When to use agentic retrieval and when direct search is enough

On July 24, 2026, AWS described how AgenticRetrieveStream in Amazon Bedrock Managed Knowledge Base can turn a query into a small investigation: it can plan steps, retrieve material from a knowledge base, and return a record of what it did. The useful point is not that every question should go through an agent. It is learning to separate a request for a local fact from one that requires breaking down, checking, and combining several pieces of evidence.

The 60-second decision

Start by writing down the result the user needs. If they want one specific fact that should live in one document —for example, “what is the cancellation period in contract X?”— direct retrieval is usually the sensible option. Bedrock’s Retrieve API searches a knowledge base for chunks relevant to a given query. There are fewer steps to inspect, less delay, and a shorter path from question to source passage to answer.

If the request contains linked tasks —“compare the three contracts, identify which exception affects our office, and cite each clause”— one search may retrieve relevant text without resolving how to combine it. That is where agentic retrieval can be worth evaluating. AWS describes it in its technical announcement as a process that plans and executes retrieval in order to answer complex queries. That is a capability, not a guarantee that the final answer is correct. And AWS puts numbers on when it pays off: on MuSiQue, a benchmark of questions that require chaining several documents, agentic retrieval improves recall by 22.8% on two-hop questions, 31.9% on three-hop, and 37.3% on four-hop. The pattern is the lesson: the harder the question, the bigger the gain — and, conversely, on a single-hop question the agent adds cost while adding almost nothing.

The transferable skill is straightforward: do not ask, “do we have agents?” Ask, “does this task require finding a fact, or coordinating several verifiable searches?”

What changes when retrieval plans steps

The AgenticRetrieveStream documentation specifies that the operation streams events while it handles a request. Those events can include the response and trace information. The trace matters more than the agentic label: it gives a reviewer a way to inspect intermediate queries, considered results, and the evidence behind the final synthesis. The trace is not decoration: AWS emits it as an ordered sequence of named events — speculative retrieval, planning, retrieval or full-document expansion, and result — so that every turn of the loop is visible. That log is what turns a black box into something auditable.

Picture an internal policy split across a travel handbook, an expense rule, and a local addendum. A direct query may retrieve all three texts but may not decide which rule applies first or notice that a geographic exception changes the outcome. An agentic flow can split the work: find the general rule, locate the exception, and check whether the employee profile falls within it. That ordering is useful when the task has dependencies.

Do not mistake ordering for infallible understanding. A system can choose a weak subquery, retrieve an outdated passage, or combine text cleanly even though it does not answer the real situation. The output should therefore retain source links or identifiers and separate documentary quotation from inference. If you cannot inspect the evidence, the agent has only made a possible error harder to see.

Two APIs, two ways of answering

The technical difference between the two operations is not a manual footnote: it shapes how the answer plugs into a product. Direct retrieval is synchronous — one call, one response — and returns passages without generating text. Agentic retrieval is always streaming: it emits its events as they are produced and can optionally generate a written answer alongside the evidence. For an interface that shows the user progress, that stream is an advantage; for a batch process that only wants the final result, it is a complexity to manage.

The case where the agentic variant is clearly irreplaceable is multiple sources. A question spanning the HR handbook, the security policy, and a supplier contract does not live in a single knowledge base. Direct retrieval queries one; the agentic one splits the question across up to five, deciding which part goes where by reading each base's description, and assembles the evidence. That routing is real coordination work, not decoration — and it is exactly where a single similarity search falls short by design, not by bad luck.

A procedure you can follow

Before enabling an agentic mode, try this sequence on a real query:

1. Write the smallest version of the question that one document could answer. For example: “show the cancellation clause in contract X.” Run it through direct retrieval and inspect the returned passage.
2. List the missing dependencies. Do you need to compare versions, apply an exception, calculate a condition, or join separate documents? If not, do not add an agent for technical prestige.
3. If you do, define the expected result and the boundaries: allowed documents, effective date, and a requirement for citations. Use agentic retrieval for the compound task.
4. Review the trace and evidence before using the answer. A useful test is whether someone else could reach the same conclusion using only the cited passages. If not, the answer is not supported well enough.
5. Measure operational cost too, and in the right unit. AWS charges the managed agentic mode at 4 dollars per 1,000 agentic calls plus 1 dollar per 1,000 underlying retrieval calls, and warns that spending and latency are planned "by iterations, not by tokens alone": every turn of the loop costs money and time. Add the share of outputs that human review changes. A more elaborate answer is not worthwhile if it does not improve the decision.

What is documented, and what still needs testing

It is documented that Bedrock provides both direct retrieval and an agentic retrieval operation, and that the latter exposes an event stream and trace data according to request configuration. It is also clear that the product works with managed knowledge bases; it does not turn every external source into reliable evidence by itself — the agentic variant can query up to five managed knowledge bases at once, routing each question with a natural-language description of what each base holds.

What is not documented is that agentic mode will improve every knowledge base or every business metric. The effect depends on document quality, currency, and access rights, on the query, and on how the answer is evaluated. AWS does not turn a trace into an independent audit, or a citation into a correct conclusion.

The durable rule is to choose complexity you can inspect. For one fact, retrieve the passage and read it. For a compound question, let the system plan, but retain the plan, the sources, and the right to say “there is not enough evidence.” That makes agentic retrieval less of a buzzword and more of a tool with one clear condition: it must reveal, rather than hide, the path to an answer. And the MuSiQue figure gives the pocket rule: if your question is single-hop, pay for a single hop; the agent earns its keep where several documents must be joined, and its gain grows with exactly the difficulty a human finds hardest to resolve by hand.