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Knowing When Not to Use AI: AI Agents vs Rules vs ML
Focus: Agents for Everyone
Date: July 23, 2026
Duration: 10:35
Topics:
Evidence: Practitioner Analysis
Availability: Not Applicable
Summary: IBM's practical decision framework compares fixed rules, classical machine learning and generative agents against task ambiguity, predictability and verification risk. For repeatable eligibility checks or fixed routing, a deterministic rule can be cheaper and safer than an agent; established predictive ML suits numeric classification with suitable data; agent tools help interpret variable multi-step requests, but need permissions, validation and human review. The discussion is presented as a reusable selection framework, not as evidence of actual financial savings or autonomous reliability.
Why it matters: Use the shortest dependable method for each step and reserve autonomous tools for uncertain work with approval and audit gates.
Source: IBM Technology official video