About
Jonathan Malkin is an enterprise tech leader and founder of Built with Jon in Austin, Texas. He builds AI-powered workflows, internal tools, and operating systems for teams that need practical leverage, not hype.
His background spans presales, solutions engineering, business analysis, project management, operations, and technical delivery. The common thread has been translating between business and technical audiences, understanding what people actually need, and turning messy requirements into systems people can run.
Seven of those years were inside the automation wave at Automation Anywhere. Jonathan watched RPA move from one-off departmental wins to broader programs with governance, queues, stakeholder alignment, operating models, and eventually federated delivery. That experience is the backbone of how he thinks about AI agents now: the tools are newer, but the operational questions are very familiar.
The work has crossed functions and borders: finance, HR, IT, operations, sales, and marketing; industries including banking, airlines, and telecommunications; and teams in the United States, Germany, and Brazil. Jonathan lived in Sao Paulo for five months in 2012, which gave him a practical feel for working across cultures, time zones, and operating norms instead of treating international work as a slide-deck abstraction.
What Jonathan Brings
Jonathan's useful edge is the mix. He can sit with operators and map the real workflow, talk with leaders about risk, scope, ownership, and adoption, and work with technical teams on architecture, prompts, data flow, automation scripts, and the build path. Most AI work fails somewhere between those worlds; his work tends to live in the translation layer.
- Enterprise automation pattern recognition. He has seen what happens when automation spreads across Finance, HR, IT, Operations, Sales, and Marketing without enough governance, and what happens when governance turns into a bottleneck.
- Business-to-technical translation. Years in presales, solutions engineering, analysis, and delivery shaped a practical way to turn vague operational pain into scoped, explainable implementation work.
- Hands-on builder judgment. He runs his own AI operating system across research, content, coding, scheduling, review, and memory. The consulting point of view comes from systems he actually uses.
- Calm implementation bias. The preference is clear maps, small useful builds, reviewable artifacts, and operating docs that survive after the exciting demo is over.
Public Builder Work
Built with Jon is the public edge of that work. Jonathan writes and builds around AI agents, Claude Code, automation systems, and the difference between demos and systems people actually use. The current focus is simple: AI agents and assistants that become operating leverage, not throwaway proofs of concept.
In April 2026, Jonathan gave an AI Tinkerers demo about Jules, his AI collaborator system. That setting matters because AI Tinkerers is a builder-first room: live systems, implementation details, and technical scrutiny over polished positioning.
Why Jonathan Works This Way
Jonathan has been around enough enterprise software to know that the demo is the easy part. The harder parts are choosing the right use case, making ownership explicit, dealing with exceptions, keeping humans in the review path, and making sure the thing still works when the original builder is not in the room.
That is why the service model is Discovery -> Advisory -> Build. First understand the work. Then decide what should be automated, governed, delegated, or left human. Then build the smallest useful system that proves the pattern.
Current Work
Today Jonathan works through Built with Jon on AI automation services, advisory work, and operating-system projects like Jules and Knowledge OS. The public writing is part of the same practice: make the work visible, explain the tradeoffs, and keep improving the system in public where useful.