“Agentic AI” gets used loosely enough that it’s worth pinning down what actually distinguishes an agent from a regular chatbot call. The short version: a plain LLM call takes an input and returns an output, once. An agentic system takes a goal, breaks it into steps, takes actions in the world (calling tools, reading files, running code), observes what happened, and decides what to do next, repeating that loop until the goal is met or it gives up. The intelligence isn’t new, it’s still the same underlying language model, what’s new is the loop wrapped around it.
The core loop: plan, act, observe
Almost every agent framework, regardless of branding, implements some version of the same cycle, formalized in the influential 2022 paper “ReAct: Synergizing Reasoning and Acting in Language Models” (Reason + Act) (Yao et al., arXiv:2210.03629). The model reasons about what to do next in natural language, takes an action (typically calling a tool or function), observes the result, and feeds that result back into its next reasoning step. This is why agent transcripts often look like a model “thinking out loud,” deciding to check something, getting a result, and adjusting its plan, that visible reasoning is often the mechanism itself, not just a debugging aid for the user. In the original paper’s own benchmarks, this interleaved reasoning-and-acting approach beat pure imitation- and reinforcement-learning baselines by an absolute 34-point success-rate margin on interactive decision-making tasks, a meaningfully larger gap than most single architectural changes produce.
This loop is what lets an agent recover from a wrong turn. A single-shot LLM call that makes an incorrect assumption just produces a wrong answer. An agent that makes the same incorrect assumption gets to observe the consequence (a failed test, an error message, a file that doesn’t exist) and revise its plan, which is the actual source of agentic systems’ practical advantage over single-shot prompting on any task with multiple steps or a verifiable outcome.
Tool use is what makes the loop useful
An agent that can only reason in text, with no way to act on the world, isn’t meaningfully different from a chatbot. Tool use, giving the model a defined set of functions it can call (read a file, run a shell command, query a database, hit an API), is what turns reasoning into action — and how well that tool-calling loop is described in the system prompt in the first place is very much a prompt engineering problem, not just a model-capability one. Modern frontier models are trained specifically to produce structured tool calls rather than plain text when a task calls for it, and the quality of that training is a major, underappreciated differentiator between models: two models can have similar raw reasoning benchmarks and produce very different real-world agent results depending on how reliably they format tool calls, recover from a tool returning an error, and avoid calling a tool when it isn’t actually needed.
This is also the mechanism behind coding agents specifically, arguably the most mature and widely deployed category of agentic AI right now. A coding agent’s tools are typically: read a file, edit a file, run a shell command, search a codebase. The loop of “read the relevant files, propose an edit, run the tests, read the failure, revise” is a direct application of plan-act-observe, and it’s a big part of why coding was one of the first domains where agentic AI moved from research demo to daily professional tool.
Memory and context, not the same thing
A common source of confusion: an agent’s context window (how much text it can see in a single call) is not the same as memory across separate sessions. Within a single task, an agent typically carries its entire history of actions and observations forward in context, which is why long-running agentic tasks are one of the main pressures pushing frontier labs toward bigger context windows. But once a session ends, that context is usually gone unless the system explicitly persists something, a summary, a set of learned facts, a vector database of past interactions, to reload next time. Systems marketed as having “memory” are almost always doing exactly this: writing a distilled summary somewhere durable and retrieving relevant pieces of it into a fresh context window later, not literally keeping a model’s internal state alive between sessions.
