LLMs, AI Agents and Agentic AI explained: discover how artificial intelligence is evolving from chatbots that answer questions into systems that can carry out complex tasks.
Updated: 29 October 2026 | CTRL + ALT News — Artificial Intelligence
Transparency note: this site uses Claude, made by Anthropic, and Claude appears in the comparison below. So that we don’t rely on a single source, the performance data comes from an independent evaluator (Artificial Analysis), and each figure says where it came from.
You open ChatGPT, ask a question and receive an impressive answer. You turn to Gemini for an explanation of something complicated. You use Claude to write an email or analyse a document.
And you think: “Right, I understand artificial intelligence now.”
But what if you’ve only scratched the surface of what AI can actually do?
Imagine three situations.
In the first, you ask an AI assistant to recommend a destination for your next holiday. It replies with a list of interesting places and useful suggestions.
In the second, you ask it to research destinations, compare hotels, check flight prices and put together a personalised itinerary.
In the third, you simply say: “Organise my next holiday within my budget.”
The system breaks the job into smaller tasks, consults different services, compares the options, checks the information, adjusts its plan when something goes wrong and prepares the bookings for your approval.
All three situations involve artificial intelligence.
But they’re not the same thing.
The difference becomes much clearer when you understand three concepts that are shaping the future of AI:
- LLM (Large Language Model): the language model that interprets information and generates responses.
- AI Agent: a system that can use tools, access information and take actions to achieve a goal.
- Agentic AI: an approach in which AI systems plan, make operational decisions and carry out multi-step tasks with varying degrees of autonomy.
These concepts help explain one of the biggest changes taking place in technology: the shift from AI that tells you how to do something to AI that can help get it done.
1. What is an LLM? The language engine behind modern AI
LLM stands for Large Language Model.
In simple terms, it’s a type of artificial intelligence trained on vast amounts of data to recognise patterns, interpret instructions and generate language.
Modern LLMs can write, translate, summarise, explain difficult subjects, analyse documents, produce computer code and help solve a wide range of problems.
Some can also process images, audio, video and other forms of information. These are often called multimodal models.
### How does an LLM actually work?
Imagine someone who has spent years studying books, scientific papers, technical manuals, conversations and programming examples.
Over time, that person develops an extraordinary ability to recognise relationships between ideas and formulate useful explanations.
An LLM works very differently from a human brain, but the comparison helps illustrate the result.
During training, the model adjusts an enormous number of mathematical parameters to learn statistical patterns and relationships within its training data.
Many modern LLMs generate text by predicting the next token, a unit that might represent a word, part of a word or another fragment of information. IBM describes LLMs as giant statistical prediction machines built on the transformer architecture.
Combined with specialised training and computational reasoning techniques, this relatively simple principle can produce remarkably sophisticated behaviour.
Here’s an example.
You type:
“Explain quantum computing as if I were 12 years old.”
The model interprets your request and produces an explanation suitable for a younger reader, perhaps using everyday examples to make complicated physics easier to understand.
But there’s an important detail.
The model hasn’t necessarily consulted a scientific source while answering.
It may be relying on patterns learned during training, together with information available in the conversation.
That helps explain one of the biggest limitations of LLMs.
They can sound completely confident and still be wrong.
This is commonly known as an AI hallucination: when a model produces false, misleading or unsupported information.
Another common misunderstanding is that all artificial intelligence is based on LLMs.
It isn’t.
Image recognition, recommendation algorithms, prediction systems and traditional machine-learning methods are also forms of AI.
LLMs are a particularly important branch of artificial intelligence, not the whole field.
