What Is Agentic RAG?

What Is Agentic RAG?

Agentic RAG is a retrieval-augmented generation pattern where an LLM agent orchestrates the retrieval process across multiple steps rather than doing one-shot retrieve-then-generate. The agent plans the search strategy, calls retrieval as a tool, examines the returned context, decides whether it has enough information, and either retrieves again with a refined query or proceeds to answer. It handles multi-hop questions (“what changed between version 2.1 and 2.4?”), cross-document synthesis, and ambiguous queries that demand clarification. The pattern is more capable than naive RAG — and more expensive, slower, and harder to debug.

Why It Matters in Production LLM and Agent Systems

Naive RAG breaks on three real-world query classes: multi-hop questions where one retrieval is not enough, ambiguous queries where the right context depends on a clarification, and cross-document questions that require synthesising chunks from different sources. Agentic RAG handles all three by giving the LLM agency over the retrieval process — but every step the agent adds is a new place the system can fail silently.

The pain shows up across roles. Engineers see traces with five retrieval calls, two of which returned junk, and the agent still produced a confident answer. Product managers see latency p99 in the tens of seconds because the agent looped on retrieval. Cost engineers see token spend per query 8x what naive RAG cost. SREs see infinite-loop incidents where the agent keeps reformulating the same query. Each of these is invisible without trajectory-level observability.

In 2026 agent stacks, agentic RAG is the default for any non-trivial knowledge-grounded task. Research agents, coding agents, and customer-resolution agents all use some form of agentic retrieval. The rise of the Model Context Protocol (MCP) standardises how agents call retrieval as tools, making agentic RAG portable across frameworks. But standardisation does not mean reliability — without per-step evaluation and trajectory scoring, an agentic-RAG system in production is a black box that mostly works and sometimes burns cost on a loop.

How FutureAGI Handles Agentic RAG

FutureAGI’s approach is to treat agentic RAG as both an agent problem and a RAG problem. The traceAI-langgraph integration captures every node and edge in a LangGraph-style agent — planner, retriever node, reflector, final generator — as typed spans linked into a single trace. traceAI-langchain and traceAI-llamaindex cover their respective agentic patterns. Each retrieval call inside the loop emits its own retrieve span, complete with retrieval.documents and retrieval.score attributes, so a five-step agentic-RAG trace is fully decomposable.

On the eval side, two layers run in parallel. fi.evals.RAGScore (or RAGScoreDetailed) evaluates each retrieval step independently — was the right context fetched at this step? fi.evals.TaskCompletion and GoalProgress score the trajectory as a whole — did the agent reach the user’s goal across all steps? fi.evals.Groundedness scores the final answer against the union of retrieved contexts.

Concretely: a research-agent built on LangGraph instruments with traceAI-langgraph, samples 10% of production traces into an evaluation cohort, and runs RAGScore on each retrieval step plus TaskCompletion on the full trajectory. When TaskCompletion drops, the dashboard shows whether retrieval steps were the failure point (low per-step ContextRelevance) or whether retrieval was fine but the planner kept asking the wrong sub-questions. That separation — retrieval failure vs reasoning failure — is what makes agentic RAG debuggable instead of magic.

How to Measure or Detect It

Agentic RAG demands trajectory-level signals on top of standard RAG signals:

• fi.evals.TaskCompletion: pass/fail on whether the agent reached its goal across the full trajectory.
• fi.evals.GoalProgress: 0–1 partial-credit score across multi-step plans.
• fi.evals.RAGScore per retrieve step: catches retrieval failures inside the loop.
• fi.evals.Groundedness on final answer: catches hallucinations in the synthesis step.
• Step count distribution: median and p99 step counts per trajectory — runaway loops show as p99 outliers.
• Token-cost-per-trace segmented by step type — exposes which pattern is burning budget.
• OTel attributes: agent.trajectory.step, retrieval.documents, tool.name — emitted by traceAI-langgraph.

from fi.evals import RAGScore, TaskCompletion

trajectory_score = TaskCompletion().evaluate(
    input="Compare our Q3 and Q4 ARR by segment.",
    output="Enterprise ARR grew 18% Q3→Q4; SMB grew 6%.",
    expected_output="Multi-segment quarterly comparison."
)
print(trajectory_score.score)

Common Mistakes

• Conflating agentic RAG with corrective RAG. Agentic RAG is general agent-driven retrieval. Corrective RAG is a specific pattern with a retrieval-evaluator triggering fixed fallback strategies. Different abstractions.
• No step cap on the agent loop. Without a max-steps guard, a confused agent will spin on retrieval until tokens run out.
• Scoring only the final answer. A trajectory can produce a fluent wrong answer through three good retrievals and one bad one — score per step.
• Skipping TaskCompletion on multi-hop tasks. Per-step ContextRelevance can all pass while the agent answers the wrong question. Trajectory eval catches that.
• Treating agentic RAG as a default for everything. A simple FAQ does not need an agent loop — it doubles cost and latency for no quality lift.