Agent is doing a lot of work in that sentence
The word has been stretched to cover everything from a prompt template to an autonomous system. A working definition that stays useful: an agent takes a goal rather than a question, decides what to do next based on what it has found so far, uses tools to act on the world, and stops when the goal is met or a budget is spent.
By that definition, a chat interface answering from its training data is not an agent. A program that reads six job boards, notices which companies posted new roles, and writes them into a record is, even though it contains no clever reasoning at all.
Why several narrow agents beat one broad one
A single general agent asked to "research this company" produces output whose quality nobody can predict, because nobody can say which sources it happened to read. Splitting the work into specialists changes that: one agent reads job boards, another reads pricing captures, another reads filings, another decides what is worth keeping.
The benefits are practical rather than theoretical. Each agent can be tested against its own sources. A failing source degrades one agent rather than the whole run. And the output can name which agent produced each finding, which is the difference between a report you can audit and a report you have to trust.
The failure modes to check for
Three recur across every multi-agent research system, and all three are quiet:
- Fabricated citation. The model echoes back a URL that looks right and does not resolve, or resolves to something that does not say what was claimed. The fix is mechanical: fetch the source and bind the claim to it before the model is involved, rather than asking the model to remember it.
- Duplicate counting. The same event arrives from five sources and is reported as five findings, which makes a quiet week look busy.
- Silent source failure. A source goes down or changes shape, the agent that depends on it returns nothing, and the report simply has less in it. Without per-source reporting, nobody notices for weeks.
What to ask of any agent system
Which agent produced this finding, which source did it read, and what did the other agents fail to do on this run. A system that can answer all three is one you can debug. A system that presents a single confident narrative with no seams is one where you will find out about a broken source when a customer corrects you.