### Examples of leading LLMs
Some of the best-known model families include:
| Model family | Developer | Examples in 2026 |
|---|---|---|
| GPT | OpenAI | GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra |
| Claude | Anthropic | Claude Opus 5.5, Claude Sonnet 5.5 and Claude Fable 5.1 |
| Gemini | Google DeepMind | Gemini 4 Carbon, Gemini 4 Argon and Gemini 3.8 Flash |
| Muse | Meta | Muse Wonder and Muse Spark Open |
| Grok | xAI | Grok 4.7 |
| DeepSeek | DeepSeek | DeepSeek V4.1 Flash |
| Qwen | Alibaba | Qwen3.8 |
| Kimi | Moonshot AI | Kimi K3.1 Pro |
These families contain models with different capabilities, licences, costs and access arrangements. Not every model is available to every user.
It’s also worth understanding the difference between a model and an application.
GPT isn’t the same thing as ChatGPT.
GPT is a family of AI models. ChatGPT is a product that provides an interface and additional features for using AI.
Similarly, Claude refers to both Anthropic’s model family and its assistant, while Gemini is used for Google’s models and AI-powered products.
The application can add tools, context, integrations and other capabilities.
Think of an LLM as a car engine.
The engine is crucial, but it isn’t the entire car.
To turn the engine’s power into real-world movement, you need other components.
And that’s where AI agents come in.
2. What is an AI Agent? When AI starts taking action
An AI Agent is a system designed to achieve objectives by using resources such as AI models, external tools, available information and execution rules.
The key difference is its ability to take action rather than simply generate an answer.
A conventional chatbot might tell you how to organise the files on your computer.
An AI agent with the appropriate tools and permissions could inspect a folder, identify its contents, categorise the documents and create a new filing structure.
Instead of just explaining what needs doing, it can participate in doing it.
### Example: a personal travel assistant
Imagine you say:
“Find me a four-night trip from London to Rome for less than £800.”
A chatbot without access to live travel information might suggest destinations, typical prices and general travel tips.
An AI agent connected to suitable tools could potentially:
- Search for available flights.
- Check hotel options.
- Compare prices and booking conditions.
- Calculate the total cost.
- Check whether the itinerary meets your requirements.
- Present suitable alternatives and prepare the next booking steps.
If a hotel is unavailable, the agent may search for another.
If the flights push the trip over budget, it may consider different departure times.
And if a booking requires payment, a properly designed system can pause for your approval.
That’s a fundamentally different experience from receiving a list of suggestions.
However, an agent can only perform actions supported by its tools and permissions.
Giving a model an instruction doesn’t magically give it access to booking systems, bank accounts or your computer.
### What makes an AI agent work?
A modern AI agent usually combines several components.
| Component | Purpose | Simple analogy |
|---|---|---|
| AI model | Interprets information and helps decide what to do | The brain |
| Instructions | Define the objective and rules | The job description |
| Tools | Enable searches, calculations and actions | The hands |
| Context and memory | Maintain relevant information | The notebook |
| Orchestration | Coordinates the sequence of tasks | The work plan |
| Safety controls | Set boundaries and permissions | The rules and approvals |
Anthropic describes the basic building block of an agent as an LLM augmented with retrieval, tools and memory. OpenAI’s practical guide to agents, which we could not open, is described by press reports as naming the model, tools and instructions as the three core elements. Other capabilities, including memory, state management and monitoring, can be added to support more demanding systems.
An AI agent doesn’t necessarily require a new type of LLM.
Often, it’s a system built around an existing model, giving that model access to tools and a structured way of using them.
### The AI agent’s working loop
Many agents follow a recurring process:
Goal → Plan → Action → Observe → Adjust → Act again → Complete
Imagine an agent asked to analyse a business spreadsheet.
It begins by understanding the goal: identify unusually high expenditure.
Next, it opens the data, performs calculations, compares periods and looks for inconsistencies.
If it encounters missing information, it might request clarification or consult an authorised source.
It then checks the outcome and prepares a report.
What matters is that the system can use the result of one action to help determine the next.
This is often called an agent loop.
### Is every customised chatbot an AI agent?
Not necessarily.
Giving a chatbot a name, a particular personality or a detailed set of instructions doesn’t automatically make it an operational agent.
If it only responds to questions, it’s still primarily conversational.
