Everyone is talking about "Agentic AI" right now. The demos look incredible: an AI that plans a trip, books the flights, and adds them to your calendar all at once.

But if you look inside most companies today, you’ll find a different reality.

Almost everyone has a pilot running. Almost no one has it in production.

We call this "Pilot Purgatory."

According to McKinsey’s State of AI in 2025 survey, 88% of organizations use AI in at least one business function, and while interest in AI agents is growing, with 39% reporting they have begun experimenting with them, only 23% say they are scaling an agentic AI system somewhere in their enterprise, typically in just one or two functions.

Why is the failure rate so high? It’s not because the models are "dumb." It’s because of a simple mathematical problem that most teams ignore.

1. The "Reliability Math" That Breaks Agents

In a traditional chat (like asking ChatGPT a question), you only need the model to be right once.

But an Agent has to take multiple steps to finish a job. It has to:

1. Understand the user.

2. Search the database.

3. Analyze the data.

4. Decide on an action.

5. Execute the API call.

Here is why that kills performance.

Let’s say you have a great AI model that is 95% accurate. In the world of software, 95% sounds like an A grade. But if your agent needs to take 10 steps to finish a workflow, the math looks like this:

$$0.95^{10} = 59\%$$

Your "A-grade" model now has a 41% failure rate.

If the workflow is complex and takes 20 steps, the success rate drops to 36%.

This is why agents work perfectly in a 3-step Twitter demo but fall apart when you try to use them for a real 20-step enterprise process like "Employee Onboarding."

2. The "Run Cost" Nobody Budgets For

The second reason agents are failing is financial.

With traditional software, you pay a lot to build it, but running it is cheap. AI is the opposite.

A Gartner report predicts that at least 30% of generative AI (GenAI) projects will be abandoned after the proof-of-concept, often due to poor data quality, inadequate risk controls, escalating costs, and unclear business value, reflecting the significant cost and investment challenges organizations face with GenAI.

Unlike standard code, AI models drift. They require constant "re-grounding" (updating their knowledge). Industry estimates now show that the annual maintenance cost for an AI agent is roughly 15% to 25% of the initial build cost.

If you spend $100,000 building an internal agent, you are signing up for a $25,000/year recurring bill just to keep it from breaking, and that doesn't include the token costs, which scale up with every user.

3. The Fix: Build "Narrow" Agents

Does this mean agents are useless? No. It means we are building them wrong.

The companies succeeding in 2026 aren't building "General Employees" (e.g., “An AI that handles all customer support”). They are building "Narrow Agents" that do one thing with very few steps.

• Don't Build: An agent that researches, drafts, and sends cold emails autonomously. (Too many steps; reliability will crash).

• Do Build: An agent that only categorizes incoming emails so a human knows which ones to read first. (1 step; 95% reliability).

Multi Model Comparison

With Geekflare Connect’s Multi-Model Comparison, you can send the same prompt to multiple AI models like GPT-5.2, Claude 4.5, and Gemini 3 at once. Their responses appear side-by-side in a single view, making it easy to compare quality, tone, and accuracy. This helps you quickly decide which model gives the best output for your specific task, without switching tabs or losing context.

You Don’t Actually Own Your AI Setup

Cheers,

Keval, Editor