# Retrieval, Reconsidered: Agentic RAG, GraphRAG and the Semantic Layer

> Agentic RAG turns retrieval into a planning problem, and the data platforms answer with semantic layers whose accuracy claims are, so far, their own.

- Canonical: https://aiagentinfra.com/articles/agentic-rag-graphrag-semantic-layer
- Author: Ryan Elliott Dennis
- Category: Memory & Knowledge
- Kind: Reference article
- Last verified: 2026-09-04
- Keywords: agentic RAG, GraphRAG, semantic layer, knowledge layer, Snowflake Cortex, Databricks Agent Bricks, enterprise context, retrieval for agents, context engineering, Glean

> "transform tokens into actual economic value" — Alex Karp, Chief Executive Officer of Palantir (Palantir second-quarter 2026 earnings release, Aug. 3, 2026)

Palantir reported second-quarter 2026 revenue of $1.935 billion, up 93% from a year earlier, with U.S. commercial revenue of $764 million growing 149%, in an earnings release filed with the Securities and Exchange Commission on Aug. 3, 2026, in which Alex Karp, the company's chief executive, described its business as helping customers "transform tokens into actual economic value." That verb, transform, names the whole retrieval problem. Tokens acquire value when a model reads the right context at the right moment, and agentic RAG, retrieval that an agent plans, executes and revises across a task in place of a fixed embed-search-generate pipeline, is the mechanism by which enterprises now attempt that conversion at scale. The data platforms have noticed. Databricks, Snowflake, Glean and Google each shipped or renamed a semantic layer in the 12 months to September 2026, and the accuracy numbers attached to those layers are, to the last decimal, the vendors' own.

## From Pipelines to Plans: How Agentic RAG Reframes Retrieval

Classic retrieval-augmented generation is a pipeline: embed the query, fetch the nearest chunks, stuff them into the prompt, generate. Agentic RAG is a loop. The model decides whether to retrieve, composes the query, inspects what comes back, reformulates, retrieves from a second source and checks the answer against the evidence before it commits, which moves retrieval from a preprocessing step into the plan itself. O'Reilly's "The AI Agents Stack (2026 Edition)," written by Paolo Perrone and published June 8, 2026, calls the surrounding discipline context engineering and reduces it to one design question: "What do you stuff in-context versus what do you retrieve on demand?" Reasoning models sharpen the question from the other direction. Perrone notes that agents which previously needed multistep chains can now solve problems in a single reasoning call, so the retrieval plan increasingly unfolds inside one long inference in place of a chain of short ones.

Quality is the reason the loop matters. LangChain's "State of Agent Engineering" survey of 1,340 practitioners, fielded Nov. 18 to Dec. 2, 2025, ranked quality as the top barrier to production at 33%, ahead of latency at 20%, with cost receding as a concern; the same survey found 59.8% relying on human review and 53.3% on LLM-as-judge methods to evaluate outputs. Retrieval is where quality is won or lost, because a model that reasons well over the wrong context produces a fluent error. Distribution has standardized in parallel: the Model Context Protocol reached 97 million monthly SDK downloads and more than 10,000 servers by the time Anthropic donated it to the Linux Foundation's Agentic AI Foundation on Dec. 9, 2025, and retrieval tools increasingly arrive as MCP servers, which is why Glean now benchmarks itself against them.

## GraphRAG and the Entity Graph: When Relationships Beat Embeddings

Entity graphs answer a specific weakness of vector search: questions that hop across entities. Perrone's stack lists Neo4j and GraphRAG beside pgvector in the memory-and-knowledge tier and describes the graph approach as following relationships between entities as the alternative to matching embeddings, which is the capability that multi-hop enterprise questions demand, such as which supplier's contract governs the invoice an agent is about to dispute. The cost is construction. Building a graph means extraction inference over every document, plus maintenance as documents change, so the graph route trades a large fixed cost for lower marginal retrieval cost, while vector retrieval trades cheap indexing for repeated, expensive context stuffing. Which side wins depends on query mix and update rate, and the honest reading in September 2026 is that public, independent measurements comparing the two on enterprise corpora remain scarce.

The semantic layer is the third option, and the one the data platforms are betting on. A semantic layer defines business entities such as customer, order and margin with governed definitions, lineage and access rules, so an agent retrieves meaning that has already been agreed upon in place of raw rows it must interpret. GraphRAG discovers structure; a semantic layer declares it.