If it can select tools, take actions and adapt its approach based on results, it begins to exhibit the behaviour associated with AI agents.
This distinction matters because the technology industry often uses the word agent to describe products with very different levels of autonomy.
3. What is Agentic AI? When AI organises its own work
Now we arrive at the concept that causes perhaps the most confusion: Agentic AI.
The term describes AI systems designed to pursue goals, plan tasks, choose appropriate actions, use tools and adapt to new information during execution.
It isn’t necessarily a completely separate technology from AI agents.
In fact, AI Agent and Agentic AI are closely related terms, and you’ll often see them used interchangeably. Sources don’t agree on a single definition, and the distinction below is the one we adopt in this article.
The most useful distinction is this:
AI Agent describes the system carrying out the work. Agentic AI emphasises the behaviour and architecture that allow the work to be carried out with some degree of autonomy.
A single agent can be agentic.
You don’t need a team of agents to qualify.
However, agentic systems can also coordinate several specialised agents working towards a shared objective.
### The restaurant analogy
Imagine three different situations.
Scenario 1 — LLM: the recipe adviser
You ask how to prepare an Italian dinner.
The AI suggests recipes, lists the ingredients and explains the cooking process.
You still have to organise and prepare everything yourself.
Scenario 2 — AI Agent: the kitchen assistant
You ask the system to organise the dinner.
With suitable tools, it can research recipes, calculate quantities, check your shopping list and put together a preparation schedule.
It’s now actively helping with the task.
Scenario 3 — Agentic AI: the coordinated kitchen
You give one objective:
“Organise an Italian dinner for six people, including a vegetarian option, without exceeding my budget.”
The system breaks the task down.
One component researches recipes.
Another checks the ingredients.
A third calculates costs.
A final stage examines the plan to ensure it meets your requirements.
If an ingredient isn’t available, the system can revise its recommendations.
And before spending your money, it can ask for approval.
The important difference isn’t simply how much information the AI knows.
It’s how effectively it can coordinate decisions and actions to achieve an outcome.
### Is Agentic AI just another word for automation?
Not quite.
Traditional automation usually follows a predefined sequence.
For example:
“When someone submits a form, copy their details into a spreadsheet and send a confirmation email.”
That can work perfectly well without an LLM.
An agentic system can handle situations where the next step isn’t known in advance.
For example:
“Read incoming customer enquiries, identify urgent cases, consult the relevant information and recommend the appropriate action.”
The system may need to interpret different situations and select different approaches.
There’s an important caveat, though.
More autonomy doesn’t automatically mean better technology.
For simple, repetitive and predictable tasks, conventional automation is often faster, cheaper and easier to test.
Anthropic’s guidance on building effective agents recommends beginning with the simplest architecture that solves the problem and adding complexity only when it brings a measurable benefit. It separates workflows, where code sets the order of the steps, from agents, where the model itself directs the process.
4. LLM vs AI Agent vs Agentic AI: the differences explained
Here’s how the three concepts compare.
| Capability | LLM on its own | AI Agent | Agentic AI system |
|---|---|---|---|
| Answer questions | Yes | Usually | When required |
| Access external tools | Not independently | Can do | Usually does |
| Perform external actions | Not independently | With permission | With permission |
| Plan multi-step tasks | Can suggest a plan | Can execute a plan | Can plan and adapt execution |
| Respond to results | Through further interaction | Yes | Yes |
| Coordinate other agents | Not on its own | May do | May include this capability |
| Autonomy | Depends on the application | Varies | Central to the design |
| Typical example | Explain a spreadsheet | Analyse a spreadsheet | Coordinate analysis, verification and reporting |
These are typical patterns rather than rigid technical categories. An LLM integrated into an application with tools and an execution loop can form part of either agent-based architecture.
The easiest way to remember it is:
An LLM helps formulate answers and decisions. An AI agent turns decisions into actions. Agentic AI organises decisions and actions to achieve goals.
