Agentic AI describes software that can plan a multi-step task, choose its own tools, and act without a human approving each move. It's the difference between a chatbot that answers a question and a system that goes and does the job.
Search interest in the term has exploded over the past year, but ask ten people what is agentic ai and you'll get ten different answers. Some think it means any chatbot with plugins. Others think it's a synonym for AGI. Neither is right. Agentic AI is a specific architecture: an AI model wrapped in a loop that lets it observe a result, decide what to do next, and keep going until it either finishes the job or gets stuck. That loop is the whole idea. Everything else is implementation detail.
Generative AI produces an output and stops. You ask ChatGPT to draft an email, it drafts the email, the task ends there. Agentic AI keeps a goal in memory and keeps acting until that goal is met, calling tools, checking its own work, and adjusting when something fails. Cognition's coding agent Devin is a clean example: given a bug report, it doesn't just suggest a fix, it clones the repository, writes the code, runs the test suite, reads the failure output, and rewrites the code again if the tests don't pass. That loop of act, observe, revise is what separates an agent from a generative tool. A generative model is a very good intern who hands you a draft. An agentic system is closer to a contractor who takes the job and doesn't come back until it's done, or until it hits a wall it can't climb.
The underlying model in both cases is often the same large language model. What changes is the scaffolding around it: a planner that breaks the goal into steps, a memory store that tracks what's already happened, and a set of tools, a code interpreter, a browser, an API, a database query, that the model can call mid-task. Strip away the marketing and agentic AI is really just an LLM given a to-do list, a set of hands, and permission to use them repeatedly.
How AI Agents Work in Practice
Under the hood, most agentic systems run some version of the same cycle. First, the model breaks a high-level goal into smaller sub-tasks. Then it picks a tool for the next sub-task, whether that's searching the web, writing code, or querying a database. It executes that action, reads back the result, and decides whether the sub-task succeeded. If it did, it moves to the next step. If it didn't, it tries a different approach, or asks for help. Salesforce built its Agentforce platform around exactly this pattern: a customer service agent that can look up an order, check a return policy, issue a refund, and escalate to a human only if the policy is ambiguous. Salesforce has said publicly that Agentforce handles a large share of routine support queries without a human touching the ticket. That's not generative AI answering a question. That's an agent completing a transaction.
The planning step is where most of the sophistication lives, and also where most of the disagreement between vendors shows up. Some systems use a single model to both plan and execute. Others, like multi-agent frameworks such as Microsoft's AutoGen, split the job across several specialized model instances that critique each other's work before anything ships. More agents in the loop generally means better error-catching and slower, more expensive execution. There's no free version of this trade-off yet.
Where Autonomous AI Agents Break Down
Here's the part the marketing decks leave out: autonomous agents fail constantly, and they fail in ways that are hard to catch because the system sounds confident the whole time. The clearest public case is AutoGPT, the open-source agent that went viral in early 2023 with the promise of fully autonomous task completion. Users quickly found it burning through OpenAI API credits on loops that never resolved, agents that would research a task, decide they needed more research, research the research, and never actually produce the output. AutoGPT didn't fail because the underlying model was weak. It failed because nothing was checking whether the loop was converging on an answer or just spinning.
That's the core failure mode of agentic systems: compounding error. If a single model call is 95% reliable, that sounds fine until you chain twenty of those calls together, and now your end-to-end success rate is under 36%. Every additional step an agent takes to complete a task is another chance for a wrong tool call, a misread result, or a plan that quietly drifts off the actual goal. And because the agent narrates its own reasoning in confident, well-formed sentences, a wrong step often looks identical to a right one until someone checks the output.
The second failure mode is tool access itself. An agent that can only draft text is limited but safe. An agent with a live database connection, a payment API, or shell access can do real damage from a single bad decision, and there's no universal safety rail yet that catches this reliably across vendors. That's why the agent deployments that are actually working today, Klarna's customer service assistant among them, tend to operate inside a narrow, well-defined domain with a hard boundary: Klarna's assistant handles refunds and order questions, not open-ended requests, and it hands off to a human the moment a query falls outside that boundary. Klarna has said the assistant does the work of roughly 700 full-time agents, but that number only holds because the task is bounded. Nobody has shipped a general-purpose autonomous agent that can be trusted with an unbounded task and no human check.
What This Means If You're Building or Buying
If you're a founder evaluating agentic AI for your own product, the question to ask a vendor isn't
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