Beyond the SOP: Agentic Operating Procedures | Ai4 2026 Video
Beyond the SOP: Why Your AI Agents Need an Agentic Operating Procedure | Ai4 2026
Many agentic AI vendors promise a digital twin to train your AI agents. What they often deliver looks more like a bank statement: every transaction logged, but none of the story behind it. At Ai4 2026 in Las Vegas, Vinay Mummigatti, Chief Customer Officer at Skan AI, and Dayton Semerjian, former Chief Customer Care Officer at LPL Financial, explained why that gap matters, and how Agentic Operating Procedures (AOPs) give agents the full picture.
Key insights
1. Humans fill gaps that most agents can’t see
Experienced employees naturally handle the exceptions and judgment calls that SOPs never cover. An under-trained agent can't handle these process variants and instead scales errors faster than any human could.
2. Event logs miss most of the work
Digital twins built from system logs capture only a fraction of what happens at the desktop. The tribal knowledge, exception paths and workarounds that drive real outcomes stay hidden.
3. Shifting from static SOPs to living AOPs
An agentic operating procedure grounds agent execution in the exact exception paths and human decisions needed to do the work, giving agents the context to execute safely from day one.
4. Building AI governance that satisfies risk committees
Agent execution is continuously monitored against compliance policies and KPIs, with auditable decision logs that give regulators and risk teams confidence.
See how agents execute with workflows grounded in real work
Request a demo to see how AOPs turn observed operations into predictable automation ROI.
Frequently asked questions
An AOP is a dynamic procedure built from real-time observation of how work happens. Unlike a static SOP, it learns from continuous capture of exception paths and human workarounds, giving AI agents the complete context they need to execute safely.
SOPs describe how work is supposed to happen and are rarely updated. AOPs reflect how work actually happens, are built from observed data and evolve as the business changes.
Most are built from system event logs, which capture only a small share of real desktop activity. Without the full operational context, agents trained on them make costly mistakes at scale.
Skan AI deploys a lightweight, privacy-first desktop agent to capture and map work activity across in-process applications, including legacy systems, with no complex integrations required.
