From Chats to Agentic AI: Understanding the Shift

The Starting Point: Chats

Think about the first time you used ChatGPT or a similar tool. You typed something, it responded. You asked a follow-up, it answered. This back-and-forth is what we call a "chat" interface with AI.

Here's what was actually happening under the hood: you sent text to a large language model, it predicted what words should come next based on patterns it learned from enormous amounts of text, and it sent that prediction back to you. The model had no memory beyond your current conversation. It couldn't check the weather. It couldn't send an email. It couldn't look anything up online. It was, in essence, a very sophisticated autocomplete system with a conversation wrapper around it.

The analogy I find useful: imagine you're texting with someone who has memorized every book, article, and forum post ever written, but who is locked in a room with no phone, no internet, and no way to interact with the outside world. They can tell you what they remember. They can reason through problems using what they know. They can write, explain, and even code. But the moment you need them to actually do something in the real world—check a fact, look up current information, run a program—they're stuck.

This was the state of consumer AI until roughly 2023-2024.

The First Evolution: AI Agents

The word "agent" in AI circles means something specific: a system that can take actions, not just generate text.

The shift from chat to agent happened when developers started giving language models access to tools. Instead of just answering "here's how you could book a flight," an AI agent could actually access a booking system and make the reservation. Instead of writing code and showing it to you, it could run that code and show you the results. Instead of telling you what the weather might be, it could check a weather API and give you the actual forecast.

Let me extend the earlier analogy. That person locked in the room now has a phone with specific apps installed. They can make calls, check websites, run programs, and interact with external systems. They're no longer just responding from memory—they're taking actions that affect the real world.

The architecture looked something like this: you would give the AI a request, the AI would decide which tools it needed to use, it would call those tools (search the web, execute code, query a database), receive the results, and then formulate a response that incorporated what it learned. The AI became a coordinator between you and various capabilities.

This was a meaningful upgrade, but it came with limitations. Most early agents were reactive rather than proactive. They waited for your instruction, executed a single task or short sequence, and reported back. They struggled with complex multi-step plans. They often got confused when a tool didn't work as expected. They needed you to hold their hand through anything complicated.

Think of early AI agents like a new employee on their first day. Yes, they can use the computer and the phone and the coffee machine. But they need constant supervision. They complete one task and wait for the next instruction. They don't see the bigger picture of what needs to happen. If something goes wrong, they freeze and ask for help.

The Current State: Agentic AI

"Agentic AI" describes systems that can pursue goals across extended sequences of actions with significant autonomy. The key word is autonomy: these systems don't just execute individual tool calls, they can plan, adapt, and persist toward objectives over time.

Here's what distinguishes agentic AI from earlier agents:

1. Planning and decomposition. When you give an agentic system a complex goal, it can break that goal into subtasks, figure out the dependencies between them, and work through them in a sensible order. If you ask it to research a topic and write a report, it doesn't just do a single search and start writing. It might identify what questions need answering, search for information on each, synthesize what it finds, identify gaps in its knowledge, do additional research to fill those gaps, and then compose the report.
2. Persistence and error recovery. When something goes wrong, like a website is down, a tool returns unexpected results, or a subtask fails; agentic systems can recognize the problem, try alternative approaches, and continue working toward the goal. They don't simply stop and report failure.
3. Multi-step reasoning with world interaction. Earlier agents could reason or act, but struggled to deeply interleave the two. Agentic systems reason, act, observe the results, update their understanding, reason again, and continue. Each action informs the next decision.
4. Working memory and context management. Complex tasks require tracking lots of information across many steps. Agentic systems have developed better mechanisms for managing this state, such as remembering what they've already tried, what they've learned, and what still needs to happen.

Let me push the analogy further. That new employee has now been working for six months. They understand the organization. When you give them a project, they can plan their approach, navigate obstacles, coordinate with other systems and people, and deliver results without requiring your involvement at every step. You can give them an objective and trust them to figure out how to achieve it.

This is the transition the AI field has been working through over the past year or two, and it's why you're seeing AI systems that can browse the web for extended periods, write and debug complex software projects, conduct research across many sources, and handle multi-hour tasks with minimal human intervention.

Why This Evolution Happened

Several things came together to enable this shift.

The underlying models got smarter. Language models improved at reasoning, at following complex instructions, and at handling longer contexts. A model that forgets what it was doing after a few steps can't be agentic. A model that struggles to follow multi-part instructions can't plan effectively.

Tool integration became more sophisticated. Early tool use was limited; rigid formats, limited error handling, narrow capabilities. The infrastructure for giving AI systems reliable access to external capabilities matured considerably.

Developers learned what worked. There's now substantial practical knowledge about how to structure agentic systems, how to handle failure modes, how to balance autonomy with reliability. Much of this came from trial and error over the past few years.

And critically, people started building scaffolding around the models; code that manages the loop of thinking, acting, and observing, that tracks state across many steps, that handles the messy realities of interacting with real-world systems.

Why This Matters in 2026

Here's why you should care about understanding this evolution right now.

The tools you'll use are changing. The software you interact with is increasingly powered by agentic AI. When you use a coding assistant that can run your code, debug errors, and refactor entire projects, you're using an agentic system. When you use a research tool that can spend an hour investigating a question across dozens of sources, you're using an agentic system. Understanding what these systems can and can't do helps you use them effectively.

The jobs landscape is shifting. Many tasks that previously required human judgment and multi-step execution are becoming automated. This doesn't mean all jobs disappear; it means the nature of valuable work is changing. Understanding where agentic AI is strong and weak helps you position yourself.

You'll need to direct these systems. Agentic AI is powerful but not autonomous in the full sense. It still needs clear goals, appropriate constraints, and human oversight for consequential decisions. The skill of effectively directing agentic systems, such as giving them clear objectives, setting appropriate boundaries, and reviewing their work, is becoming genuinely important.

The risks are real and specific. Agentic systems can take many actions quickly, which means mistakes can compound. A chat system that gives you bad advice requires you to act on that advice for harm to occur. An agentic system that books the wrong flights, sends the wrong emails, or modifies the wrong files creates problems directly.

Understanding this helps you calibrate how much autonomy to grant.

The Honest Assessment

Let me be direct about where I believe things actually stand.

Agentic AI in early 2026 is impressive but inconsistent. These systems can accomplish things that would have seemed remarkable three years ago. They can also fail in ways that are frustrating and sometimes baffling. They're not reliable enough for fully unsupervised high-stakes work. They require more oversight than the marketing materials suggest.

The progress has been real. The trajectory seems clear. But we're still in the early stages of figuring out how to build these systems reliably, how to integrate them into workflows safely, and how to handle the complications that arise when AI can take extended action in the world.

What you're witnessing is a genuine transition from AI as a conversation partner to AI as a capable executor. Understanding this shift, including both its power and its limitations, puts you in a better position to work with these systems as they continue to develop.

Acknowledgement: This is piece was co-authored with Anthropic's Claude Opus 4.5 with the aim of gaining deeper understanding of how AI agents, agentic AI, and AI systems behave.