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Agentic RAG

Agentic RAG is a retrieval-augmented generation architecture in which an LLM agent — not a fixed pipeline — decides what to retrieve, when, how, and whether the answer is good enough. Instead of a single query → retrieve → answer flow, an agent plans, issues multiple searches, evaluates its own partial answers, and retries until it's confident.

Agentic RAG is a retrieval-augmented generation architecture in which an LLM agent — not a fixed pipeline — decides what to retrieve, when, how, and whether the answer is good enough. Instead of a single query → retrieve → answer flow, an agent plans, issues multiple searches, evaluates its own partial answers, and retries until it's confident.

Why It Matters

Classic RAG has a ceiling: one query, one retrieval, one answer. That works for straightforward lookups but fails on complex questions, ambiguous queries, or tasks that require reading multiple documents across steps. Agentic RAG breaks that ceiling by giving the model autonomy over the retrieval process itself. 2024–2025 benchmarks from LangChain, LlamaIndex, and Anthropic show agentic RAG outperforming vanilla RAG by 20–40% on multi-document QA, fact verification, and research tasks. It's the architecture behind Perplexity's deep research, ChatGPT's browsing, and most enterprise "chat with your docs" systems that actually work.

How It Differs from Standard RAG

Standard RAG:

  1. User asks question
  2. System embeds question, retrieves top-k
  3. Model generates answer from retrieved context

One shot. Static. No retry.

Agentic RAG:

  1. User asks question
  2. Agent plans: "What do I need to know to answer this?"
  3. Agent calls retrieval tool with a specific sub-query
  4. Agent reads results, decides what's missing
  5. Agent calls retrieval again with refined queries (loop)
  6. Agent decides when it has enough and drafts an answer
  7. Agent optionally self-critiques and revises
  8. Final answer delivered

Multi-step. Adaptive. Can backtrack.

Core Components

Planner: An LLM (often the same one answering) that breaks the question into retrieval steps.

Retrieval tools: Vector search, keyword search, API calls, database queries — the agent can pick among them.

Memory: The agent tracks what it has already seen to avoid redundant calls.

Self-critique loop: The agent evaluates whether its draft answer is well-grounded, and if not, retrieves more.

Exit condition: Either a confidence threshold, a step budget, or an explicit "I have enough" signal.

Common Patterns

ReAct (Reasoning + Acting): The agent alternates between thinking and calling tools in a single scratchpad. The original agentic pattern from Yao et al., 2022.

Plan-and-execute: The agent writes a multi-step plan first, then executes each step. Better for deep research; slower for simple questions.

Self-RAG: The model decides dynamically whether retrieval is needed at all. If the question is trivial, it skips retrieval entirely.

Multi-agent RAG: Multiple specialized agents (searcher, reader, critic, writer) collaborate. Powerful but expensive.

When to Use It

Complex research tasks: "Summarize the 2025 Q4 earnings trends across FAANG."

Multi-document fact-checking: Cross-referencing claims against several sources.

Ambiguous questions: Where the right retrieval depends on disambiguation ("Which Jordan?").

High-stakes outputs: Legal, medical, financial — where a single retrieval might miss a critical context.

Agent-integrated chat: Assistants that also take actions (send email, schedule meeting) based on what they learn.

When Not To Use It

Simple FAQ lookups: One retrieval is enough; agentic loops add latency and cost.

Tight latency budgets: Chat UIs with a 1-second target can't afford multi-step agent loops.

Cost-sensitive volume: Every loop iteration is another inference call. At scale, agentic RAG can be 5–10× more expensive than standard RAG.

Well-indexed small corpora: If your data is small enough that one dense retrieval always finds the right passage, don't add complexity.

Trade-offs

Latency: Multi-step loops mean responses take 5–30 seconds, not under 1 second.

Cost: Each step is an LLM call plus a retrieval call. Budget accordingly.

Determinism: Agentic systems are harder to debug and reproduce because the agent can take different paths on different runs.

Evaluation: Measuring "is the retrieval good" is hard when the retrieval plan is dynamic. You evaluate final answers, not intermediate decisions.

Common Mistakes

Forcing agents on simple questions: Overkill inflates cost without improving quality.

No step budget: An unconstrained agent can loop for minutes. Cap steps at 5–10.

No memory: Without tracking past retrievals, the agent repeats work.

Weak planner: If the planning LLM is too small or poorly prompted, plans are bad and loops waste calls.

Skipping eval: Because agent traces are noisy, teams skip formal eval — then can't tell if their system is actually better than vanilla RAG.

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