Agentic RAG
A reasoning loop replaces the fixed retrieve-then-answer flow. The agent thinks, picks a tool (vector search, keyword filter, summarize), observes the result, and repeats until it has enough evidence to answer. Retrieval happens only when the agent decides it's needed.
Stage-by-Stage Data Flow Explorer
Select a phase from the controller below, then click individual step nodes to view their technical role, inputs, outputs, and mockup diagnostics data stream.
Agent Loop
Think → act → observe, repeated until the agent is confident in its evidence.
> THOUGHT 1: "I need to look up Ragiment's 2026 revenue. I will execute a vector search." > ACTION: vector_search(query="Ragiment Corp 2026 revenue")
Best suited for
Open-ended, multi-step questions where the right retrieval strategy isn't known up front.
Complexity
A reasoning loop that chooses and calls tools over several rounds — the most moving parts and the hardest pipeline to make reliable and predictable.
Relevance today
The frontier of RAG — powerful for genuinely complex tasks, but adopted carefully because of cost, latency, and non-determinism.
Where it's used
Research assistants
Break a question into sub-queries and gather evidence over several retrieval rounds.
Support triage
Decide per question whether to search, filter, summarize, or escalate.
Analysis copilots
Pick the right tool — search, filter, compute — for each step of a task.
Why it matters
- Adapts its retrieval strategy per question instead of following a fixed pipeline.
- Combines multiple tools (vector search, keyword filter, summarize) in a single answer.
- Naturally handles questions that need several rounds of evidence gathering.
Trade-offs & considerations
- The most expensive and slowest pipeline — multiple LLM calls per query.
- Non-deterministic by design, which makes it harder to test and guardrail.
- Needs careful iteration limits and tool design to avoid runaway loops.
Alternatives to consider
When Agentic RAG isn't the right fit, reach for one of these instead.