https://www.reasontochain.com/foundations daily 0.75 2026-10-08 https://www.reasontochain.com/foundations/the-anatomy-of-agency monthly 0.5 2026-10-07 https://www.reasontochain.com/foundations/what-agentic-ai-actually-means-in-late-2026 monthly 0.5 2026-10-08 https://images.squarespace-cdn.com/content/v1/69c3b3ad3cbece302da0c0c6/f64b7490-83cc-4392-9b5d-31cb3e019273/Article_What+Agentic+AI+Actually+Means+in+Late+2026_In+Post.png Foundations of agency - What Agentic AI Means in Late 2026 - This hierarchy avoids one of the most common mistakes in the agentic-AI debate: treating more agency and autonomy as synonymous with more value. That is not the case . A procurement agent that can autonomously place an order is more autonomous than one that only recommends a supplier. Whether that additional autonomy creates value depends on the quality of the decision, the risk of error, the reversibility of the action, the cost of supervision and the economics of the process. https://www.reasontochain.com/evidence daily 0.75 2026-10-08 https://www.reasontochain.com/evidence/can-ai-agents-run-a-supply-chain-what-the-evals-tell-us-and-what-they-dont-844gg-26mp6 monthly 0.5 2026-10-08 https://www.reasontochain.com/evidence/can-ai-agents-run-a-supply-chain-what-the-evals-tell-us-and-what-they-dont-844gg monthly 0.5 2026-10-08 https://www.reasontochain.com/evidence/can-ai-agents-run-a-supply-chain-what-the-evals-tell-us-and-what-they-dont monthly 0.5 2026-10-08 https://images.squarespace-cdn.com/content/v1/69c3b3ad3cbece302da0c0c6/8668ee05-efb6-40d3-88a5-2ad96576e244/Benchmark+Coverage+Overview+Across+the+REASONTOCHAIN+Working+Model.png Evidence - Can AI Agents Run a Supply Chain? What the Benchmarks Tell Us and What They Don't - Make it stand out This table summarizes the selected benchmarks and evidence cases examined in detail in sections 1 and 2. Direct = explicitly exercised and evaluated (note: Direct may still be bounded to a sandbox or simulation and should not be interpreted as live enterprise); Partial = meaningfully represented but not comprehensively evaluated; Limited = present mainly as an environmental constraint or secondary consideration; Not evaluated = outside the evaluation’s meaningful scope. † OpenAI–Hugging Face Incident and the MBA thesis are deliberately included as special evidence cases, not presented as formal public benchmarks. A caution when comparing benchmarks: The evaluations reviewed here are a selected, not exhaustive, view of the state of the art in late 2026. They do not have equal evidentiary status or directly comparable scoring systems. Some are peer-reviewed or conference-published, others are preprints, and some are vendor-developed evaluations. Several are continuously updated. Their scores should therefore not be treated as a common leaderboard. The durable value lies in the capability, operating condition or failure mode each evaluation exposes. 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