RAG & Retrieval Systems
The Problem
By hop three, the intermediate query has drifted from the original question. Key terms shift definition between source and retrieved content. The embedding model encodes both as near-identical vectors. And when retrieval comes up empty, the agent fabricates an answer instead of admitting the gap. Memory tracks which retrievals are stale. Anti-Deception forces honest "I don't know." Reasoning catches the semantic drift. Code verifies retrieval pipeline logic.
How Ejentum Solves It
One API call forces your model to track semantic fingerprints across retrieval hops and detect the moment a key term shifts definition between the query and the retrieved content. When the retrieval fails, the agent says so instead of fabricating an answer.
How Four Harnesses Protect Your Agents
Memory Harness
primaryTracks context state across retrieval hops. Detects when retrieved information contradicts previously established facts. Identifies which chunks are stale and which are current. Prevents context pollution from outdated retrievals.
Reasoning Harness
Detects semantic drift across retrieval hops. Forces the agent to verify that each reformulated query preserves fidelity to the original question. +16.4pp on simulation tasks.
Anti-Deception Harness
Forces honest "I cannot verify this" when retrieval fails or returns low-confidence results. Blocks fabrication of sources, citations, or facts to fill retrieval gaps. Zero hallucinations across targeted fabrication scenarios.
Code Harness
Verifies retrieval pipeline logic — embedding generation, chunk ranking algorithms, re-ranking heuristics. Catches bugs in the retrieval code that silently degrade result quality.
Run your next multi-hop retrieval through the API. See how the injection catches the query drift your embedding model misses.