What is agentic RAG? The 2026 explainer

Agentic RAG is what classic RAG became when retrieval got smart enough to be an agent loop. Instead of one retrieval + one answer, the agent plans, queries, evaluates, reformulates, re-queries, and synthesizes — closer to how a human researcher actually works.

TLDR

Classic RAG: search → top-K chunks → stuff into prompt → answer. One shot.

Agentic RAG: decompose question → query → judge results → reformulate → query again → cross-source synthesis → cited answer. Iterative.

The agentic version is 5-20× more expensive per query but produces materially better answers on hard questions.

Read the long version in the agentic RAG glossary entry.

The shape of agentic RAG

A typical agentic RAG loop:

1. Decompose the user's question into sub-questions
2. Query retrieval (vector search, web search, structured DB) for each sub-question
3. Judge each result — relevant? trustworthy? fresh enough?
4. Reformulate queries that returned weak results, search again
5. Cross-source synthesize — reconcile contradictions, weight sources
6. Cite — link claims to source documents
7. Loop — if the synthesized answer isn't confident, go back to step 2

Latency: 10-60 seconds for typical queries (vs. 1-3 seconds for classic RAG).

Cost: $0.05-0.50 per query (vs. ~$0.01 for classic RAG).

Why agentic RAG matters

Classic RAG fails on:

• Multi-hop questions — "What did the CEO of the company that acquired Anthropic's biggest competitor say about AGI?" requires 3 hops, not one
• Comparative questions — "Compare Stripe + Adyen + Checkout.com on developer experience" requires querying 3 sources + cross-source synthesis
• Open-ended research — "Investigate the regulatory landscape for AI agents in healthcare" requires multiple queries + synthesis
• Ambiguous queries — "Find a competitor doing better than us in customer support" requires understanding the user's context + reformulating

Agentic RAG handles all of these. The price is latency + cost.

When to use which

Use classic RAG when:

• Simple lookups: "what's our return policy"
• Single-hop questions with a clear answer
• Latency-sensitive surfaces (chatbots, voice agents)
• Cost-sensitive at high query volume

Use agentic RAG when:

• Multi-hop or comparative questions
• Research-flavored workflows (executive briefs, due diligence)
• High-stakes answers where accuracy matters more than latency
• Users explicitly opt-in to deeper research ("Deep Research" UX)

Most production systems run both — classic RAG as the default, agentic RAG triggered by query complexity heuristics or explicit user request.

Leading implementations in 2026

Consumer products

• Perplexity Labs — strong agentic search with citations
• OpenAI Deep Research — multi-step research with extended thinking
• Gemini Deep Research — Google's flavor, integrated with Workspace data
• Anthropic Research mode — newer, ships with Claude.ai

Enterprise products

• Glean — agentic RAG over internal knowledge bases
• Hebbia — agentic RAG for financial research
• Elicit — agentic RAG for academic research

Developer frameworks

• LangGraph with retrieval nodes
• LlamaIndex Agents with query engines
• CrewAI with retrieval tools

All are credible. Pick by your stack + ergonomic preferences.

Architecture patterns

The two dominant patterns in 2026:

Pattern 1: Plan-and-execute

plan → [query_1, query_2, query_3] → execute_all_in_parallel
→ judge_results → optional_reformulate → synthesize → cite

Faster (parallel queries), works well when sub-questions are independent.

Pattern 2: ReAct-style iterative

reason → act (query) → observe → reason → act (re-query)
→ observe → ... → synthesize → cite

Slower (sequential), works better when later queries depend on earlier results.

Most production systems use a hybrid: plan-and-execute for the first round, ReAct-style iteration for refinement.

Cost economics in 2026

For a typical agentic RAG query:

• 5-10 LLM calls (planner + judges + synthesizer)
• 3-8 retrieval calls (vector search + web search)
• Total cost: $0.05-0.50 per query depending on model + retrieval choice

For 10K queries/month: $500-5,000/month. Materially more expensive than classic RAG at the same volume — justified only by the answer-quality difference.

Common mistakes

1. Using agentic RAG for everything. Most queries don't need it. Triage; route by complexity.
2. Skimping on judging. The "judge each retrieval" step is what makes agentic RAG work. Skip it and you've just made classic RAG slower + more expensive.
3. Ignoring citation quality. The cited-source pattern is the trust signal. If citations are wrong or fabricated, the whole system loses credibility.
4. Not setting a budget. Agentic RAG can spiral — a bad query can drive 20+ LLM calls. Hard-cap retries.

See also

• Agentic RAG glossary entry
• RAG vs fine-tuning vs agents
• How to use AI for research

Bottom line

Agentic RAG is the right pattern for hard research-flavored questions. Don't use it for simple lookups (waste of latency + cost). Build a triage layer that routes simple queries to classic RAG and complex queries to agentic RAG. Most production systems converge on this hybrid pattern.

Read the agentic RAG glossary entry →