AI Agents: The Year That Changed Everything

If 2023 was the year of chatbots, 2024 was the year of AI agents.

That's not a distinction without a difference.

A chatbot responds to a user's prompt. An agent decides what actions to take, takes them, observes the results, and adapts.

The shift from chatbot to agent is the shift from reactive to proactive. From responding to commanding.

In December, I'm looking back at a year that fundamentally changed what's possible with AI in enterprise software.

The Breakthrough Moments

There were four key moments this year that made agents real:

First, Claude's computer use (November). Claude can now navigate interfaces, click buttons, fill forms, observe the result. For the first time, an LLM can execute actions in real systems without custom integrations.

Second, OpenAI's o1 (December). A model that can plan and reason over many steps. Complex problems that would have required a coordinated multi-step agent can now be handled by a single model.

Third, AutoGPT and ReAct maturity. The frameworks for building agents stabilized. Patterns emerged. Agents stopped being experimental and started being implementable.

Fourth, Integration APIs improving. Easier to connect agents to actual business systems. Zapier, Slack, ERPs, databases. The infrastructure for agents to operate on real systems got better.

What an Agent Actually Is

Let me define this precisely because I think a lot of people are confused.

A chatbot: user → LLM → response.

An agent: user requests → plan → execute steps → observe results → adapt → repeat until done.

The difference: the agent doesn't just respond. It decides. It acts. It checks whether its action worked. It corrects course.

Here's an example from our work in December:

User: "I need a purchase order for 500 widgets from Supplier X, but only if the unit cost is lower than our current supplier."

Chatbot: "I can help you create a purchase order. Here are the steps you'd follow..."

Agent: goes to ERP → looks up current supplier for widgets → gets their price → searches supplier database → finds Supplier X → gets their price → compares → if favorable, creates draft PO → presents it to user → waits for approval → submits when approved.

The agent didn't just tell the user what to do. It did most of it.

Why 2024 Was Different

Agents have been theoretically possible for years. But three things were missing:

First, reliability: older agents would get stuck, loop indefinitely, or take wrong branches. Claude's computer use and o1's reasoning fixed this. Agents can now navigate through complex scenarios without getting lost.

Second, integration capability: agents needed custom code to interface with every system. Claude's computer use is different. It can use any system that has a visual interface. You don't need to build API integrations.

Third, user trust: before 2024, the results were unpredictable. An agent might hallucinate, take the wrong action, or delete data. Now agents are stable enough that you trust them with real operations.

What We're Building

In December, we started building an agent for ERP approvals.

Here's the scenario: a purchase requisition comes in. It needs approval. The approval depends on multiple factors: is this vendor approved? does the budget allow it? is the quantity reasonable for this time period? what's the business justification?

Traditional ERP approach: you route the requisition through a workflow. A manager sees it. They check supplier database. They check budget. They make a decision.

Agent approach: submit the requisition → agent gathers all relevant information → agent evaluates approval criteria → agent makes recommendation with reasoning → human approves.

We built this using Claude's computer use. The agent can navigate the ERP interface just like a human would.

The breakthrough: we didn't have to build any custom API integrations. The agent uses the web interface exactly like a user would. It works with any ERP that has a UI.

The Economics

This is the thing that's not obvious: agents change the economics of enterprise software dramatically.

Right now, every integration is custom. You want Salesforce talking to NetSuite? You build an integration. Zapier can automate some of this, but Zapier is limited.

With agents, you don't build integrations. You let the agent use the UI.

This is revolutionary for mid-market and lower companies that can't afford custom integrations.

A manufacturing company with SAP and a custom inventory system: they can't afford custom integration work. But they can afford to let an agent coordinate between them.

The Risks We Talk About Less

I want to be honest about what worries me.

Agents operating on real systems without humans in the loop is powerful but dangerous.

An agent can:

• Accidentally create duplicate records.
• Delete data it shouldn't.
• Execute transactions that shouldn't have been executed.
• Propagate errors across multiple systems.

We build agents with guardrails:

First, approval gates: certain actions require human confirmation before execution.

Second, audit trails: everything the agent does is logged. We can trace exactly what it did and why.

Third, rollback capability: if the agent made a mistake, we can undo it.

Fourth, sandbox testing: agents practice on test data before operating on production.

But here's the honest truth: we don't have perfect solutions for all of these yet.

An agent is more powerful than automation, which means the potential for harm is higher. We're still learning best practices.

What's Coming in 2025

In January, we're shipping the agent framework to select clients. It's not public yet because we want to understand failure modes better.

We're also investing in "agent monitoring and oversight." Building tools to watch what agents are doing, catch mistakes early, and alert humans.

The pattern will be: agents do 80% of the work automatically. They escalate the 20% that requires judgment to humans. Humans review and approve. Agents execute the approved actions.

That's the balanced approach.

The Market Shift

Every enterprise software vendor is now either building agents or buying companies that build them.

Salesforce is adding agents. HubSpot is adding agents. The ERP vendors are working on it.

The companies that move first with agentic capability will have a substantial advantage. Customers will demand it.

By end of 2025, "AI agents" will be a table-stakes requirement, not a differentiator.

My Take on 2024

2024 was the year AI moved from doing work for humans to doing work with humans.

The chatbots of 2023 were consultants. You asked them questions and they gave you answers. Helpful but passive.

The agents of 2024 are more like employees. You give them objectives and they work toward them. They check in when they hit roadblocks. They execute when they have guidance.

It's a different relationship.

Looking Forward

For the next five years, I think enterprise software is going to undergo radical restructuring around agentic AI.

The companies that built themselves around human-driven workflows (lots of approvals, lots of steps, lots of waiting) will have to adapt. Because an agent-driven workflow is faster and more efficient.

The companies that figure out how to combine human judgment with agentic execution will win.

Arvension is betting on this shift. We're building the infrastructure for agents in enterprise systems. By the time agents become standard, we want to be the company that's already solved the hard problems.

2025 is going to be the year of agent implementation. Hundreds of companies will build agentic capabilities into their products.

The year after that, agents will be expected. By 2027, an enterprise software product without agents will seem anachronistic.

2024 was the inflection point. The year everything changed. And we're only at the beginning of what's possible.