Building Agentic AI solutions in enterprise environments✦Training for IronMan 2027✦Building Aether Ops · AI-era governance for small, mid & large enterprise✦Ultra Marathon 2028 · because ERP wasn't hard enough✦Leading system integration post-$13B IPG acquisition at Omnicom✦Finance nerd by day. AI builder by night. Endurance athlete on weekends.✦8+ years · $17B+ in transactions · 150+ global business units✦Currently shipping: Aether Ops · Onyx · and things I can't talk about yet✦Stevens Institute · MS Business Intelligence & Data Analytics✦Ask my agent anything 👇✦Building Agentic AI solutions in enterprise environments✦Training for IronMan 2027✦Building Aether Ops · AI-era governance for small, mid & large enterprise✦Ultra Marathon 2028 · because ERP wasn't hard enough✦Leading system integration post-$13B IPG acquisition at Omnicom✦Finance nerd by day. AI builder by night. Endurance athlete on weekends.✦8+ years · $17B+ in transactions · 150+ global business units✦Currently shipping: Aether Ops · Onyx · and things I can't talk about yet✦Stevens Institute · MS Business Intelligence & Data Analytics✦Ask my agent anything 👇✦
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Govind Waghmare

Agentic AI for ERP. Cutting through red tape. Building in public from NYC.

govindwaghmare@icloud.comcal.com/govindw

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© 2026 · New York City·RSS·llms.txt

The Loop Is the Product

Jun 24, 2026

The thing that changed in coding this year is not that the models got smarter, though they did. It's that the agents stopped waiting for you.

Through 2024 and most of 2025, an AI coding tool answered a prompt and stopped. You were the loop. You read the diff, ran the tests, found the bug, and prompted again. In 2026 the loop moved inside the agent. It plans, edits, runs the tests, reads the failure, fixes it, and repeats, for minutes or hours, in a background execution environment built for exactly that. Cognition's Devin, OpenAI's Codex, and Claude Code all run this way now. The human moves from operator to reviewer.


Why this maps to enterprise work

I build this way already, and it changed what I attempt. When the cost of one iteration drops near zero and the agent can run a hundred of them unattended, you stop scoping work to what you can babysit and start scoping it to what you can verify.

That distinction is the whole game in enterprise finance. The bottleneck was never writing the integration between two systems. It was the back-and-forth of testing it against a messy data model, finding the edge case in a country-specific tax rule, fixing it, and running it again. A long-running agent eats that loop. What's left for the human is the part that actually requires judgment: deciding whether the result is correct and whether it's allowed.


The catch nobody puts on the slide

A loop that runs unattended is only as safe as the box it runs in. If the agent has write access to a real system, the loop is now executing changes you didn't read line by line. That's fine in a sandbox with tests as the guardrail. It is not fine pointed at a general ledger.

So the same lesson keeps showing up from every direction this year. The autonomy is real and the leverage is enormous, but it's earned by the environment around the agent, not granted by the agent itself. Give the loop a sandbox, strong tests, and a human at the sign-off, and it's the best leverage I've ever had. Give it production credentials and no guardrails, and you've automated the thing that ends careers.

The loop is the product. The boundary around it is the discipline.

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