Is your AI agent worth its tokens? We measured it with TigerGraph
Is your AI agent worth its tokens? We measured it with TigerGraph Tags: ai, rag, graph, python Everyone is bolting agents onto retrieval. Almost nobody asks what they cost. For the TigerGraph Agentic GraphRAG Hackathon, the guidebook states the real question: it is not whether agentic produces a better answer, but whether the extra reasoning and retrieval steps are worth the extra token…
Is your AI agent truly cost-effective? A study reveals that not all AI agents delivering Retrieval-Augmented Generation (RAG) or Graph Augmented Retrieval (GraphRAG) with added reasoning via an agent provide equal value. Thorough measurements using TigerGraph show that while agents improve answers for about 3% of questions, they add significant token costs for the other 97%.
The OCCAM tool assesses this by routing questions through different tiers - tier 0 uses a rule-based graph query with no LLM call, tier 1 requires a single planning call, tier 2 executes a full agent loop with re-planning, and tier 3 combines vector and BM25 document retrieval alongside an LLM reader. Most questions (98) are answered at tier 0 with zero tokens, as the graph directly holds relevant information.
On six paraphrased questions the rule-based tier couldn't match, all were answered correctly by tier 1 at around 830 tokens each. The study emphasizes that the true value lies in understanding which questions truly need an agent rather than blindly deploying one.
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