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After this lesson you canDescribe how an AI agent uses tools in rounds to reach a goal, and say which steps should wait for your approval.
The short answerAn AI agent is an AI that does things for you, not just talks. You give it a goal, and it takes the steps on its own, such as searching, clicking or filling in forms.
In simple words
- A chatbot answers you. An agent also acts for you.
- It works in rounds: plan, act, check the result, and go again.
- Tools give it hands: a web browser, your email or your calendar.
- It can make mistakes, so important steps should wait for your OK.
The short answerAn AI agent is a system in which a language model decides which steps to take and which tools to use to reach a goal. It runs in a loop: plan, act, observe the result, and decide again.
In simple words
- An agent combines a model, tools and instructions, and runs them in a loop.
- In a workflow, code fixes the steps; in an agent, the model chooses them.
- Open standards such as MCP connect one agent to many tools.
- More freedom means higher costs and errors that can add up.
The short answerAn agent is a language model inside a control loop. It gets a goal and tool schemas, emits structured tool calls, reads each result back into its context, and repeats until a stop condition. Reliability, cost and security belong to the whole system, not just the model.
Key points
- Tool calling: the model emits a call that matches a JSON schema, and the runtime executes it.
- Errors compound: 20 steps at 95% each give a clean run only about 36% of the time.
- Every result enters the context window, so long runs need memory, summaries and limits.
- Agent benchmarks depend on the harness, tools and budget, not only on the model.
1 One task, step by step
- Plan
- Act
- Observe
- Check
- Your OK
- GoalBook the cheapest train from Budapest to Vienna on Friday, under €60.
2 Who decides the next step?
- ChatbotYou decide every stepIt writes; you act on what it says.
- WorkflowCode fixes the stepsThe model fills in parts of a fixed process.
- AgentThe model decidesIt chooses the next step and the tool; code runs the tools and enforces limits.
- You
- Fixed code
- The model
3 Strongest models as agents now
- Claude Opus 5.5Anthropic · Silicon score 100.0 · 2 tests
- Claude Fable 5.1Anthropic · Silicon score 98.9 · 2 tests
- GPT-6 AstraOpenAI · Silicon score 98.5 · 2 tests
Words to know
- Agent
- An AI that takes steps on its own, using tools, to reach your goal.
- Tool
- Something the agent can use to act, such as a web browser or email.
- Workflow
- A setup where fixed code sets the steps and the model fills in parts.
From talking to doing
A chatbot writes an answer, and you decide what to do with it. An agent goes one step further and does the work itself.
You might say: “Find a train to Vienna on Friday under 60 euros, and book it.” The agent searches, compares the prices and books a ticket. OpenAI describes agents as systems that complete tasks for you on their own.
Some AI tools sit in between. In a workflow, a programmer fixes the steps in advance, and the AI only fills in parts, such as writing each reply.
Working in rounds
An agent works in small rounds. It makes a plan, uses a tool, looks at the result and decides what to do next.
It repeats this until the job is done, or until it gets stuck and stops. Each round is a new chance for a mistake, and one early mistake can spoil the rest.
Tools give it hands
On its own, an AI model can only write text. Tools let it act: a browser to open web pages, an email account to send messages, a card to pay.
Every tool you connect gives the agent more power, and more ways to go wrong. Good agents ask you before they pay, send or delete anything.
Try it yourself
Click through the train booking in the diagram, one step at a time. At “Ask first”, say why the agent stops before it pays.
Check yourself
You ask an AI to find a train to Vienna under 60 euros and book it. What does an agent do that a chatbot does not?
The agent’s search shows three trains, and the cheapest one is sold out. What does the agent do next?
Your agent is booking the train for you. Which step should wait for your OK?
Words to know
- Agent
- An AI that takes steps on its own, using tools, to reach your goal.
- Tool
- Something the agent can use to act, such as a web browser or email.
- Workflow
- A setup where fixed code sets the steps and the model fills in parts.