5. A practical example: how AI could transform a technology newsroom
Imagine an independent technology news website covering artificial intelligence, robotics and consumer gadgets.
Its editorial team needs to publish timely, accurate and useful information.
Using an LLM, an editor might ask:
“Write an article explaining the launch of a new processor.”
The model could produce a draft based on the information available to it.
But the editor would still need to check sources, verify technical specifications, prepare images, review the writing and organise publication.
Now imagine a specialist research agent.
When given a new product to investigate, it could consult the manufacturer’s website, inspect technical documentation, compare independent sources and create a research brief.
That already reduces some of the repetitive work.
### What would an agentic newsroom look like?
A more advanced system could split the editorial workflow into several specialised roles.
| Specialist agent | Responsibility |
|---|---|
| Research agent | Find relevant news and reliable sources |
| Fact-checking agent | Verify figures, dates and claims |
| Writing agent | Produce an original, accessible article |
| SEO agent | Assess search intent and organise the content |
| Visual editor agent | Plan images, captions and alternative text |
| Review agent | Identify mistakes, inconsistencies and gaps |
A coordinating agent could oversee the process, assigning tasks and passing relevant information between them.
If the fact-checking stage uncovered contradictory information, the system could send the issue back for further research rather than allowing an unsupported claim into the final article.
It could then prepare a draft with references and editorial notes.
The human editor would still make the final publication decision.
This is an example of a possible architecture, not a claim that every AI product can perform all these tasks without configuration.
Its reliability would depend on the quality of the sources, the tools available and the review process.
6. Which LLMs are best for AI Agents and Agentic AI in 2026?
Before comparing models, we need to separate two questions:
Which AI model performs best on complex reasoning and agentic tasks?
And:
Which AI platform makes it easiest to build or use a genuinely useful agent?
The answers aren’t necessarily the same.
A model might achieve outstanding benchmark results but have restricted access.
Another might achieve a slightly lower score while offering better tools, lower costs or more convenient integrations.
So there isn’t one universal winner.
### OpenAI: GPT-6 and the agent ecosystem
OpenAI’s GPT-6 family includes models designed for different levels of complexity and efficiency.
GPT-6 Astra, announced on 3 September 2026, is, according to OpenAI, state of the art in computer use, software engineering, professional work and science. It was the company’s first model to reach the “Critical” level of its internal cybersecurity risk threshold, and OpenAI limited access to those capabilities (CNBC).
GPT-6.1 Sol is another high-capability model in the family. In Artificial Analysis’s index it sits just below Astra.
GPT-6.1 Terra, introduced on 26 October 2026, is described by press reports as a faster, cheaper model focused on computer use and work tasks with tools and graphical interfaces. OpenAI’s page would not open for us; the date, the focus and the price of $1 per million input tokens and $5 per million output tokens come from press coverage and should be checked.
OpenAI’s ecosystem also includes products such as ChatGPT Work and Codex, plus agent-development features through its API.
Particularly relevant for (according to OpenAI): computer use, professional workflows, research with tools and software engineering.
Sources: CNBC on GPT-6 Astra and BleepingComputer on GPT-6.1 Terra.
### Anthropic: Claude and long-running agents
Anthropic’s Claude family is another major contender.
Claude Opus 5.5 is described in the company’s documentation as the model for long-running agentic coding and knowledge work, and the page names it as the starting point for most workloads.
Claude Fable 5.1 is described by Anthropic as built for “jobs that take hours and span many applications”, including Cowork, browser operation and unattended managed-agent runs. It costs $10 per million input tokens and $50 per million output tokens.
Claude Sonnet 5.5 is the “best combination of speed and intelligence”, according to the documentation, at $2 and $10.
Anthropic’s ecosystem includes Claude Code, Claude Cowork and tools for building and running managed agents.
Particularly relevant for (according to Anthropic): coding, document analysis, extended professional projects and specialist agent workflows.
Sources: Claude Fable and the models documentation.
