Recognizes an agent, its tools and its degree of autonomy

Concepts · competency concepts/recognizes-agents

Taught in: the Concepts course

Draws on: What an agent is, Capabilities and limits

Learning objectives

Tells a chat assistant from an agent by what it can do unprompted (base)

Claim	Why	Example
An assistant produces text for a person to act on, and an agent takes actions through tools and decides what to do next on its own.	The two need different handling, and treating an agent as a chat box means not noticing what it did.	A chatbot that drafts an email is an assistant. A tool that reads the inbox, drafts replies and sends them without another prompt is an agent.
The learner tells the difference by asking what the system can do without another prompt, whatever its interface looks like.	A chat window can front an agent, and a button can trigger a plain model call, so the interface says nothing.	Two products both show a chat box, and the learner asks "does it run the query itself or give me the query?" and classifies them differently.
The learner names the tools an agent has before judging what it can do.	An agent's reach is its tool list, and a research agent with a browser tool is a different risk from one with a browser and an email tool.	Reading the product page, the learner lists "web search, file read, calendar write" and notes that calendar write is the one that acts on others.

Served by: Assistant or agent?

Places a product or workflow on the autonomy scale (base)

Claim	Why	Example
The learner places a workflow by who decides the next step: a person at every step, the system with approval at some steps, or the system alone.	Autonomy is about who makes the decisions, and the amount of text the model produces says nothing about it.	Autocomplete in an editor is low autonomy even when it writes a whole function, because the person accepts each suggestion.
The learner can say what would move a workflow one step up or down the scale.	Autonomy is a design choice, and seeing the dial makes the choice visible rather than accepting the product's default.	"If the agent also merged the pull request instead of opening it, it would move from supervised to autonomous."
The learner places the same product differently depending on how it is configured.	Most agent products have permission settings, and the setting decides the autonomy.	A coding agent in "ask before each edit" mode and the same agent in "auto-accept" mode go on different points of the scale.

Served by: Who decides the next step

Describes the observe, think, act loop and the tools in it (base)

Claim	Why	Example
The learner describes the loop as: the agent observes (reads a result), thinks (decides the next step), acts (calls a tool), and repeats until it decides it is done.	Naming the loop makes the agent's behavior predictable and points at where a step can go wrong [1].	Watching a coding agent, the learner narrates "it ran the tests, read the failure, and now it is editing the file".
The learner can name which tools were used in a given run and what each observation was.	A tool call is where the agent touches the world, so the tool list is the list of things that can have side effects.	From a transcript the learner lists "searched the web twice, read one file, wrote one file" and notes the write is the step to check.
The learner knows the loop ends either because the agent decides it is done or because a limit stops it, and can tell which happened.	"Stopped because the budget ran out" and "stopped because it is finished" need different follow-up.	An agent that ends with a half-written file and no summary hit a limit, so the learner checks the log rather than trusting the output.

Served by: Watch a tiny agent work

Alignment

Framework	Code	Asks	Objectives here
AI Fluency 4D (Dakan and Feller)	Delegation	Decide what to hand to AI, which tool fits, and how much autonomy to give	places-on-autonomy-scale

References

1. Anthropic. Claude Platform 101. Claude Academy. Course. Academy claude-platform-101