What are AI agents? An AI agent is regular software whose core decision-making comes from a large language model rather than if/else branches. That single change makes the behavior nondeterministic: the same input can lead to different tool calls, different answers, and different paths to the same goal. Everything else, from memory to frameworks to harnesses like Claude Code, follows from that shift.
What Are AI Agents, and How Do They Differ from Traditional Software?
What are AI agents? An AI agent is regular software whose core decision-maker is a large language model instead of hand-written control flow. The model, not your branches, decides how to interpret input, which tools to call, and how to reach the result, so the behavior is nondeterministic by design.
Programming languages are deliberately small. A handful of keywords such as if, else, for, and while exist to remove ambiguity: when this happens, do that. Determinism is a feature of the languages most developers use daily, and it is exactly the property an agent gives up.
An LLM understands natural language, so an agent can do things conventional programs generally cannot. It can work with unstructured data, hold a wider context, choose and combine tools creatively, and simulate scenarios before acting. The trade-off is that the same prompt can yield different valid answers, which is the main adjustment developers have to make when testing and operating agents.
Two properties keep agents manageable. First, a good agent has a narrow role: agents that try to do everything tend to be good at nothing, or they need very strict prompts to stay on track. Second, an agent knows its environment: it can see which tools, MCP servers, and databases it is allowed to touch, and it stays inside that boundary.
How Do Agents Handle Memory?
Agents normally carry two kinds of memory: short-term and long-term. Short-term memory holds intermediate state inside a single session, such as what the user just said and what the agent tried. Long-term memory persists across sessions, letting the agent reuse facts about the user, past decisions, and earlier outcomes.
Memory is one of the genuinely hard parts of agent engineering. The agent has to decide what is worth storing, when to retrieve it, and how much of it fits into the model's context window on each call. Get it wrong and the agent either forgets the customer or drowns the model in stale detail. This is why memory management is usually built into the surrounding platform rather than left to a single prompt.
Why Is Claude Code a Harness and Not an Agent?
Claude Code, Anthropic agentic coding tool that runs in the terminal, is a harness: a platform that surrounds and runs agents, rather than an agent itself. A harness bundles the model, the available tools, the user's subscription and model selection, memory handling, and agent management built on Anthropic SDK into one developer platform.
When a developer says they 'created an agent in Claude Code', something much simpler happened. Writing a file such as reviewer.md inside the agents folder gives the harness a named prompt. At runtime, the platform lists that file's contents, instantiates an internal agent with those instructions, the chosen model, its tools, and its MCP connections, and then routes conversation turns to it. The name 'agent' here is semantic convenience, not a new piece of software you wrote.
The distinction matters commercially. Companies do not need 'their own Claude Code' in the sense of a markdown file. They need their own harness: a platform that instantiates agents, hands them the right tools and memory, and exposes them to real users. The markdown file is transparent; the platform behind it is where the engineering lives.
Which Frameworks Help You Build Agents?
Five mainstream frameworks cover most agent development today, and each solves the same core problem: calling the model is one API request, while everything around it, tools, memory, orchestration, and state, is the hard part. Frameworks give you that surrounding scaffolding so you do not build it from scratch. Anthropic itself built Claude Code on top of its own SDK rather than a public framework.
Agents also need a standard way to reach external tools. The Model Context Protocol (MCP), an open standard introduced by Anthropic November 2024, defines how agents discover and call external tools and data sources (MCP documentation).
| Framework | Maintainer | What it gives you |
|---|---|---|
| LangChain | LangChain | General LLM application toolkit |
| LangGraph | LangChain | Graph-based agent orchestration |
| CrewAI | CrewAI | Multi-agent teams with roles |
| Google ADK | Agent Development Kit for Python/Java | |
| AutoGen | Microsoft | Conversational multi-agent framework |
The framework choice is less important than understanding what the framework hides. Whichever you pick, you still have to define the agent's narrow role, its tool surface, its memory strategy, and how it gets deployed. Skills transfer between frameworks; the concepts do the heavy lifting.
What Does a Real Company Agent Platform Look Like?
A production agent platform is a Claude Code-like stack built for one company's context, and the Full Cycle video from August 2026 illustrates it with an admissions-consultant agent. The channel's premise is simple: an AI agent is software whose core is an LLM, and every serious deployment is a platform around that core.
The example works like this. A prospect messages the company on WhatsApp. The message hits the company's own platform, which instantiates a consultant agent with its prompt, tools, skills, and memory. That agent can ask for a name and store it in a database, understand questions about the courses, consult its knowledge base, save conversation context, and schedule a meeting with a human salesperson. The reply flows back through WhatsApp.
From the user's side it looks like a chatbot. Underneath, it is a platform with orchestration, tool access, and memory, the same layered architecture a harness provides. The same pattern generalizes: in agriculture, an incoming report-analysis request can trigger one agent, then another, then another, each with its own tools. Building this is about orchestration, tool design, and where you place intelligence, not about any single product.
This is also why 'agent' in everyday conversation covers two different things. A named prompt inside a harness is one thing; a purpose-built agent platform like the WhatsApp consultant is another. Treating them as the same concept is what causes most of the confusion the Full Cycle video sets out to fix. The topic is now core material in Brazilian developer education as well, appearing in programs such as the Bootcamp do Dev Doido and the Crazystack Typescript track from Gustavo Dev Doido (crazystack.com.br).
FAQ
- What are AI agents in one sentence? An AI agent is software whose core decision-making is performed by a large language model instead of fixed control flow, so it interprets input, chooses tools, and adapts its path to a goal on its own.
- Is Claude Code an AI agent? No. Claude Code is a harness, a platform that hosts and runs agents. Its model, tools, subscription layer, memory handling, and SDK-based agent management sit around the agents, and a file in its agents folder is just a named prompt.
- Do I need a framework to build an agent? No, but you would rebuild a lot. Calling the model is a single API request; the surrounding work of tool integration, memory, and orchestration is what frameworks like LangGraph, CrewAI, Google ADK, and AutoGen already provide.
- Why are agents nondeterministic, and does that matter? The core is an LLM reasoning in natural language, so the same input can produce different valid outputs and different tool sequences. It matters for testing, observability, and how you design guardrails around the agent.
Turn Your Own Video Explainers Into Written Articles
The clearest idea in this article is that one sharp concept, the difference between an agent and a harness, can save a developer weeks of confusion. If you explain ideas like this on YouTube, that same clarity usually stays locked inside a video that only some of your audience will ever watch.
Skala Blog turns an existing video into a structured written article: paste the YouTube URL, the video is transcribed, and you get a publishable draft you can edit. If a 14-minute explanation like this one is worth recording, it is worth a version people can search, skim, and quote.
Fork this article
Start a new branch from the same video, shaped your way. You keep the credit; the original keeps the attribution.
A fork in another language is filed as a translation of this article, so the two pages point at each other. You can unlink it later from the editor.
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