### Google: Gemini and enterprise agent platforms
Google is also investing heavily in advanced models and agent-based systems.
Gemini 4 Carbon, announced on 28 October 2026, reached the top of the Artificial Analysis Intelligence Index v5 with a score of 59. Google positions it for long-horizon agentic coding and engineering and, in particular, for cybersecurity defence.
However, there’s a significant qualification: access is restricted. In the week of the announcement Google made it available to “a subset of cyber defenders” and says it will extend access gradually to trusted partners, with no date for a general release. On the date of this article it was not widely available to the public. Google also says the external performance figures were measured without safeguards and that most of the figures come from Google or its partners.
Gemini 4 Argon, launched two weeks earlier, has a context window of about 1 million tokens. Google also lists models such as Gemini 3.8 Flash.
Google also provides platforms for agent-based development, including Antigravity for software engineering and Gemini Enterprise Agent Platform for business use.
Particularly relevant for (according to Google): advanced reasoning, agentic coding, cybersecurity and integrations across Google’s ecosystem.
Source: Google — Introducing Gemini 4 Carbon.
### What about open-weight models?
Families such as Muse (Meta), DeepSeek, Qwen and Kimi also deserve attention. One example is Meta’s Muse Spark Open, with open weights under its own licence.
Models whose weights are made available to developers can be integrated into privately managed systems, subject to their respective licences and technical requirements.
For some organisations, that provides greater control over deployment, infrastructure and data handling.
But there’s a crucial distinction.
An open-weight LLM isn’t automatically a complete AI agent.
Developers still need to provide tools, permissions, memory or state management, execution logic and appropriate monitoring.
Hardware requirements, licensing terms, performance and security also need to be considered.
### What do independent AI benchmarks show?
As of 29 October 2026, Artificial Analysis’s overall leaderboard showed these leading results:
| AI model | Intelligence Index v5 score |
|---|---|
| Gemini 4 Carbon (high) | 59 |
| Claude Opus 5.5 (max, with fallback) | 58 |
| GPT-6 Astra (max) | 57 |
| GPT-6.1 Sol (max) | 55 |
| Claude Sonnet 5.5 (max, with fallback) | 54 |
| Claude Fable 5.1 (xhigh, with fallback) | 50 |
| Muse Wonder Full (max) | 50 |
| Kimi K3.1 Pro | 46 |
| Grok 4.7 (high) | 35 |
| Qwen3.8 Max (0902) | 34 |
| DeepSeek V4.1 Flash 1017 (max) | 32 |
Source: Artificial Analysis, model evaluations available on 29 October 2026.
These numbers are not accuracy percentages. Nor are they a standalone measurement of how effective a model is as an AI agent.
The index combines nine evaluations across knowledge work, coding, science and general capabilities, including challenges that involve tools and multi-step execution.
The scores also reflect specific reasoning-effort settings and, in some cases, fallback configurations.
One example of why the index doesn’t settle everything: Claude Fable 5.1, which Anthropic describes as the model for its longest and hardest jobs, scores 50 on this index, below Sonnet 5.5 at 54.
On a separate evaluation aimed at coding agents, Artificial Analysis put Claude Code with Claude Opus 5.5 ahead, on 62, against 59 for Gemini 4 Carbon with Antigravity, in its 28 October comparison. On an agentic knowledge-work evaluation (AA-Briefcase), Gemini 4 Carbon trailed Kimi K3.1 Pro, Muse Wonder and Grok 4.7.
That is why a single ranking can’t decide the best model for every application.
Sources: Artificial Analysis — model leaderboard and its Gemini 4 Carbon analysis.
### So which AI model should you choose?
For complex coding and specialist workflows, Claude Opus 5.5 and GPT-6 Astra are strong candidates worth testing, and Anthropic points to Fable 5.1 for very long jobs.
For professional tasks involving browsers, files and multiple applications, OpenAI’s agent-enabled tools provide another route.
Organisations already using Google’s services may find its Gemini and enterprise agent platforms particularly relevant.
