Agents and autonomy

Several agents working on one job with different responsibilities, which can help on large tasks and adds its own coordination problems.

A process where an AI decides the next step rather than following a fixed path, trading predictability for the ability to handle variation.

The parts agents play in a multi-agent setup, typically one planning and delegating while others carry out the individual pieces.

Splitting a large goal into steps small enough to act on, the step most agent failures can be traced back to.

The repeating cycle an agent runs of deciding, acting and checking the result, continuing until the goal is met or a limit stops it.

A toolkit for building agents that supplies the loop, the tool connections and the memory, so each project does not rebuild them.

Testing whether an agent completes real tasks correctly end to end, which is harder to measure than a single answer's quality.

A helper agent given one narrow piece of a larger job, keeping each part focused and its context manageable.

An agent that operates a web browser to complete tasks on sites that offer no other way to reach them.

An AI that works on a codebase across many steps, reading files and making changes, rather than suggesting one snippet at a time.

Confining what an agent can reach and change, so a mistake or a hijacked instruction is contained rather than loose in your systems.

An emerging way for agents from different vendors to talk to each other directly, aimed at the problem of tools that cannot cooperate.

How much an agent may do without asking, the setting that decides most of the value and most of the risk of using one.

The point where an agent passes work to a person or to another agent, and the moment worth designing most carefully.

An agent running in the background without anyone watching, which makes limits, logging and a stop control matter far more.