Published by Vinay Kumar, Chief Product and Technology Officer at DigitalOcean
Every era of the cloud has a unit of work. For agents, it’s a human who directs, a controller who runs the operation, and a worker who does the heavy lifting. Last week we watched all three move a production backend, from a phone.
I’ve spent fifteen years building cloud services: S3 and EBS in the early days of AWS, Oracle Cloud Infrastructure from inception, and now the agentic cloud at DigitalOcean. In every era, moving a backend meant the same thing: a project plan, a maintenance window, and a team working late into the night.
Last week, Rohit Bhattacharjee on our team moved an entire production backend from his phone. Database, APIs, file storage, and about 31,000 AI search embeddings. He never opened a laptop. A message while waiting for coffee. A decision between meetings. By the end of the week, it was done.
This wasn’t a demo. It was a real workload with a bill.
Three parties, three jobs
The worker was a DigitalOcean Managed Agent, an AI that does real work in the cloud, not just chat. It ran in our Harness Runtime: a private, isolated computer that spins up for each session, with a workspace that persists when the agent pauses. Through our Action Gateway, a single connection to 20,000+ tools, it could use GitHub, Supabase, a web browser, and the whole DigitalOcean API.
The agent never saw a password. The gateway applies credentials at the moment of use, so they never reach the model.
It provisioned the database, loaded all 40 access policies, and mapped every Supabase feature to its DigitalOcean counterpart. It wasn’t advising on a migration. It was performing one.
The controller was a personal agent running on his phone, the agent Rohit actually talked to. He ran the migration in plain chat, and the personal agent turned his messages into work: tasking the worker, tracking the run, and reporting back. When he asked it to check a claim, it did. The worker said 145,432 rows had been loaded, so the controller counted them. Validation was the side job. The main job was holding the whole operation together in one conversation, so a person never had to leave the flow of his day.
The worker and controller come from different model families. That’s the point of an open platform: you choose the right model for each job.
Rohit provided the credentials, his judgment, and a credit card. He reviewed a $180 proposed plan and saved almost 80% on a revision with just a few taps. Everything else, the two agents handled between themselves.
What moved
The workload was huffl.ai, an agentic canvas that puts docs, spreadsheets, whiteboard, presentations, and search in one place. Underneath: 31 tables, 145,432 rows, three storage buckets, 24 functions, and 30,650 embeddings, the numeric fingerprints that make search work.
Before moving anything, the worker read all of it and caught two problems that would have poisoned the migration. Two overloaded functions that looked like duplicates. A “vector count” that was really measuring string lengths.
The new home: managed Postgres ($15), one App Platform app running four services ($20), and one object store ($5). About $40 a month. The data moved with per-table parity checks, and every one of the 30,650 embeddings verified non-null.
Pay for the work, not the wait
An agent spends most of its session waiting: on a model, a tool, or a person. The runtime paused the worker whenever it waited and resumed it in about 300 milliseconds. While it slept, the meter stopped. We bill for active CPU, not provisioned capacity. The agent runtime for the entire run cost a few dollars.
What this changes
Moving a backend used to mean a team, a plan, a maintenance window, and a war room. Last week it meant chat bubbles. The human says what needs to happen, the controller runs the operation, the worker does the work, and every claim gets checked along the way.
The work didn’t get smaller. What one person can accomplish got bigger. See it in action: watch the video:
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What would you trust an agent to move for you, and what would you want checking its work?
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