Multimodal workflow memory for AI agents.
Problem: AI agents are powerful, but they are blind to how work actually happens. They see prompts, files, and final outputs — not the real sequence of human decisions across windows. Insight: The most valuable context for agents is not just documents. It is the timeline of human actions: what was seen, clicked, corrected, retried, and completed. Solution: Gofer Agent Harness records multimodal workflows and converts them into structured, searchable agent memory. Demo: We record a workflow, segment it into task steps, run multimodal understanding on AMD GPUs, and let an agent retrieve the workflow to answer questions or generate an SOP. Business value: Every company has repetitive workflows trapped in screen recordings, calls, support sessions, and internal demos. Gofer turns that into reusable automation context. Future: This becomes the data layer for robotics and embodied AI: human demonstrations become reusable context for agents and robots.
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