Multi-agent systems
Once single-agent loops became reliable enough for real tasks, the next step was having multiple agents coordinate: one agent plans and delegates, sub-agents execute narrower pieces of the task in parallel, and results get merged back. OpenAI’s “ultra mode” for GPT-5.6 Sol is a direct example of this pattern reaching a shipped, consumer-facing product rather than staying a research pattern: instead of processing a task as one sequential reasoning chain, ultra mode decomposes a task and spawns multiple parallel subagents that coordinate mid-task before combining their results into a single output (Previewing GPT-5.6 Sol, OpenAI). On Terminal-Bench 2.1, a benchmark of real command-line coding workflows, that subagent approach has been reported to measurably outperform the same model running single-threaded, evidence that the coordination overhead is worth paying for tasks that genuinely decompose into parallel pieces. The appeal is straightforward: many real tasks parallelize naturally (research three independent sub-questions, refactor three independent modules), and splitting them across agents can be faster than one agent working serially through all of them, at the cost of more complexity in merging results and more total tokens spent, since each subagent generates its own reasoning independently.
Agentic system patterns compared
| Pattern | Coordination | Best Fit | Main Cost |
|---|---|---|---|
| Single-agent tool use | None — one loop, sequential | Well-scoped tasks with a clear step order | Slower on tasks that parallelize naturally |
| Multi-agent (planner + sub-agents) | Planner delegates, then merges results | Tasks that split into independent sub-problems | Higher token cost, merge complexity |
| Human-in-the-loop | Human explicitly approves high-consequence steps | High-stakes or hard-to-reverse actions | Slower; requires a human available to respond |
The framework landscape: mostly orchestration, not new intelligence
It’s worth being clear about what an “agent framework” actually provides, since the term can make it sound like separate AI capability rather than plumbing. LangChain, one of the most widely adopted open-source agent frameworks, and its sister project LangGraph provide the orchestration layer around the plan-act-observe loop described above: pre-built integrations to models and tools, persistence and checkpointing so a long-running agent can pause and resume, and support for the multi-agent coordination patterns discussed below (LangChain). None of this makes the underlying model smarter. What it does is turn “write your own tool-calling loop, error handling, and state management from scratch” into “configure an existing, tested orchestration layer”, which is precisely why frameworks like this proliferated once the underlying models got reliable enough at tool use for agentic patterns to be worth building on top of at all.
Real-world adoption of agentic systems specifically, as distinct from generative AI more broadly, is still early. McKinsey’s 2025 State of AI survey found 23% of organizations report they’re actively scaling an agentic AI system somewhere in the enterprise, with a further 39% still in the experimentation stage (McKinsey, The State of AI in 2025). Read together with the multi-agent coding benchmark result above, the pattern is consistent with the rest of this piece’s argument: the core technique works and is measurably better on tasks suited to it, but most organizations deploying it are still closer to the “3 well-scoped autonomous steps” end of the spectrum described below than the “30 steps, fully unsupervised” end.
Why “agentic” is a spectrum, not a checkbox
It’s worth resisting the framing that a product either “is” agentic AI or isn’t. In practice, agentic capability sits on a spectrum: how many steps can the system take autonomously before it needs a human to check in, how much of the tool surface can it operate versus needing a human to execute privileged actions, and how well does it recover from an unexpected observation versus getting stuck in a loop or confidently proceeding on a wrong assumption. A system that can reliably run 3 well-scoped steps unsupervised is meaningfully different from one that can run 30, even if both get marketed under the same “agentic AI” label, and that gap, autonomy that holds up over many steps without drifting or getting stuck, is where most of the real engineering difficulty in this category actually lives. For a broader look at where generative and agentic techniques are actually landing in production outside of coding, see our generative AI applications explainer.
The practical safety question
The genuine, non-hypothetical risk with agentic systems isn’t science-fiction autonomy, it’s the more mundane failure mode of an agent confidently taking a wrong but plausible-looking action with real side effects: deleting the wrong file, sending a real email, executing a destructive command, because nothing in its loop caught that the action was a mistake before it happened. This is why the more careful agent frameworks build in checkpoints, explicit confirmation before high-consequence actions, sandboxed execution environments, rate limits on tool calls, rather than treating full autonomy as the goal to maximize by default. The state of the art right now isn’t “how autonomous can we make this,” it’s “how do we get the benefits of autonomy on the steps that are safe to automate, while keeping a human in the loop on the ones that aren’t.”