Developers who need more deployment control may prefer to explore suitable open-weight models.
And while Gemini 4 Carbon leads the overall intelligence benchmark, its restricted availability is an important practical consideration.
The best model is the one that reliably completes your particular task, not simply the one with the highest score.
A sensible comparison should measure accuracy, completion rates, cost, speed, integration, safety and the amount of human supervision required.
7. How do AI agents connect to apps and work with one another?
One of the most important developments in agentic AI is the emergence of standards that help AI systems connect to external tools and collaborate.
Two names are particularly worth knowing:
MCP and A2A.
### MCP: connecting AI to tools and information
MCP stands for Model Context Protocol.
Anthropic introduced it on 25 November 2024 as an open standard for connecting AI assistants to the systems where data lives.
Imagine an agent that needs to access a document repository, query a database and use a business application.
Without a shared standard, developers may need to build separate connections for each combination of systems.
MCP provides a more consistent way for AI applications to discover and interact with supported resources.
Think of it as a type of standardised connector between AI applications and digital services.
But MCP isn’t a free pass to access everything.
Permissions, authentication and security controls still matter.
Source: Anthropic — Introducing the Model Context Protocol.
### A2A: helping different AI agents collaborate
A2A stands for Agent2Agent.
Google announced it on 9 April 2025 to support communication and coordination between agents built using different systems or frameworks. Google says it complements MCP.
Imagine a company with two specialised agents.
One manages stock levels.
The other handles delivery planning.
When the stock-management agent identifies a shortage, it can request support from the logistics agent.
The logistics agent can then check delivery options and return the information needed to continue.
A2A aims to make this kind of collaboration easier to standardise across platforms.
In simple terms: MCP helps agents work with tools and data. A2A helps agents communicate with other agents.
The two approaches can complement one another.
Source: Google Developers — Agent2Agent Protocol.
8. The more autonomous AI becomes, the more important control becomes
So far, the possibilities sound impressive.
But there’s a major difference between an AI system producing an incorrect answer and an AI system taking an incorrect action.
If an LLM invents a reference, a reader might receive misleading information.
If an agent has permission to modify files and misunderstands a task, it could change important documents.
And if an agentic system coordinates several steps without adequate checks, errors can spread through the entire workflow.
One particular threat is known as prompt injection.
This happens when malicious instructions embedded in a webpage, document or other source attempt to influence an AI system’s behaviour.
For example, an external page might contain instructions designed to persuade an agent to ignore its original task.
That’s why autonomy must be matched with appropriate safeguards. In Trustworthy agents in practice, Anthropic describes controls such as setting permissions for each tool (allow, require approval or block), showing the plan before execution and pausing to ask for clarification when intent is unclear, and says defences against prompt injection must work at several layers because no single safeguard is a guarantee.
Good practices include:
- Granting only the permissions required for a task.
- Keeping records of actions taken.
- Checking important results against reliable sources.
- Treating external content as data rather than authoritative instructions.
- Requiring human approval for sensitive or irreversible actions.
- Setting limits on spending, execution time and tool use.
- Allowing users to stop or interrupt a task.
The principle is straightforward:
The more an AI system is allowed to do, the more important it becomes to verify, restrict and control what it does.
The OWASP GenAI Security Project publishes a threat-model-based reference on agentic AI, “Agentic AI: Threats and Mitigations”.
Source: OWASP — Agentic AI Threats and Mitigations.
9. The future of AI is about more than having a conversation
When generative AI first became widely popular, most people experienced it through a chat window.
You typed a question.
The model replied.
And that interaction remains enormously useful.
But the latest developments point towards a broader role for artificial intelligence.
Instead of simply explaining how to carry out a task, AI systems can increasingly help with the execution itself.
That doesn’t mean every job should be handed over to autonomous software.
Some tasks require human judgement, contextual understanding, legal accountability or professional expertise.
Other tasks are straightforward enough that ordinary automation remains the better solution.
