The tech world loves its buzzwords, and “AI agent” is the term on everyone’s lips right now. Top tech CEOs claim these tools will join the workforce, replace knowledge workers, and reshape how business gets done. But ask five experts what an AI agent actually is, and you’ll get six different answers.
This lack of clarity isn’t just an academic problem. As businesses rush to adopt “agentic” solutions, the confusion leads to mismatched expectations, wasted investments, and growing frustration.
Why Even AI Experts Can’t Agree
Tech industry veterans from Andreessen Horowitz (a16z) – one of the most active venture capital firms funding AI startups – recently tackled this question on their podcast. Despite their expertise, even they struggled to land on a single definition.
“The simplest thing I’ve heard being called an agent is basically just a clever prompt on top of some kind of knowledge base,” said Guido Appenzeller, an a16z investment partner.
Guido Appenzeller from a16z
Meanwhile, Google’s Ryan Salva has grown to “hate” the term agent because the industry has made it “almost nonsensical” through overuse.
The problem stems from both technical complexity and marketing motives. Companies stretch the definition to fit their products, while others make bold claims about human replacement to justify premium pricing.
What AI Agents Actually Can (and Can’t) Do Today
Cutting through the noise, we can understand AI agents as systems that:
- Use large language models (LLMs) for reasoning
- Make decisions based on input
- Take actions autonomously
- Often use external tools to complete tasks
Yoko Li from a16z put it simply: an agent is “a multi-step LLM chain with a dynamic decision tree.” In plain terms, it’s software that can think through problems step by step and decide what to do next without constant human guidance.
Current examples include:
- Sales agents that can search customer databases, decide who to contact, and draft emails
- Coding agents that write and fix code across multiple files
- Research agents that can gather and summarize information from multiple sources
These tools show real promise, but they fall far short of the human replacement narrative some companies push. The technology faces significant hurdles:
- Persistent memory limitations
- Problems with hallucination (making up facts)
- Difficulty handling complex decision-making
- Security and authentication challenges
The Business Reality Behind the Hype
Many companies market their agent products with claims about replacing human workers and the cost savings this will bring. “We can price the software much higher because this is an agent,” as Appenzeller described the common sales pitch. “You’re replacing a human worker with this agent.”
This pricing model sounds compelling at first glance – pay $30,000 yearly for an agent that replaces a $50,000 employee. But this approach misses a crucial point: complete human replacement rarely happens in practice.
As Matt Bornstein from a16z noted: “From our perch in Silicon Valley, we can forget that most people have jobs that require human creativity and thinking. To replace humans with a bot, I’m just not sure that even is kind of theoretically possible.”
The more likely outcome? Productivity tools that make employees more effective rather than replace them entirely. This mirrors what we’ve seen with other automation technologies throughout history.
How to Think About AI Agents for Your Business
For business leaders trying to make sense of AI agents, consider these practical points:
Focus on tasks, not jobs: Instead of trying to replace entire roles, look for specific, repetitive tasks where agents excel. The best returns come from freeing human workers from dull, time-consuming work.
Watch for technical limitations: Current agents struggle with memory, complex decisions, and handling exceptions. Make sure vendors address how their solutions handle these challenges.
Consider the integration aspect: The hardest part of implementing agents is often connecting them to your existing systems and data. Ask tough questions about API access, authentication, and security.
Be skeptical of pricing based on human replacement: The cost of software tends to move toward its production cost over time, not stay pegged to the human labor it replaces. Look for value-based pricing that makes sense for your specific use case.
The Technical Reality Behind AI Agents
For the more technically minded, understanding what happens under the hood can help cut through marketing claims.
Most current AI agents follow a similar pattern:
- They retrieve context from external databases
- Assemble this information into prompts for an LLM
- Run these prompts to get responses
- Use those responses to decide next steps
- Occasionally call external tools to take action
The core logic is actually fairly lightweight. The expensive part is running the LLM itself, which typically happens on specialized infrastructure. This is why many experts believe agent technology will eventually be priced based on computing resources used rather than the human labor supposedly replaced.
Where AI Agents Are Headed
Despite the confusion and hype, AI agents represent a significant shift in how we work with computers. The technology is developing rapidly, with improvements in multimodal capabilities (understanding images and text together) and reasoning abilities leading to more capable systems.
As one a16z investor suggested, the best outcome might be if we stop using the term “agent” altogether and simply treat these tools as normal technology – like electricity or the internet – that helps us work more effectively.
For businesses today, the smart approach is to look past the buzzwords and focus on specific problems these tools can solve. The companies seeing real ROI from AI aren’t chasing vague promises of “agentic” workflows – they’re finding concrete ways to make their people more productive.
Start small, measure results, and expand based on proven value. In the end, that practical approach will yield far better results than chasing the latest AI buzzword.
Think about where your teams spend time on repetitive, predictable tasks that don’t need human judgment. Those areas offer the best starting points for testing what today’s AI agents can really do for your business.
