DigitalOcean has released Managed Agents, a new cloud service that bundles everything AI developers need to run autonomous agents in one integrated platform. The service combines isolated runtime environments, access to over 16,000 tools, and serverless inference across 75+ AI models, with billing that only charges when agents are actively working.

Why Are Developers Struggling to Build AI Agents Today?

Building production-ready AI agents currently requires developers to stitch together multiple pieces from different vendors: a runtime environment to execute code, a sandbox to isolate untrusted operations, an inference service to connect to AI models, and a tool integration layer to let agents interact with external services like Slack, Jira, or Stripe. This fragmented approach creates security risks, performance bottlenecks, and significant operational overhead.

Agents also behave differently from traditional applications. They run long, unpredictable workflows that pause and resume constantly while waiting for model responses or tool results. They need to persist state across sessions, allow multiple users to collaborate on the same task, and fork into different execution paths when workflows branch. Existing cloud infrastructure, built around virtual machines that take minutes to start, simply isn't designed for this pattern.

What Makes DigitalOcean's Approach Different?

Managed Agents is purpose-built for this workload. The platform includes three core components that work together: a Harness Runtime that provides hardware-isolated execution environments with built-in tools like Chromium and a coding sandbox; an Action Gateway that gives agents governed access to 16,000+ tools from over 500 providers through a single managed endpoint; and integration with DigitalOcean's Inference Engine for serverless access to open-weight models like Nemotron 3 Ultra and proprietary models like Claude Fable 5.1 and GPT 6 Astra.

The platform supports popular agent frameworks without modification, including Claude Code, Codex CLI, OpenCode, and agents built with LangGraph. Teams can also package custom agents as standard container images and reuse them as templates.

How to Get Started Building Agents on DigitalOcean

• Choose Your Harness: Select from pre-supported frameworks like Claude Code or Codex CLI, or bring a custom agent packaged as an Open Container Initiative (OCI) image without requiring modifications to your existing code.
• Connect Your Tools: Access governed connections to 16,000+ tools from 500+ providers through a single MCP (Model Context Protocol) endpoint, with credentials brokered at execution time so they never reach the model or sandbox.
• Deploy and Scale: Launch your agent with automatic pause and resume functionality that preserves state while stopping CPU and memory charges, then scale to thousands of concurrent agents with integrated billing that only charges for active CPU cycles.

What Performance Improvements Does This Deliver?

DigitalOcean claims significant performance advantages over competing offerings. The platform resumes agents from pause in 305 milliseconds, which is 46% faster than other leading solutions. End-to-end performance from microVM creation to agent response is 31% faster, and tool search accuracy is 42% higher than conventional methods.

Pricing reflects how agents actually work. CPU costs $0.044 per vCPU-hour, memory costs $0.0095 per GB-hour, and snapshots cost $0.005 per GiB-month. Because billing only charges for active CPU cycles and not for idle time while agents wait for responses, DigitalOcean reports up to 37% lower total cost of ownership compared to leading independent sandbox providers across real-world use cases.

Who Is Already Using This Platform?

Qencode, a media processing company, built a support-triage agent on Managed Agents that reviews incoming requests across Slack, email, and Intercom, assesses urgency and sentiment, creates or updates Jira tickets, and flags low-confidence cases for human review. The agent saves the team an estimated four to eight hours per week while reducing response times from hours to near-instant.

"It's been a huge force-multiplier for our team. It gets the right ticket to the right person without anyone having to watch every thread themselves," said Murad Mordukhay, CEO and co-founder of Qencode.

Murad Mordukhay, CEO and Co-founder at Qencode

Other early adopters include OpenHands and Amplitude. The service moved from private preview to public availability on September 23, 2026.

What Does This Mean for the Broader AI Infrastructure Market?

DigitalOcean's Chief Product and Technology Officer Vinay Kumar framed Managed Agents as a fundamental shift in how cloud infrastructure serves AI workloads. Just as Amazon's EC2 virtual machines became the foundation of the first generation of cloud computing, agents represent the new primitive that requires purpose-built infrastructure.

"EC2 was the front door to the first generation cloud. Customers rented a virtual machine and assembled everything else around it. The next generation cloud is AI-native, and the agent is the front door. Agents need an integrated platform with inference and tokens to think, computers to execute, and data and applications for context, persistence, and user interaction," explained Vinay Kumar, Chief Product and Technology Officer at DigitalOcean.

Vinay Kumar, Chief Product and Technology Officer at DigitalOcean

The platform includes security features at no additional cost, including single sign-on, multi-factor authentication, role-based access control, audit logs, cloud firewalls, and DDoS protection. Collaborative sessions allow multiple users and agents to work together in shared environments, a capability DigitalOcean notes is unique among major cloud providers.

By bundling runtime, tools, inference, and billing into one integrated service, DigitalOcean is betting that developers will choose integration and simplicity over assembling best-of-breed components from multiple vendors. The early customer results suggest this approach resonates with teams building production agents that need to scale beyond a handful of experimental deployments.