Garbage in, Agentic out: why data and document quality is critical to autonomous AI’s success (www.techradar.com)

Agentic AI — autonomous systems that plan and act without human intervention — is drawing heavy investment and early wins (PwC: 88% of teams plan bigger AI budgets; 79% already adopting agents; 66% of adopters report measurable productivity gains), and is being piloted in high‑impact settings such as Nvidia’s pre-operative patient assistant for The Ottawa Hospital. But analysts warn of real risk: Gartner expects over 40% of agentic projects to be canceled by 2027 because of rising costs, unclear ROI, or weak controls. The difference between success and costly failure often comes down to input quality: unlike generative tools, agents make autonomous decisions that directly affect KPIs and compliance, so “garbage in, agentic out” can lead to dangerous outcomes (e.g., misapproved loans, clinical misinformation). Technically, agents need clean, structured, digitized documents and validated data feeds. Poor OCR from low‑resolution scans or uncleaned corpora skews LLM reasoning; advanced scanners (300 DPI, adaptive thresholding, de‑skewing) and rigorous preprocessing improve accuracy. Best practices include deduplication and cleanup, semantic classification and metadata tagging, strict data minimization and confidentiality controls, and small‑scale testing with feedback loops to catch hallucinations and formatting errors before scaling. With proper data governance, agentic AI can outstrip generative models in business impact—but high‑quality data and document pipelines are a prerequisite for trustworthy, reliable autonomous systems.