DigitalOcean, the AI-Native Cloud, today announced Agent Learning and Data Services as another key element of its modern agentic stack, so teams can launch self-improving agents. Together with Inference Engine and Managed Agents, DigitalOcean is one of the few clouds where agents can run, learn, and improve on one platform.
Quick Intel
DigitalOcean has announced Agent Learning and Data Services for recursively self-improving AI agents.
Agent Learning Services includes Insights, Signals, and Evaluations & Simulations.
Agent Data Services gives agents one managed place to read context and write back what they learn.
Agent Learning Services are available today in public preview.
Inference Router optimizer and Agent Data Services are available in private preview.
DigitalOcean will demonstrate the services live at the Open Intelligence Summit on Oct. 13 in San Francisco.
DigitalOcean Agent Learning Services: three products that work as one loop. Insights records what an agent does, Signals flags where it falls short, Evaluations & Simulations tests a change before customers see it. The evidence they produce is what teams use to optimize an agent.
DigitalOcean Agent Data Services: feeds the loop, giving agents one managed place to read the context they need and write back what they learn.
“The question has changed from ‘can we launch an agent’ to ‘does our agent get better with every run,” said Vinay Kumar, Chief Product and Technology Officer, DigitalOcean. “We built Inference Engine so agents could think, Managed Agents so they could act, and now Agent Learning Services and Agent Data Services so they can improve, on one open cloud and on the customer’s own data. That is what owning your intelligence means, and it is why DigitalOcean is the place to launch and scale self-improving agents.”
Once an agent is deployed, it plateaus. Making it better is hard because every piece of the job lives somewhere else. The record of what the agent did sits in a monitoring tool that shows whether systems are up, not why the agent called the wrong tool or lost a user. A fix is usually a prompt tweak with no way to test it, so the first to notice a regression is a customer. Improving the model means exporting production data to a training vendor and wiring together four or five tools, so it happens once a quarter or not at all.
Agent Learning Services give every team that loop in one place. Insights records every step an agent takes, and Signals flags the sessions that matter: where the agent stalled, looped, lost the user, or failed. That evidence becomes a proposed change. Evaluations and Simulations test changes ahead of real users with realistic conversations built from flagged sessions. Each change feeds the next stage of the loop. Today, this recursive self-improvement is available for models: the newly enhanced Inference Router picks the best-scoring model for each task, optimized for cost, quality, or the team’s own evaluations, and re-runs those evaluations as new models ship. Next, DigitalOcean plans to bring the same automated loop to agent workflows, augmenting the current human approvals.
Agent Data Services is the data layer the loop runs on, and the one agents work from. Every piece of a company’s data becomes agent-ready the moment it arrives. Video is transcribed and described, documents are read and indexed, logs and agent sessions are assembled with configurable redaction controls for sensitive details, and all of it is searchable by meaning in a single call. It lands in one catalog, designed to be logically isolated to that customer, with governance designed so that only authorized people and agents can reach each piece of data. Agent Learning Services are built on it: Signals writes sessions into it, and Optimizer draws its training and test data from it only where the customer expressly opts in. The same platform serves far more than the learning loop. Teams can use it for search and analytics across video, audio, documents, and structured data, and for grounding any agent in the company’s own records.
“Amplitude helps organizations deliver more impact when building with AI,” said Spenser Skates, Co-founder and CEO of Amplitude. “Amplitude Wave is an agentic product team for building self-improving products. It analyzes product data, customer feedback, and code to recommend changes, ship experiments, and measure the real-world results. With DigitalOcean, the models and the agent infrastructure are on one platform. We choose the model for each task based on the results it delivers, not just its cost. DigitalOcean Managed Agents powers Wave and handles the agent infrastructure as we scale, so our engineers can focus on helping customers build better products and get more value out of their AI spend.”
About DigitalOcean
DigitalOcean is the AI-Native Cloud, purpose-built for the inference and agentic era. One integrated platform spans GPU and CPU infrastructure, core cloud, inference, data, learning, and managed agents. It is open throughout so builders can start fast, scale production AI workloads, and improve unit economics. More than 680,000 customers and millions of developers globally trust DigitalOcean to build, ship, and scale their applications.