## Semantic Layers: Snowflake Cortex, Databricks Agent Bricks and the Convergence

Databricks and Snowflake held their 2026 summits weeks apart and, according to PointFive's summary of both events, converged on the same thesis: agents need a semantic context layer above the tables. PointFive's account credits Databricks with more than 100,000 agents built on its platform and more than 1 quadrillion tokens processed a year, and records the launch of Genie One alongside the Agent Bricks product line. The company's own Aug. 13, 2026, press release put its revenue run rate at $7 billion, growing more than 80% year over year, with 20,000-plus organizations, 70% of the Fortune 500, more than 1,000 customers above $1 million in annual run rate and a Lakebase run rate of $100 million, and it announced $5 billion in new funding at a $190 billion valuation. Snowflake renamed Snowflake Intelligence to CoWork and Cortex Code to CoCo and introduced Cortex Sense, a semantic layer for which the company claims an improvement in agent accuracy from 24% to 86%, as relayed by PointFive. That claim is vendor-published, its test set and methodology remain to be released, and a 62-point jump on any benchmark should be read as a statement about how badly agents perform on bare tables as much as a statement about Cortex Sense.

Google and Palantir complete the picture from opposite ends. At Cloud Next on April 22, 2026, Google rebranded Vertex AI as the Gemini Enterprise Agent Platform and paired it with Workspace Studio and an Agent Designer, according to The Next Web, positioning its knowledge layer inside a general agent platform; Palantir's growth, with U.S. commercial revenue up 149% and full-year guidance of $8.15 billion, is the market's clearest signal that an opinionated semantic model of the enterprise, which Palantir markets as an ontology, commands a premium once agents need it.

| Platform | Knowledge-layer product | Scale evidence (date) | Status of accuracy claims |
|---|---|---|---|
| Databricks | Agent Bricks, Genie One, Unity Catalog | 100,000+ agents; >1 quadrillion tokens/yr (summit 2026, via PointFive); $7B run rate (Aug. 13, 2026) | Company-reported adoption figures |
| Snowflake | Cortex Sense, CoWork, CoCo | Renames and launches at Summit 2026 (via PointFive) | Vendor claim: 24% → 86% agent accuracy; method pending |
| Glean | Enterprise context platform with MCP-served retrieval | $300M ARR (May 28, 2026) | Vendor benchmark: 2.5× preferred, 30% fewer tokens vs off-the-shelf MCP tools |
| Palantir | Ontology (Foundry, AIP) | Q2 2026 revenue $1.935B, +93% (Aug. 3, 2026) | Financial results in place of benchmarks |
| Google | Gemini Enterprise Agent Platform, Workspace Studio | Cloud Next, April 22, 2026 (via The Next Web) | Platform launch; accuracy figures to come |

## Enterprise Context as a Business: Glean, MCP Tools and the Token Bill

Glean is the pure play. On May 28, 2026, the company said annual recurring revenue had passed $300 million, up from $100 million roughly 15 months earlier, that its count of Fortune 500 customers had nearly doubled year over year and that weekly active users ran at 45% of monthly actives, and it published a benchmark in which its retrieval was "2.5x preferred, with 30% fewer tokens than off-the-shelf MCP tools," with preference rising from 66% on simpler tasks to 73% on complex, multi-step queries. The release cites the benchmark and links to details, but the document itself omits the evaluators, the models and the sample size, so the 2.5x figure belongs in the vendor-published column beside Snowflake's 86%.

Token volume turns retrieval efficiency into a line item. Microsoft said on its July 29, 2026, earnings call that the number of Azure AI Foundry customers at an annual run rate of 1 trillion tokens had quadrupled year over year, across a base of more than 100,000 Foundry customers; Gartner forecast on Aug. 10, 2026, that inference would absorb 55% of the $42 billion enterprises spend on AI-optimized infrastructure-as-a-service in 2026, or $23.3 billion, rising to 59% in 2027. At those volumes a retrieval layer that trims 30% of tokens per query, if Glean's figure survives replication, is a budget decision. Caching pulls the other way: Anthropic cut cache-read pricing for Claude Fable 5.1 to $0.25 per million tokens on Sept. 1, 2026, a 75% reduction that makes a large standing context cheaper to carry and forces every retrieval layer to beat a lower bar. Retrieval sources have meanwhile acquired price tags of their own; Cloudflare's Monetization Gateway, launched July 1, 2026, and extended on Aug. 4 with agent wallets, charges agents for web pages, datasets, APIs and MCP tools through the x402 protocol, according to Search Engine Journal's Aug. 12, 2026, report.

## Governance of the Knowledge Layer: Contracts, Credentials and Contamination

A semantic layer is a contract, and contracts need enforcement. Three risks concentrate at the knowledge layer. Credentials come first: the Salesloft Drift breach of Aug. 8 to 18, 2025, documented by Google's Threat Intelligence Group on Aug. 26, 2025, used stolen OAuth tokens to bulk-export Salesforce data and harvest AWS keys, Snowflake tokens and passwords, a reminder that whatever an agent's retrieval layer can reach, an attacker holding its token can reach too. Contamination comes second: OWASP's Top 10 for Agentic Applications, released Dec. 9, 2025, lists Memory and Context Poisoning as ASI06, and retrieved context is the largest poisoning surface an agent has, since every indexed document is a potential instruction. Definitions come third: a semantic layer whose "margin" differs from the finance department's "margin" produces confident, governed, wrong answers, which is why catalog-level lineage and access control sit underneath Databricks' agent products, with Unity Catalog named in the company's Aug. 13 release beside Agent Bricks. Security already registers with buyers; LangChain's survey found 24.9% of enterprises with 2,000 or more employees citing it as a barrier to production.

