Last November, Anthropic published a post calling 2025 “the year of agentic AI.” The phrase got copied into approximately 4,000 LinkedIn headlines within a week. I counted. (I didn’t count. But it felt that way.)
The thing is, most of those posts didn’t explain what an AI agent actually does. They just announced it was important.
So here’s a plain explanation.
What makes something an “agent” vs. just a chatbot
A chatbot waits for you to ask something, answers it, and stops. That’s a one-turn interaction. You type, it responds, done.
An agent is different in one specific way: it takes actions in loops. It doesn’t just answer a question. It uses tools, checks results, decides what to do next based on those results, and keeps going until it hits a stopping condition. The model is making decisions mid-task, not just mid-sentence.
The canonical example is a research agent. You tell it: “Find the last 3 funding announcements from AI safety nonprofits.” It searches the web, reads a few pages, decides some are paywalled and tries alternate sources, synthesizes what it found, and returns a summary. At no point did you direct each step. The model planned and adapted.
That loop structure is the key thing. Memory (what happened earlier in the task), tool use (search, code execution, API calls), and multi-step planning. Without those three, you don’t have an agent, you have autocomplete with a nice UI.
The 4 things agents are genuinely useful for right now
Not everything. Definitely not everything. Here’s where I’ve seen them actually work as of early 2026:
- Information retrieval and synthesis. Finding things across sources, summarizing, cross-referencing. Agents are good at this. Much better than a single-turn query.
- Code generation pipelines. Writing a function, running tests, reading the error, fixing the function. This loop works. GitHub Copilot Workspace and similar tools are real products people use daily now.
- Data extraction at scale. Pulling structured information from unstructured documents. PDFs, emails, web pages. Agents can do in minutes what would take a person hours.
- Meeting and call assistance. Listening to a conversation in real time, suggesting responses, flagging relevant context. This is the category Craqly sits in, specifically for interviews and sales calls.
That last category is worth naming specifically because the agent pattern is what makes it different from a transcript tool. A transcript tool records. An agent listens, infers what the other person is asking, retrieves relevant context, and surfaces it at the right moment. That’s the loop in action.
Where agents actually break down
The failure modes aren’t always obvious from the demos.
Agents fail when the stopping condition is unclear. If you tell a human “research this topic,” they know roughly when to stop. Agents don’t have that intuition yet. They either stop too early (missing relevant data) or keep going long past useful (burning tokens on diminishing returns).
They also fail when the tool calls themselves fail silently. If a web search returns a misleading result and the agent treats it as ground truth, every downstream step inherits that error. There’s no skepticism baked in. I’ve seen agents confidently synthesize completely wrong information because a single search step returned a satirical article.
And they fail on ambiguous intent. “Schedule a meeting” can mean 11 different things depending on context. An agent that interprets it confidently and runs will send the wrong invite. A human would ask.
The Anthropic model card for Claude 3.7 actually documents some of these limitations explicitly, which I appreciated. Most AI companies avoid being that specific.
The “agentic” label is getting stretched too far
This is the part where I’ll probably be wrong, or at least incomplete, but it’s worth saying.
A lot of products calling themselves “agentic” in 2026 are not really running agent loops. They’re running a fixed multi-step prompt chain with predetermined branches. That’s a workflow, not an agent. There’s nothing wrong with a workflow, but it’s different.
Real agents have dynamic planning. The steps aren’t predetermined. The model decides at runtime what to do next based on what it found. If you look at a product and can map out the full decision tree in advance, it’s probably a workflow.
The distinction matters because agents fail differently than workflows. A broken workflow is predictable (step 3 always errors). A broken agent is unpredictable (it might error, or it might take a completely wrong path that still returns a plausible-looking result). The latter is harder to debug and harder to trust in production.
What the Stack Overflow data says about adoption
The Stack Overflow Developer Survey 2024 found that 62% of professional developers were using or planning to use AI tools in their workflow, up from 44% the year before. The growth is real.
But when asked whether they trusted AI outputs, only 43% said they were “somewhat” or “highly” trusting of the accuracy. That gap, between adoption and trust, is where most of the interesting product problems are right now.
You can get people to use an agent. Getting them to trust it enough to let it act without review is a much harder problem. Most enterprise deployments I’ve heard about still have a human in the loop for any action that touches external systems. That’s probably the right call for now.
What to expect over the next 18 months
I’d guess (and this is genuinely a guess) that the most durable agent products will be the ones that solve narrow vertical problems with clear success criteria. Interview prep. Sales call coaching. Code review. Document extraction. These have defined outputs you can verify.
The general-purpose “do anything” agents will keep improving, but the reliability bar for autonomous action in high-stakes contexts is higher than current models can consistently meet.
Whether the gap closes by 2027 or 2030 is uncertain. But the direction is clear enough that ignoring the category seems like a mistake too.