Platform · Agentic RAG

Agentic RAG

The answer your engineers need is rarely in one document. It is a chain across a patent, a failure report, a simulation log and a line of code. Agentic RAG walks that chain, verifies every link, and returns a grounded, cited decision.

01The problem

Take a chip maker like Intel. Decades of hard-won know-how sit scattered across patents, internal failure reports, simulation logs and source code, so a compound question, which past project used a material compatible with process X and also held loss below Y, and why, has no single document to retrieve. Flat vector search returns the closest paragraph, not the reasoned chain across sources, which is why Intel has itself patented query-aware, graph-guided retrieval to attack exactly this.

One question, many sources, one chain. Take a chip maker like Intel as an example. The answer to a compound question is a path across patents, failure reports, simulation logs and code, not a single passage, so retrieval has to walk the graph.

02How we solve it

Retrieve the chain, not the closest paragraph.

Agentriq treats retrieval as reasoning: decompose the question, traverse a knowledge graph across every source, then keep only the evidence that survives verification.

Retrieval as a verified funnel. Each stage narrows a wide, meaning-based recall down to the few sources that survive verification, with a loop back whenever the evidence is thin.

GraphRAG (knowledge-graph retrieval)

Facts, documents and their relations become a graph, so retrieval traverses connections across patents, reports and logs instead of scoring one passage at a time.

Query decomposition and multi-hop

A compound question is split into sub-questions, each retrieved and answered in turn, then recomposed, so the system reasons across several hops rather than one shot.

HyDE and multi-query expansion

The model drafts a hypothetical answer and several query rewrites to widen recall, surfacing sources that share the meaning but not the exact vocabulary.

Cross-encoder reranking and compression

A cross-encoder rescores every candidate against the precise query and compresses it to the load-bearing sentences, cutting noise and context tokens before the model reads it.

03What it produces

Grounded, cited, benchmark-anchored answers.

The gap widens on hard questions. On published RAG benchmarks the two approaches tie on simple lookups, then graph-guided agentic retrieval pulls ahead as questions require multiple hops and cross-document synthesis.

Every answer ships with its citation trail, and on complex multi-hop questions graph-guided retrieval reaches roughly 53 percent accuracy against 43 percent for flat vector RAG on published benchmarks, a gain of about ten points where it counts most.

04The agentic loop

A retrieval loop that verifies itself.

Agentriq wraps the whole pipeline in a closed agent loop that decomposes, retrieves, self-checks against sources, and re-queries until the evidence is strong enough to decide.

From work to a verified decision. The same closed agent loop that turns a scattered corpus into a grounded, cited answer.

01Decompose the compound question into sub-questions and plan a traversal across the knowledge graph.
02Retrieve iteratively: HyDE and multi-query expansion widen recall, GraphRAG walks the relations, then cross-encoder reranking and contextual compression keep only the load-bearing evidence.
03Self-RAG verifies each claim against its retrieved source and gates out anything unsupported, re-querying wherever the evidence is thin.
04A judge agent weighs the assembled evidence and decides whether the answer is grounded enough to return or needs another hop.
05The verified answer, its citation trail and any new relations are written back to the graph, so the next query starts from stronger ground.

05Tooling

Simulation, solvers & frameworks.

Get started

From engineering problems to autonomous solutions.

Talk to our engineers john.ferderigos@agentriq-sa.com