## What to Watch

Five items will decide whether the semantic layer becomes infrastructure or marketing. Snowflake's Cortex Sense claim of 86% accuracy needs a published test set. Glean's 2.5x preference benchmark needs named evaluators and models. Databricks' 1 quadrillion-token figure, if repeated in a filing, would become an audited measure of retrieval volume at an agent platform, and its $190 billion valuation prices that outcome. Cloudflare's metered MCP tools will show whether retrieval sources can charge per call at agent scale. GraphRAG's cost curve, once someone publishes construction cost per million documents against multi-hop recall, will settle the pipeline-versus-graph argument that the vendors currently wage with adjectives.

## By the numbers

- Palantir Q2 2026 revenue: $1.935B, +93% — U.S. commercial revenue $764M, +149%, per the Aug. 3, 2026, release [1]
- Databricks revenue run rate: $7B, >80% YoY — Aug. 13, 2026; $5B raised at a $190B valuation the same day [2]
- Glean annual recurring revenue: $300M — May 28, 2026, up from $100M roughly 15 months earlier [3]
- Cortex Sense agent-accuracy claim (vendor): 24% → 86% — Snowflake claim relayed by PointFive's summit summary; test set and method still to be published [4]
- Glean retrieval vs off-the-shelf MCP tools (vendor benchmark): 2.5× preferred, 30% fewer tokens — Glean's own benchmark, May 28, 2026; evaluators, models and sample size omitted from the release [3]

## Sources

1. Palantir Technologies, "Palantir second-quarter 2026 earnings release (Exhibit 99.1)," U.S. Securities and Exchange Commission, Aug. 3, 2026. https://www.sec.gov/Archives/edgar/data/1321655/000132165526000039/a2026q2ex991pressrelease.htm
2. Databricks, "Databricks grows 80% YoY, surpasses $7B revenue run-rate," Databricks Newsroom, Aug. 13, 2026. https://www.databricks.com/company/newsroom/press-releases/databricks-grows-80-yoy-surpasses-7b-revenue-run-rate-scales
3. Glean, "Glean surpasses $300M ARR," Glean Press, May 28, 2026. https://www.glean.com/press/glean-surpasses-300m-arr-unrivaled-enterprise-context-fuels-ai-adoption
4. PointFive, "Snowflake and Databricks Summits 2026: What Actually Matters," PointFive blog, 2026. https://www.pointfive.co/blog/snowflake-and-databricks-summits-2026-what-actually-matters
5. Paolo Perrone, "The AI Agents Stack (2026 Edition)," O'Reilly Radar, June 8, 2026. https://www.oreilly.com/radar/the-ai-agents-stack-2026-edition/
6. "Google Cloud Next 2026: AI agents and the agentic era," The Next Web, April 22, 2026. https://thenextweb.com/news/google-cloud-next-ai-agents-agentic-era
7. The Linux Foundation, "Linux Foundation Announces the Formation of the Agentic AI Foundation," Linux Foundation Press, Dec. 9, 2025. https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation
8. LangChain, "State of Agent Engineering," LangChain, December 2025. https://www.langchain.com/state-of-agent-engineering
9. Microsoft, "Microsoft Fiscal Year 2026 Fourth Quarter Earnings Conference Call," Microsoft Investor Relations, July 29, 2026. https://www.microsoft.com/en-us/investor/events/fy-2026/earnings-fy-2026-q4
10. Gartner, "Gartner Forecasts Worldwide AI-Optimized IaaS Spending to Grow 96% in 2026," Gartner Newsroom, Aug. 10, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-08-10-gartner-forecasts-worldwide-artificial-intelligence-optimized-iaas-spending-to-grow-96-percent-in-2026
11. Anthropic, "Claude Fable 5.1 and Claude Mythos 5.1," Anthropic, Sept. 1, 2026. https://www.anthropic.com/claude-fable-and-mythos-5-1
12. "Cloudflare Gives AI Agents Wallets That Pay for What They Access," Search Engine Journal, Aug. 12, 2026. https://www.searchenginejournal.com/cloudflare-gives-ai-agents-wallets-that-pay-for-what-they-access/584959/
13. Google Threat Intelligence Group, "Widespread Data Theft Targets Salesforce Instances via Salesloft Drift," Google Cloud Blog, Aug. 26, 2025. https://cloud.google.com/blog/topics/threat-intelligence/data-theft-salesforce-instances-via-salesloft-drift
14. OWASP GenAI Security Project, "OWASP Top 10 for Agentic Applications for 2026," OWASP, Dec. 9, 2025. https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/