The real transformation lies in combining language models, tools, software and human oversight into systems capable of solving problems more effectively.
For many people, the next important question won’t be:
“What can this AI tell me?”
It will be:
“What can this AI actually do, and how can I check that it’s done it properly?”
That shift may prove to be one of the defining developments in the practical use of artificial intelligence.
10. Frequently asked questions about LLMs, AI Agents and Agentic AI
### Is ChatGPT an LLM or an AI agent?
ChatGPT is an AI application powered by language models.
Depending on the features being used, it can act as a conversational assistant or provide agentic capabilities through tools, integrations and task execution.
GPT refers to the model family. ChatGPT is the product through which users access different AI capabilities.
### What’s the difference between an AI Agent and Agentic AI?
An AI Agent is generally a system that carries out actions towards a defined goal.
Agentic AI describes the approach and capabilities that enable planning, tool use, decision-making and adaptive execution.
A single agent can be agentic, while an agentic system can also involve multiple agents.
### Can an LLM access the internet on its own?
Not necessarily.
Live internet access depends on the tools and integrations available to the application using the model.
Without access to current external sources, a model may generate answers from its training and conversation context, but that’s not equivalent to carrying out a live web search.
### Do you need programming skills to use AI agents?
Not always.
Some AI products offer visual interfaces that allow people to use agents and configure certain workflows without writing code.
However, building custom integrations or complex business systems may require technical skills.
### Is Agentic AI better than generative AI?
They’re not directly competing categories.
Generative AI is particularly useful for producing and transforming content.
Agentic AI uses AI capabilities to plan, coordinate and carry out processes.
Many practical applications combine both.
The best approach depends on the problem you’re trying to solve.
### Are AI agents completely autonomous?
Not necessarily.
Some agents work within tightly defined boundaries and require approval at key stages.
Others can perform longer sequences of actions independently, subject to their tools, permissions and operational limits.
Autonomy exists on a spectrum. It isn’t simply an on-or-off feature.
Conclusion: knowing how to use AI isn’t the same as understanding what it can do
If you’ve made it this far, you’ve probably realised that artificial intelligence is about much more than a chat window capable of answering questions.
LLMs have transformed the way we work with language and information.
AI agents add tools and the ability to take action within digital systems.
Agentic AI enables more flexible planning, coordination and execution, with different levels of autonomy.
But perhaps the most important development won’t be creating systems that do absolutely everything on their own.
It will be building systems that can complete useful tasks, produce verifiable results and operate within boundaries set by the people using them.
The question is no longer simply whether AI can write an article, analyse an image or explain a complicated subject.
It’s how far AI can help turn an intention into a real, reliable outcome.
Thought you knew what artificial intelligence was? You might only just be getting started.
What we could not confirm
We could not open OpenAI’s pages (GPT-6.1 Terra, the agents guide and the GPT-6 Astra page). GPT-6 Astra was checked through CNBC and GPT-6.1 Terra through press reports, and we could not confirm OpenAI’s context window or pricing. The Gemini 4 Carbon performance claims are Google’s. Artificial Analysis is an independent evaluator, but scores depend on each model’s settings and change. We tested no model. The travel, restaurant, spreadsheet and newsroom examples are illustrative.
### Sources and further reading
Editorial research checked against sources available on 29 October 2026.
- Anthropic — Building Effective Agents
- Anthropic — Trustworthy Agents in Practice
- Anthropic — Claude Fable and models documentation
- Google — Introducing Gemini 4 Carbon
- CNBC — OpenAI announces rollout of GPT-6 Astra
- BleepingComputer — OpenAI launches GPT-6.1 Terra
- Artificial Analysis — Intelligence Index v5
- Artificial Analysis — model leaderboard
- Artificial Analysis — Gemini 4 Carbon
- Anthropic — Model Context Protocol
- Google Developers — Agent2Agent Protocol
- OWASP — Agentic AI Threats and Mitigations
- IBM — What are large language models
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