Workflow or agent?
Anthropic draws a useful line between two designs. In a workflow, the steps are fixed in code and the model fills in parts. In an agent, the model directs its own process and tool use.
Many jobs do not need an agent at all. Anthropic advises starting with the simplest setup that works, and giving the model more freedom only when simpler setups are not enough.
Model, tools, instructions
OpenAI’s guide to building agents names three core parts: the model that reasons and decides, the tools it can use to take action, and the instructions that set its rules.
Tools are described to the model as functions with a name and inputs. When the model wants one, it writes a structured request, and the software around it runs the tool and returns the result.
One plug for many tools
The Model Context Protocol, or MCP, is an open standard for connecting AI apps to data and tools. Its website compares it to a USB-C port: one plug for many devices.
Claude, ChatGPT and coding tools such as Visual Studio Code support it. Silicon AI News has an MCP connector too: it lets Claude search our checked stories but not change them.
Why agents need care
Each round of the loop can go wrong, and mistakes add up. Anthropic warns that more freedom for the model brings higher costs and errors that build on each other. It recommends testing agents in closed test areas, called sandboxes, with firm limits.
OpenAI’s guide adds that a person should approve sensitive or high-stakes actions, and actions that cannot be undone. The next lessons show real failures and the permissions that limit them.
Try it yourself
Click through the train booking in the diagram, one step at a time. At “Ask first”, say why the agent stops before it pays.
Check yourself
A support system always runs the same three steps in the same order, and a model writes each reply. Anthropic would call it a workflow, not an agent. Why?
Your agent decides it needs today’s weather, and it has a weather tool. What happens next?
Your agent has written replies to 40 customers and is ready to send them. Who should give the final OK?
The control loop
A typical runtime sends the model a system prompt, the goal and tool definitions, usually as JSON schemas. The model replies with text or a tool call, which the runtime executes and appends to the context.
The loop ends when the model reports success, a step or spending limit is reached, or a person steps in. OpenAI’s guide names two triggers for human help: repeated failures and high-risk actions.
Why reliability compounds
Per-step success rates multiply. If each of 20 independent steps succeeds 95% of the time, a clean run happens with probability 0.95 to the power of 20, about 36%.
Real agents notice and repair some errors, so they can beat this simple estimate. Still, the arithmetic explains why long tasks are hard, and why long-horizon tests such as MirrorCode and EBR-bench separate models so sharply.
Context, memory and cost
Every tool result enters the context window, so long runs get expensive and can push early instructions out of focus. Systems summarise, keep notes outside the context, or hand subtasks to sub-agents with fresh context.
Cost grows with tokens per step times the number of steps. An agent that keeps retrying a broken tool can burn a large budget, so production systems cap retries, time and spending.
Measuring agents
Agent benchmarks run models through multi-step tasks, in test environments or real sessions. Arena Agent scores real sessions, τ³-bench simulates bank support work with tools, and EBR-bench tests learning over repeated games.
Results depend on the harness, the code that runs the model through each task, and on the tools and time budget, as much as on the model. Our Silicon score combines independent agent tests and shows each one separately.
Try it yourself
Click through the train booking in the diagram, one step at a time. At “Ask first”, say why the agent stops before it pays.
Check yourself
A vendor says its agent gets each step right 95% of the time. Your task needs 20 independent steps, and no errors get fixed. How often will a run be clean?
Deep into a long run, your agent starts to ignore rules from its system prompt. What is the likely cause?
Your agent has failed the same login five times in a row. What does OpenAI’s guide suggest?
Sources
- Building effective AI agents (Anthropic, December 2024)
- A practical guide to building agents (OpenAI)
- What is the Model Context Protocol? (modelcontextprotocol.io)
This lesson was generated by AI systems under the editorial rules of Silicon AI News and checked against the sources it lists. The live parts come from our checked stories, trackers, model comparison and rules checker.